Effects of software security vulnerabilities on the software development industry
All posts by Jean-Loup Richet
Cybercrime Trends: an Exploration of Ad-Fraudsters Communities
Cybercrime Trends: an Exploration of Ad-Fraudsters Communities
Jean-Loup Richet, IAE de Paris (Sorbonne Business School)
Abstract/ highlights of the paper
- • This is one of the first studies documenting the way ad-fraud communities innovate and create value for their criminal customers.
- • A multimethod approach was applied for data collection, integrating qualitative and quantitative assessment of six cybercriminal communities.
- • Specialized ad-fraud communities provided a wealth of knowledge and incremental innovations in ad-frauds.
- • General and customer-oriented ad-fraud communities showcased the most internal interactions, as well as exhibiting better performance and growth.
- • General and customer-oriented ad-fraud communities have developed specific capabilities, focusing on innovation through artificial intelligence, which fuels customer engagement and fosters (criminal) attractiveness.
Reference
Richet, J.-L. 2022. “How Cybercriminal Communities Grow and Change: An Investigation of Ad-Fraud Communities,” Technological Forecasting and Social Change (174), p. 121282. (https://doi.org/10.1016/j.techfore.2021.121282)
Developing a cybersecurity culture to influence employee behavior
Developing a cybersecurity culture to influence employee behavior
Jean-Loup Richet, IAE de Paris (Sorbonne Business School)
In our increasingly connected world, cybersecurity has become a critical concern for individuals, businesses, and governments alike. With the ever-growing threat of cyberattacks, it is more important than ever to raise awareness of cybersecurity risks and best practices. By promoting cybersecurity awareness, we can help protect ourselves and our data from malicious actors (Richet, 2021). Cybersecurity awareness helps to educate individuals about the dangers of cybercrime and the importance of taking steps to protect themselves online… But also to comply with organizational rules and deter them from deviant behaviors!
When it comes to deterring employee deviant behavior in information security, sanctions are one of the most commonly used methods. Organizations have long used sanctions as a way to deter employees from committing fraud. Sanctions can range from financial penalties to termination of employment.
However, research on this topic has been mixed, with some studies showing that sanctions are effective and others indicating that they are not. Trang & Brendel (2019) take a closer look at the role of sanctions in deterring employee deviant behavior and explore how contextual and methodological moderators can impact this deterrence approach. Their findings suggest that while sanctions have an overall effect on deviant behavior, their effectiveness depends on the context in which they are implemented and the methodology used to study them. In particular, they find that deterrence theory is more likely to predict deviant behavior in malicious contexts, cultures with a high degree of power distance, and cultures with high uncertainty avoidance. By understanding the moderating effect of these contextual and methodological factors, organizations can better design sanction mechanisms that are tailored to their specific needs and objectives.
There is a growing body of evidence that suggests organizations with strong cybersecurity cultures are better equipped to manage cyber risks, to protect their data and systems, but also to manage employee deviant behaviors. Practitioner research (IBM, 2021) found that organizations with a security-conscious culture are three times more likely to have comprehensive security programs in place and four times less likely to experience a data breach originating from an insider.
While the benefits of a strong cybersecurity culture are clear, developing such a culture is no easy task. Alshaikh (2020) identify and explain five key initiatives that three Australian organizations have implemented to improve their respective cyber security cultures. The five key initiatives are: identifying key cyber security behaviors, establishing a ‘cyber security champion’ network, developing a brand for the cyber team, building a cyber security hub, and aligning security awareness activities with internal and external campaigns. These key initiatives have helped organizations exceed minimal standards-compliance to create functional cyber security cultures. Organizations looking to improve their cybersecurity culture should consider implementing some or all of these five key initiatives. By doing so, they will be better positioned to manage cyber risks and protect their data and systems. It will also help them to create a culture of security within organizations, making it more likely that employees will report suspicious activity, take precautions to prevent attacks, and comply with information security policy. In addition, raising awareness of cybersecurity issues can help to better inform policymakers as they work to enact laws and regulations to promote cybersecurity and protect our interconnected world.
References:
Alshaikh, M. (2020). Developing cybersecurity culture to influence employee behavior: A practice perspective. Computers & Security, 98, 102003.
IBM. (2021). Cyber Resilient Organization Study 2021. Retrieved from: https://www.ibm.com/resources/guides/cyber-resilient-organization-study/
Richet, J.L. (2021). Trends in Cybercrime: Cases the Banking Sector. BPI France, Jun 2021, Paris, France. 2021.
Trang, S., & Brendel, B. (2019). A meta-analysis of deterrence theory in information security policy compliance research. Information Systems Frontiers, 21(6), 1265-1284.
Developing a cybersecurity culture to influence employee behavior
Cybercrime Research Library
Harvard Digital Collections
Reference list
Analyzing the Effect of IT Decision-Making on Cybersecurity Breaches in Higher Education
Analyzing the Effect of IT Decision-Making on Cybersecurity Breaches in Higher Education
Lawrence J. Awuah, PhD
Abstract: The recent and current data breaches and cyberattacks continue to spike at an alarming rate, which in most cases can be consequential if proactive measures are not taken. Unfortunately, taking a closer look at most of those breaches and/or cyberattacks indicates that risk-based and event-based decision-making could have intended or unintended impacts on potential threats and the level of mitigated effort implemented. In this view, the lack of centralized IT governance, particularly in Higher Ed institutions, over the years, has witnessed frequent breaches associated with rising security incidents. It has therefore become critically important that IT and cybersecurity executives do well to balance IT uptime with data protection requirements while adhering to security policy enforcement.
Liu, C. W., Huang, P., & Lucas Jr, H. C. (2020). Centralized IT decision making and cybersecurity breaches: Evidence from US higher education institutions. Journal of Management Information Systems, 37(3), 758-787.
Summary: “Despite the consensus that information security should become an important consideration in information technology (IT) governance rather than the sole responsibility of the IT department, important IT governance decisions are often made on the basis of fulfilling business needs with a minimal amount of attention paid to their implications for information security. We study how an important IT governance mechanism—the degree of centralized decision making—affects the likelihood of cybersecurity breaches. Examining a sample of 504 U.S. higher-education institutions over a four-year period, we find that a university with centralized IT governance is associated with fewer breaches. Interestingly, the effect of centralized IT governance is contingent on the heterogeneity of a university’s computing environment: Universities with more heterogeneous IT infrastructure benefit more from centralized IT decision making. In addition, we find the relationship between centralized governance and cybersecurity breaches is most pronounced in public universities and those with more intensive research activities. Collectively, these findings highlight the tradeoff between granting autonomy and flexibility in the use of information systems and enforcing standardized, organization-wide security protocols.”
Keywords: Risk management, risk assessment, IT security; IT governance; cybersecurity breach; centralized decision making; cybersecurity analytics; security operations
Recent high-profile security breaches, notably those involving much-publicized and large-scale breaches and ransomware attacks on Colonial Pipeline, Facebook data breach, Kaseya Ransomware attack, and Sony Pictures have attracted scrutiny as to how the seemingly flawed decisions of employees or IT leadership can have major cybersecurity implications. Additionally, the recent and current data breaches and cyberattacks continue to spike at an alarming rate with associated consequential impacts. A closer examination of most of those breaches indicates that risk-based and event-based decision-making could have intended or unintended impacts on potential threats and the level of mitigated effort implemented. With cyberattacks becoming more widespread and more sophisticated than ever before, due care and due diligence should consistently be the focal point of IT executives. By the third quarter of 2022, [8] indicated a total of 112 publicly disclosed security incidents were identified, resulting in over 97 million compromised records. This finding represents an increase of approximately 11% in security incidents compared to the previous year. In their study, Liu et al. [1] found that academic institutions with centralized IT governance record fewer security breaches. This claim was in part attributed to the fact that those institutions with distributed IT infrastructure benefit more from centralized IT decision-making than those who do not. This assertion suggests that lack of centralized IT governance, whether in the corporate establishments or in Higher Ed institutions, can lead to frequent breaches associated as a result of rising security incidents. As well, several studies have examined financial loss, legal implications, and moral obligations involving data breaches and their impact on organizations, data owners, and victims [1]-[6], [8]. It is therefore incumbent on IT and cybersecurity leadership to do more to balance IT functionality and uptime with data protection needs while instituting security policy enforcement. This practice can make cybersecurity a business enabler to minimize risks while maximizing revenue for continued business growth.
On the other side of the spectrum, IT governance and decision-making are contingent on human factors. Human error has been known to be the main cause of most cyber security breaches; indeed, humans are the weakest link in the security chain [12]. For this reason, cybersecurity leadership cannot ignore security awareness training programs. The executives should be mindful of the fact that humans form a significant factor contributing to data breaches. This awareness can augment the centralized IT decision-making in confronting cybersecurity breaches in Higher Ed institutions in particular and the industry in general. According to [11], security awareness training programs are educational in nature that equip employees with tools to identify, mitigate, and report such attacks crafted by social engineering techniques. One of the biggest risks to an organization’s IT security is often not a weakness in the technology control environment per se; rather it is the action or inaction by employees and other personnel that can lead to security incidents. For example, employee noncompliance related to IT security policies continues to raise eyebrows for most organizations today. In other words, considering the variety of IS security policy compliance strategies in place, security awareness training [9], [10]-[11] forms a crucial part of the war on cyber threats. Evidently, despite widespread awareness of risks, significant investments in cybersecurity protection, and substantial economic incentives to avoid security breaches, organizations remain vulnerable to phishing attacks [2].
Furthermore, several studies [4]-[6] suggest that while cybersecurity is usually treated as a technology problem, most data breaches are the result of human error. By identifying the social behavior indicators, along with the rationales behind the decision-making process, the development of cybersecurity architecture can be improved. This aligns with the assertion by Liu et al. [1] that that adopting a centralized IT unit with a better understanding of the overall IT architecture can better equip the executives in managing risks even in a sophisticated IT environment. This is particularly important to the average cybersecurity team who could possibly make reactive decisions in addressing reported breaches. In any case, the human factor needs to be an integral part of every IT implementation when reducing and protecting against information security risks accompanying the development, architecture, and maintenance of an IT system [5]. In other words, discussing IT security problems must factor in policies, behavior, and user compliance requirements [6].
Over the past few years, [1] noted that the management of information security has gained significant research interests in the research community, as well as expert interests in the field. Typically, risk-based decision-making is reflective of strategic investments by virtue of the desire for detection, prevention, and response plans. These three parameters need to be balanced for optimum gains. Additionally, the importance of good management practices in protecting organizational assets and enforcement policies in checking employee security behaviors in organizations has also been recognized [5]-[6], [9]-[10]. One typical example is law enforcement, which can play a key role in this effort. Hui et al. estimated the impact of enforcing the Convention on Cybercrime (COC) on the desire to deter and reduce distributed denial of service (DDOS) attacks, for example [7]. The authors noted that directly observing attacker behavior can impact deterrence to complement law enforcement and leadership actions. Overall, making well-informed decisions regarding the value and benefits of secure IT implementations in the organization is great if cybersecurity is considered a business enabler. For instance, proactive investment strategies should be adopted to help minimize risks to the organization and maximize return on investment (ROI) from the perspective of understanding cybersecurity as a business enabler.
Moreover, there are other factors that make the role of IT and information security leadership an important ingredient in ensuring a substantial security posture. In some literature, there have been constant calls for IT executives to improve security operations capabilities with the aim of identifying and confronting cyberattacks using applicable incident response techniques as presented by [3]. For example, by automating security controls and policies, the security operations teams can operationalize cyber response best practices with the right guidance. In another study, strict security policies surrounding Bring Your Own Device (BYOD) computing environment in organizations were studied. Thus, complying with BYOD security policies is necessary within organizations to address the factors that lead to the desired security behavior [4]. As mentioned earlier, [1], [5] examined the implications of IT decision-making on the effect of information security management on the protection of assets and critical data. In their justification, the authors developed and tested hypotheses considering how centralized and strategic IT decision-making affect the value of information security over a certain period.
Key Takeaways
IT/Cybersecurity executives in academic institutions must consider doing the following:
- Endeavor to put safeguards in place including security controls, policies, security awareness programs, disaster recovery plans (DRP)/ business continuity plans (BCP) and others.
- Focus on embracing strategic goals in line with cybersecurity as a business enabler in terms of risk reduction, cost-effectiveness, and resource optimization targeted at high ROI.
- Understand the threat landscape, assess cybersecurity maturity, improve cybersecurity program, and document short- and long-term cybersecurity strategy.
- Balanced prevention techniques, response strategies, and detection capabilities with actionable intelligence.
In a nutshell, the theoretical development and empirical analyses yielded two important findings about the adoption of centralized IT governance in the enterprise. The main goal is that this practice tends to minimize cybersecurity breaches, especially when an academic institution has a heterogeneous IT environment in place. In these days of escalating attempts to breach information systems everywhere at any time, it is imperative that senior executives—including CISO, CIO, CFO, CRO, and CEO—consider the impact of IT governance decisions on their cybersecurity maturity and the value it brings to the organization. Therefore, the quest for reinventing cybersecurity solutions must be a continuous focus to bolsters cybersecurity infrastructure with appreciable visibility and the need to gravitate toward broader security strategies for added benefits to the organization.
Full article: Centralized IT Decision Making and Cybersecurity Breaches (Harvard)
References:
[1] Liu, C., Huang, P., & Lucas, H., C. (2020). Centralized Information Technology Decision Making and Cybersecurity Breaches: Evidence from U.S. Higher Education Institutions. Journal Of Management Information Systems, 37(3), 758–787. https://doi.org/10.1080/07421222.2020.1790190
[2] Wright, R., Johnson, S. L., & Kitchens, B. (2022). Phishing Susceptibility in Context: A Multi-level Information Processing Perspective on Deception Detection. Wright, RT, Johnson, SL, Kitchens, B.” Phishing Susceptibility in Context: A Multi-level Information Processing Perspective on Deception Detection” MIS Quarterly.
[3] Kinyua, J. & Awuah, L. (2021). AI/ML in Security Orchestration, Automation and Response: Future Research Directions. Intelligent Automation & Soft Computing, 28(2), 527–545. DOI:10.32604/iasc.2021.016240
[4] Palanisamy, R., Norman, A. A., & Kiah, M. L. M. (2020). Compliance with Bring Your Own Device security policies in organizations: A systematic literature review. Computers & Security, 98, 101998.
[5] Bhaharin, S., H., Sulaiman, R., Mokhtar, U., A., & Yusof, M., M., (2019). Issues and Trends in Information Security Policy Compliance. 2019 6th International Conference on Research and Innovation in Information Systems (ICRIIS). DOI: 10.1109/ICRIIS48246.2019.9073645
[6] Angraini, A., & Okfalisa, R. Y. (2019). Information security policy compliance: Systematic literature review. Procedia Computer Science, 161, 1216-1224.
[7] Hui, K. L., Kim, S. H., & Wang, Q. H. (2017). Cybercrime deterrence and international legislation: Evidence from distributed denial of service attacks. Mis Quarterly, 41(2), 497.
[8] Irwin, L. (2022, September 1). List of Data Breaches and Cyber Attacks in August 2022–97 Million Records Breached. IT Governance. https://www.itgovernance.co.uk/blog/list-of-data-breaches-and-cyber-attacks-in-august-2022-97-million-records-breached
[9] Puhakainen, P., & Siponen, M. (2010). Improving Employees’ Compliance Through Information Systems Security Training: An Action Research Study. MIS Quarterly 34(4), 757-778.
[10] Richet, J. L. (2012). How to Become a Black Hat Hacker? An Exploratory Study of Barriers to Entry Into Cybercrime. In AIM.
[11] Solomon, A., Michaelshvili, M., Bitton, R., Shapira, B., Rokach, L., Puzis, R., & Shabtai, A. (2022). Contextual security awareness: A context-based approach for assessing the security awareness of users. Knowledge-Based Systems, 246, 108709.
[12] Richet, J. L. (2022). How cybercriminal communities grow and change: An investigation of ad-fraud communities. Technological Forecasting and Social Change, 174, 121282.
Employing Detection Techniques to Confront the Rapid Spread of Fake News
Employing Detection Techniques to Confront the Rapid Spread of Fake News
Lawrence J. Awuah, PhD
Abstract: Today, the use of social networks such as Facebook, Twitter, and Instagram has become a key part of continuous human engagement in the sense that these platforms are available for users to share personal messages, pictures, videos, and other forms of multimedia. However, these changing trends have become catalysts for creating misleading activities including misinformation, disinformation, and fake news. For example, the spread of false news on social media has adversely impacted mainstream news media, politics, public trust, and healthcare needs. Therefore, the desire to confront the spread of misinformation, disinformation, and false or misleading news remains a challenge for these social media platforms, policymakers, and law enforcement agencies. Several solutions have been suggested by the research community such as the application of machine intelligence, crowd technologies, and social media ranking algorithms with the aim of addressing this ever-evolving infodemic menace.
Wei, X., Zhang, Z., Zhang, M., Chen, W., & Zeng, D. D. (2021). Combining Crowd and Machine Intelligence to Detect False News on Social Media. MIS Quarterly.
Summary: “The explosive spread of false news on social media has severely affected many areas such as news ecosystems, politics, economics, and public trust, especially amid the COVID-19 infodemic. Machine intelligence has met with limited success in detecting and curbing false news. Human knowledge and intelligence hold great potential to complement machine-based methods. Yet they are largely underexplored in current false news detection research, especially in terms of how to efficiently utilize such information. We observe that the crowd contributes to the challenging task of assessing the veracity of news by posting responses or reporting. We propose combining these two types of scalable crowd judgments with machine intelligence to tackle the false news crisis. Specifically, we design a novel framework called CAND, which first extracts relevant human and machine judgments from data sources including news features and scalable crowd intelligence. The extracted information is then aggregated by an unsupervised Bayesian aggregation model. Evaluation based on Weibo and Twitter datasets demonstrates the effectiveness of crowd intelligence and the superior performance of the proposed framework in comparison with the benchmark methods. The results also generate many valuable insights, such as the complementary value of human and machine intelligence, the possibility of using human intelligence for early detection, and the robustness of our approach to intentional manipulation. This research significantly contributes to relevant literature on false news detection and crowd intelligence. In practice, our proposed framework serves as a feasible and effective approach for false news detection.”
Keywords: Crowd intelligence, collective intelligence, crowdsourcing, misinformation, fake news, social media analytics, machine learning, and deep learning.
With the rapid growth of social media, the ease of access, sharing, and transfer of information by numerous users on various platforms have in part necessitated the rapid spread of misinformation and disinformation in all spheres of our life. Misleading information has been known to negatively impact our social lives, financial situations, and even political affiliations around the world. Today, the use of social media has become a key part of continuous human engagement in the sense that these platforms are available for users to share personal messages, pictures, videos, and other forms of multimedia. However, these changing trends have become catalysts for creating misleading activities including misinformation and disinformation such as fake news that can quickly spread through social networks. Therefore, the desire to confront the spread of false or misleading news remains a challenge for these social media platforms, policymakers, and law enforcement agencies. In other words, detecting and combating fake news has become imperative in today’s world. Consequently, several solutions have been proposed by the research community and professionals in the field including the application of machine intelligence, crowd technologies, and social media ranking algorithms to confront this infodemic menace.
According to Wei et al. [1], while human knowledge and machine intelligence have great potential to complement machine-based strategies in this direction, both of these entities still exhibit limited success in detecting and thwarting false news permeating through social media. The authors were of the view that crowd contributes to the challenging task of assessing the veracity of news and proposed combining the capabilities of crowd judgments with machine intelligence to tackle persistent false news. The research generated valuable insights based on synergy savings involving crowd techniques, and human and machine intelligence, which not only be useful for early detection but also for the intentional manipulation of information. Additionally, a recent stream of developments suggests that the proliferation of social media platforms promotes the prevalence of false news from generation to consumption of information with consequential effects on individuals and organizations in particular and society in general [1] – [4]. The authors of this baseline paper were able to achieve this in several folds. First, they surveyed several streams of relevant literature that serves as the theoretical foundations of their work. Second, they summarize existing studies about false news detection on social media. Third, they reviewed false news studies that are related to crowd intelligence. This approach was the basis of their proposed framework designed to aggregate the extracted judgments.
Even though social media platforms and tech giants such as Facebook, Amazon, Google, and others have started taking action to address the false news epidemic, they seem to lag behind the alarming and continuous spread of misinformation. In addition, the research community has also devoted much effort to address the prevalence of false news based on two types of data sources including news content and social context [1]. According to this baseline research, the authors acknowledged other major challenges noting that in real-world applications, the number of responses and reports usually increases daily while in the development of false news events, debunking information often happens at a later stage [1]. The fake news (i.e. infodemic), propagated by social media and other mobile message-sharing platforms, has progressed from causing a nuisance to seriously impacting law and order through deliberate and large-scale manipulation of public sentiments [4,14]. A typical example is a COVID-19 pandemic. The global uncertainty due to the pandemic has manifested in a breeding ground for fake news resulting in widespread panic and hindering the efforts of governments around the world to disseminate credible information to their citizens [4]. What makes the spread of false news terrifying is its distinctive characteristic of information sharing as a result of many of the users of social media hastily sharing every piece of news content they come across regardless of its source [7].
In the recent past, several studies [1] – [4], [8] – [14] have used machine intelligence related methods to detect false news and other forms of misinformation via various means such as in social media news articles crowd intelligence, and surveys. For example, one study noted that fake news has shown adverse effects of propagation on social media, and to mitigate these effects, it is required to detect fake news at an early stage when limited information about the news is available [13]. On another note, information sharing is the most important thing among human beings; however, the shared information needs to be authentic and realistic [6]. As well, it is a fact that the ability to distinguish truth from fake is a knowledge that people acquire through experience and age [7]. Furthermore, the proposed framework is a demonstration of the complementary value of human and machine intelligence in aiding false news detection, which could also be attributed to the broader literature on hybrid human-machine intelligence and other crowd intelligence applications [1]. The authors revealed that their research has several practical implications and actionable insights for relevant stakeholders. For social media platforms, the proposed CAND framework serves as a feasible and effective approach for false news detection on social media platforms. They believe that the practical implications translate into cost-effective measures, which can save social media platforms millions of dollars invested to thwart the spread of false news.
Other researchers have proposed state-of-the-art solutions to address the false news menace. In one paper, Chon and Kim proposed another excellent way to optimize social media analytics to manage crises by using the framework of attribution theory to analyze a bunch of tweets [2]. The authors indicated that social media analytics is a valid tool to monitor how the spread of COVID-19 evolved from an issue to a crisis. Others presented a novel collection of news articles originating from fake and real news media sources for the analysis and prediction of news virality [3]. Unlike existing fake news datasets, which contain news articles, the authors’ article collection is supported by a Facebook engagement count. Yet, Gupta et al. [4] presented a survey on combating fake news and evaluates the challenges involved in its detection with the help of existing detection mechanisms and techniques to control its spread. Additionally, Sharma et al, discussed existing methods and techniques applicable to both identification and mitigation, with a focus on the significant advances in each method and their advantages and limitations [14]. Further, in one study, an ensemble classifier was developed for detecting fake news with better accuracy results using the LIAR dataset [6]. In their study, Mladenova and Valova examined the ability to detect fake news and clickbait in the use of social networks [7]. To help understand the current state of affairs, Hu et al, conducted a survey to review and analyze existing deep learning-based fake news detection methods that focus on various features such as news content, social context, and external knowledge [8]. Furthermore, to address the data scarcity problems, one study proposed an automated approach for labeling data using verified fact-checked statements on a Twitter dataset [9]. Li et al. [10] and Chen [11] described the concept of crowd intelligence and explain its relationship to crowdsourcing and human computation. The authors also introduced four categories of representative crowd intelligence platforms as a solution [10]. Despite these research studies, misinformation campaigns, with the spread of false news, can also divert users’ attention from serious problems that need urgent attention.
In the final analysis, increased global access to emerging technologies and accompanying devices with the prevalence of social media has led to the exponential growth of information thereby creating an infodemic. In other words, we have a situation where a lot of information is being produced and shared in every corner of the world, thus reaching billions of users at once. the authors revealed that their research has several practical implications and actionable insights for relevant stakeholders. The information age enables people to obtain news online through various channels, yet in the meanwhile making false news spread at unprecedented speed and has detrimental effects on social stability and public trust [8]. Furthermore, given the role of popular social media platforms in recent political and economic climates, understanding such processes might enhance information and the impact of communication technology on living environments [11]. Ultimately, the unified CAND framework proposed by Wei et al. [1] for detecting fake news and halting its dissemination can further improve the usefulness of crowd and machine intelligence to mitigate the effect of false news propagated mostly by social media platforms.
view full article here: Employing innovative detection techniques to confront fake news
References:
[1] Wei, X., Zhang, Z., Zhang, M., Chen, W., & Zeng, D. D. (2021). Combining Crowd and Machine Intelligence to Detect False News on Social Media. MIS Quarterly. DOI: https://doi.org/10.25300/MISQ/2022/16256
[2] Chon, M. G., & Kim, S. (2022). Dealing with the COVID-19 crisis: Theoretical application of social media analytics in government crisis management. Public Relations Review, 48(3), 102201.
[3] Krstovski, K., Ryu, A. S., & Kogut, B. (2022). Evons: A Dataset for Fake and Real News Virality Analysis and Prediction. arXiv preprint arXiv:2209.08129.
[4] Gupta, A., Kumar, N., Prabhat, P., Gupta, R., Tanwar, S., Sharma, G., … & Sharma, R. (2022). Combating Fake News: Stakeholder Interventions and Potential Solutions. Ieee Access, 10, 78268-78289.
[5] Richet, J. L. (2013). Overt censorship: a fatal mistake? Communications of the ACM, 56(8), 37-38.
[6] Wynne, H. E., & Swe, K. T. (2022). Fake News Detection in Social Media using Two-Layers Ensemble Model. In 2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) (pp. 411-414). IEEE.
[7] Mladenova, T., & Valova, I. (2022). Research on the Ability to Detect Fake News in Users of Social Networks. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 01-04). IEEE.
[8] Hu, L., Wei, S., Zhao, Z., & Wu, B. (2022). Deep learning for fake news detection: A comprehensive survey. AI Open.
[9] Akhtar, M. M., Sharma, B., Karunanayake, I., Masood, R., Ikram, M., & Kanhere, S. S. (2022). Machine Learning-based Automatic Annotation and Detection of COVID-19 Fake News. arXiv preprint arXiv:2209.03162.
[10] Li, W., Wu, W. J., Wang, H. M., Cheng, X. Q., Chen, H. J., Zhou, Z. H., & Ding, R. (2017). Crowd intelligence in AI 2.0 era. Frontiers of Information Technology & Electronic Engineering, 18(1), 15-43.
[11] Chen, M. Y., Lytras, M. D., & Sangaiah, A. K. (2019). Anticipatory computing: Crowd intelligence from social network and big data. Computers in Human Behavior, 101, 350-351.
[12] Richet, J. L. (2022). How cybercriminal communities grow and change: An investigation of ad-fraud communities. Technological Forecasting and Social Change, 174, 121282.
[13] Rastogi, S., & Bansal, D. (2021, December). Time is Important in Fake News Detection: a short review. In 2021 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1441-1443). IEEE.
[14] Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., & Liu, Y. (2019). Combating fake news: A survey on identification and mitigation techniques. ACM Transactions on Intelligent Systems and Technology (TIST), 10(3), 1-42.
Impact of Proactive Cyber Threat Intelligence on Exploits from the Dark Web
Impact of Proactive Cyber Threat Intelligence on Exploits from the Dark Web
Lawrence J. Awuah
Abstract: The desire to defend against the ever-growing cyber threat landscape necessitates the need to link exploits from the Dark Web to known vulnerabilities with the sole aim of proactively utilizing Cyber Threat Intelligence (CTI) solutions, with Deep Learning (DL) model and Exploit Vulnerability Attention Deep Structured Semantic Model (EVA-DSSM), to maximize data protection, privacy, and security.
A review of “Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-based Deep Structured Semantic Model”. By Samtani, S., Chai, Y., & Chen, H. (2022). MIS Quarterly, 46(2), 911-946.
Summary: “Black hat hackers use malicious exploits to circumvent security controls and take advantage of system vulnerabilities worldwide, costing the global economy over $450 billion annually. While many organizations are increasingly turning to cyber threat intelligence (CTI) to help prioritize their vulnerabilities, extant CTI processes are often criticized as being reactive to known exploits. One promising data source that can help develop proactive CTI is the vast and ever-evolving Dark Web. In this study, we adopted the computational design science paradigm to design a novel Deep Learning (DL)-based Exploit Vulnerability Attention Deep Structured Semantic Model (EVA-DSSM) that includes bidirectional processing and attention mechanisms to automatically link exploits from the Dark Web to vulnerabilities. We also devised a novel Device Vulnerability Severity Metric (DVSM) that incorporates exploit postdate and vulnerability severity to help cybersecurity professionals with their device prioritization and risk management efforts. We rigorously evaluated the EVA-DSSM against state-of-the-art non-DL and DL-based methods for short text matching on 52,590 exploit-vulnerability linkages across four testbeds: web application, remote, local, and Denial of Service. Results of these evaluations indicate that the proposed EVA-DSSM achieves Precision at 1 scores 20% – 41% higher than non-DL approaches and 4% – 10% higher than DL-based approaches. We demonstrated the EVA-DSSM’s and DVSM’s practical utility with two CTI case studies: openly accessible systems in the top eight US hospitals and over 20,000 Supervisory Control and Data Acquisition (SCADA) systems worldwide. A complementary user evaluation of the case study results indicated that 45 cybersecurity professionals found the EVA-DSSM and DVSM results more useful for exploit-vulnerability linking and risk prioritization activities than those produced by prevailing approaches. Given the rising cost of cyber-attacks, the EVA-DSSM and DVSM have important implications for analysts in security operations centers, incident response teams, and cybersecurity vendors.”
Keywords: cyber threat intelligence, deep learning, deep structured semantic models, vulnerability assessment, hacker forums, dark web, security operations, cybersecurity analytics
The desire for researchers and subject matter experts to help organizations understand the complexity of attack vectors and support their cyber defense with automated incident response capabilities, driven by machine intelligence, has become so critical in today’s world. We have reached a point where cybersecurity trainees, researchers, and professionals need to continuously gain insights into innovative cybersecurity solutions in the field. The fact that malicious actors consistently use hacking techniques to circumvent security controls and exploit system vulnerabilities in the wake of the current threat landscape motivated Samtani et al. [1] to develop proactive Cyber Threat Intelligence (CTI) model from the perspective of the Dark Web. More to the point, pattern recognition, anomaly detection, and predictive analytics remain to offer threat intelligence and cybersecurity analytics capabilities that are key ingredients in automated incident response and threats mitigation efforts in the ever-evolving threat landscape.
Additionally, machine intelligence has become so ubiquitous and an indispensable tool, in defensive and offensive operations, that it remains to be a useful resource to cybersecurity leaders and device vendors. As part of their study, the authors adopted a novel Deep Learning (DL)-based model, an Exploit Vulnerability Attention Deep Structured Semantic Model (EVA-DSSM), which comprises bidirectional processing and attention mechanisms with the capability to automatically link exploits from the Dark Web to known vulnerabilities [1]. Additionally, a Device Vulnerability Severity Metric (DVSM) model was developed to be employed by cybersecurity professionals when engaging in device prioritization and risk management activities. A high-Level CTI Framework that captures EVA-DSSM and DVSM models is depicted in figure 1.
In another study, Zhu et al. [4] adopted a computational design science solution to develop a DL-based, hierarchical, multi-phase Activity of Daily Living (ADL) framework to address similar concerns. Yet, others deployed Tor-use Motivation Model (TMM) and found a network impacted by illicit commerce and money laundering and concluded that criminality on this dark web is based more upon greed and desire, rather than any particular political motivations [3]. These models and frameworks play key roles in emerging cybersecurity mitigation strategies.
Moreover, the vulnerability assessment as part of the automated CTI process, coupled with analytics, facilitate intelligence required by CTI professionals to conduct initial triage of security incidents for anticipated mitigation strategies. Motivated by the dynamic threat landscape, the authors develop a CTI framework and compared the operational differences between the conventional DSSM and their proposed EVA-DSSM [1]. When the proposed EVA-DSSM model was evaluated against both non-DL and DL-based methods for exploit-vulnerability linkages across selected testbeds (figure 1), the DL-based technique was determined to have achieved a much higher precision than the non-DL counterpart.
Furthermore, when a user evaluation of the CTI case study was conducted, the results indicated that a number of cybersecurity professionals found the EVA-DSSM and DVSM to be more efficient in exploitation-vulnerability linking and risk prioritization activities than those produced by conventional solutions. On the other hand, the user evaluation indicated that these professionals serving in the Security Operations Center (SOC), Incident Response (IR), Vulnerability Management (VM), and Operational Cybersecurity (OS) domains of practice found the EVA-DSSM and DVSM results more useful than those generated without these two models (figure 1). Given the rising cost of cyber-attacks, the EVA-DSSM and DVSM have perceived practical significance and important implications for analysts, for example, in the areas of security operations centers, incident response teams, and cybersecurity vendors.
In summary, there is a strong desire to support the fact that the practical and theoretical significance of the proposed EVA-DSSM and DVSM models evidently benefits analysts in SOC and IR teams, as well as security operations vendors. From the preceding analysis, there is also evidence to suggest that DL-based machine intelligence, as noted by the authors, plays a key role in SOC-related engagements. To that end, in mitigating evolving threats, organizations should empower the security operations teams and vendors with automated AI-based mitigation solutions. To efficiently mitigate these threats, organizations should endeavor to empower the security operations team and leadership with appropriate strategies needed to offer security orchestration and response processes to fully automate and manage the complexity of the SOC ecosystems [1-2]. In other words, the ability to seamlessly automate and manage the complexity of security operations to address the dynamic threat landscape remains an important challenge for security researchers, cybersecurity professionals, and cybersecurity vendors. Finally, from the preceding analysis, the EVA-DSSM and DVSM models certainly have crucial implications for those analysts in the SOC-based environment and cybersecurity vendors. Researchers and professionals alike have a major role to play in search of broader cybersecurity solutions for the interest of society.
See the full review here: Research Summary-Exploits from the Dark Web
References:
[1] Samtani, S., Chai, Y., & Chen, H. (2022). Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-based Deep Structured Semantic Model. MIS Quarterly, 46(2), 911-946. DOI: 10.25300/MISQ/2022/15392
[2] Kinyua, J. & Awuah, L. (2021). AI/ML in Security Orchestration, Automation and Response: Future Research Directions. Intelligent Automation & Soft Computing, 28(2), 527–545. DOI:10.32604/iasc.2021.016240
[3] Dalins, J., Wilson, C., & Carman, M. (2018). Criminal motivation on the dark web: A categorisation model for law enforcement. Digital Investigation, 24 (1), pp. 62-71. https://doi.org/10.1016/j.diin.2017.12.003
[4] Zhu, H., Samtani, S., Brown, R., & Chen, H. (2021). A Deep Learning Approach for Recognizing Activity of Daily Living (ADL) for Senior Care: Exploiting Interaction Dependency and Temporal Patterns. MIS Quarterly 45(2), pp. 859-896.
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Viewpoint – Non-consensual Pornography: How petty desire becomes a tragedy to an individual.
This article is the first of a new series of Viewpoints from Harvard Business School, Sorbonne Business School and ESSEC Business School students and faculty. Viewpoints section is dedicated to opinions and views that pertain to issues of broad interest to the cybersecurity community, typically, but not exclusively, of a nontechnical nature. Controversial issues will not be avoided but be dealt with fairly. Authors are welcome to submit carefully reasoned “Viewpoints” in which positions are substantiated by facts or principled arguments. Moreover, this section periodically hosts editorial debates in a Point/Counterpoint format in which both sides of an issue are represented.
Non-consensual Pornography: How petty desire becomes a tragedy to an individual.
Viewpoint by Heeju ROH (Harvard Business School)
There was a woman.
She did an ordinary love.
It was not a love life that bares all things, believes all things, hopes all things, and endures all things. But there was an affection: she and her lover couldn’t say each other’s name without smiling. There was a trust. The two people did not feel guilty about their own unspoken things. There were a lot other things and the love brought all of them. They thought, as it is commonly said, that they fell in love. However, it was wrong. By nature, love is not something you can consciously fall into. Love strikes people as if it is an accident. So it was rather obvious that it was also the love who called the end.
As left-overs, they did not know how to deal with the situation because they were both victims. Since the love already left them, their frustration lost its direction to head and destined to wrong targets – each other. Through the time of hurting each other more and more, they somehow survived as two separate individuals. And that was supposed to be it. But one day, she was told that there are pictures and videos of her privacy online. Records of their love, including evidences of the intimacy. Indeed, she could see two bodies. One of those had the same face with hers. However there was a difference between the face in the monitor with the face that she reflects on a mirror every morning. The face in the monitor did not have dignity or self-respect. It did not have a name or identity. It was merely a visual material to facilitate the ejaculation. Yet, it was undoubtedly her face and body.
I admit. Above case cannot possibly be the only background story of nonconsensual pornographies (NCP) in this big world. Maybe there are other victims who has been through a bad breakup, an abusive relationship, or other terrible situation before the leakage. Even if an uploader has built an aggression toward a victim, he or she does not earn an authority to share the private moment with unspecified mass viewers. We all should agree that the distribution of such material cannot be justified under any circumstance, period. However, we are often misled by the name ‘Revenge Porn.’ We are misled in a way that the victims are deserved to be revenged. More importantly, this perception results a general tendency to highlight an interpersonal and emotional conflict between the perpetrator and the victim, while diluting the fact that the NCP is a collective cybercrime. No wonder why bystanders who are unlikely to commit such crime shows certain level of approval upon NCP[i].
So, do I want to claim that NCPs are not really the result of ‘revenge’? Maybe, but that’s not the point. Currently, frequently suggested strategies to stop the NCP is more focused to victims’ protecting themselves. The reasons said are 1) that the victim must’ve agreed on the intercourse itself and 2) that, due to the highly viral environment of online platforms, the identification of the victim and instant reaction is better taken by the first party, the victim, than by the third party, the law enforcement for example. This could be also why even well-respected Medias rather recommend victims to “make sure that your face is not on the picture” or “use more secured application.”[ii] This tendency is an unfiltered evidence of our ignorance. The ignorance about the magnitude of the damage to the victims and the ignorance about the nature of the situation – the crime. The society forces the victims should be the one taking the burden of erasing fees and legal procedures, while dealing with PTSD, trust issues, and hostile social perception of “you deserve it”[iii]. Compare to the severe physical and psychological pain of the victims, the purpose of the NCP is ridiculously shallow – the amusement.
In the online world, we easily let ourselves indulge. Online world is the perfect place to let all of us to swim in the sea of our own gluttony, envy, greed and lust. Of course, it is rather acceptable if the voyeurism is directed to something not ethically challenging, such as mother’s recipe for the banana cake. Sharing information is the accomplishment of the 3rd industrial revolution. However, behind the curtain of anonymity, we also can consume other people’s private life easily and casually. And as the word ‘we’ suggests, there hardly is a sole perpetrator in the online world. They commit to this cyberbullying by creating, consuming, distributing, and making profit from NCPs. When facing the collective wave of violent behavior, individual victims always fail to protect themselves. Examples of victories are absolute minority considering the entire number of victims. The victory has to become our social norm, the general outcome, and expected result.
I believe that laws, policies, and systems are to stand at the front line of this battle. People’s feeling safe from possible harassments is the first job for normal nations to achieve. If people can hurt others and be hurt by the technology facilitated sexual violence without any rational expectation to be salvaged, that status is rather similar to the fight of all against all. To end this fight, we need more victim-focused responses. From the investigation processes, which are often traumatic for victims, to subsidy for erasing the materials and punishing the distributors[iv]. There have been studies and implementation of policies conducted. However, in reality, victims are rather to rely on civil associations than on law enforcement, because the civil associations tend to have more experience in such cases[v]. While the NCPs have become more accessible and affordable, the prosecutorial process has not become victim-friendly. Victims have to endure the ongoing tragedy until the legal process is over, which does not guarantee a fresh clean-up. As a result, the victims are easily left in the blind spot of the system.
Screaming requires a lot more effort than you think. It is not a knee-jerk reflection. Firstly the lungs have to be inflated as big as possible. Then your abdomen has to be flat and tighten in order to expectorate the air. At the moment of exhalation, the vocal cord tremors to deliver the sound wave. It’s a duty of nasal cavity to increase the sound frequency. Finally, as a quasi-verbal communication, this single-syllable sound has to deliver a message: Somebody help me. Unfortunately, the brain cannot often orchestrate the process. It endeavors to send signals to your lungs, abs, and vocal cords, but they simply fails in doing their works.
She felt that she had to scream at the moment she found her pictures. If the screaming was a cardio exercise, her brain must have sweated to be dehydrated. But the brain cannot sweat. So something else did instead: Her eyes released vast amount of salty water. Taking that as a signal, the other body parts finally responded. But it was different from what she imagined. The sound was rather low and growling. It was similar to something that every creatures make in the time of tragedy. It was an ordinary end of an ordinary love. However, because tragedies does not have an eye, they sometimes just barge into an ordinary life. So her ordinary life suddenly became tragic.
On the website, she also found other women. The women who also had faces and bodies without the name and dignity. She wondered what made all these women exposed. What have they done? And she realized that she already knew the answer – an ordinary love. They all did an ordinary love – no more, no less. Just an ordinary love.
* This article does not mean the victimization of all women nor generalization of all men.
References
[i] Lawson, K., “People Are Terrifyingly OK with Revenge Porn, New Study Finds,” Broadly, March 3, 2017. https://broadly.vice.com/en_us/article/43gwxq/people-are-terrifyingly-ok-with-revenge-porn-new-study-finds
[ii] Young, S., “How to protect yourself against revenge porn,” Independent, August 24, 2017. https://www.independent.co.uk/life-style/revenge-porn-how-to-protect-ex-partners-nude-photos-share-public-social-media-a7909736.html
[iii] Bates, Samantha Lynn. (2015) “Stripped”: an analysis of revenge porn victims’ lives after victimization.”
[iv] Dickson, Alyse (2016) “‘REVENGE PORN’: A VICTIM FOCUSED RESPONSE,” UNISA Student Law Review, Vol. 2.
[v] 정한라 (2013) “국내외 사이버폭력 사례 및 각국의 대응방안,” 한국인터넷진흥원