On the Generation of Cyber Threat Intelligence: Malware and Network Traffic Analyses

In recent years, malware authors drastically changed their course on the subject of threat design and implementation. Malware authors, namely, hackers or cyber-terrorists perpetrate new forms of cyber-crimes involving more innovative hacking techniques. Being motivated by financial or political reasons, attackers target computer systems ranging from personal computers to organizations’ networks to collect and steal sensitive data as well as blackmail, scam people, or scupper IT infrastructures. Accordingly, IT security experts face new challenges, as they need to counter cyber-threats proactively. The challenge takes a continuous allure of a fight, where cyber-criminals are obsessed by the idea of outsmarting security defenses. As such, security experts have to elaborate an effective strategy to counter cyber-criminals. The generation of cyber-threat intelligence is of a paramount importance as stated in the following quote: “the field is owned by who owns the intelligence”. In this thesis, we address the problem of generating timely and relevant cyber-threat intelligence for the purpose of detection, prevention and mitigation of cyber-attacks. To do so, we initiate a research effort, which falls into: First, we analyze prominent cyber-crime toolkits to grasp the inner-secrets and workings of advanced threats. We dissect prominent malware like Zeus and Mariposa botnets to uncover their underlying techniques used to build a networked army of infected machines. Second, we investigate cyber-crime infrastructures, where we elaborate on the generation of a cyber-threat intelligence for situational awareness. We adapt a graph-theoretic approach to study infrastructures used by malware to perpetrate malicious activities. We build a scoring mechanism based on a page ranking algorithm to measure the badness of infrastructures’ elements, i.e., domains, IPs, domain owners, etc. In addition, we use the min-hashing technique to evaluate the level of sharing among cyber-threat infrastructures during a period of one year. Third, we use machine learning techniques to fingerprint malicious IP traffic. By fingerprinting, we mean detecting malicious network flows and their attribution to malware families. This research effort relies on a ground truth collected from the dynamic analysis of malware samples. Finally, we investigate the generation of cyber-threat intelligence from passive DNS streams. To this end, we design and implement a system that generates anomalies from passive DNS traffic. Due to the tremendous nature of DNS data, we build a system on top of a cluster computing framework, namely, Apache Spark [70]. The integrated analytic system has the ability to detect anomalies observed in DNS records, which are potentially generated by widespread cyber-threats

Graph-theoretic characterization of cyber-threat infrastructures

In this paper, we investigate cyber-threats and the underlying infrastructures. More precisely, we detect and analyze cyber-threat infrastructures for the purpose of unveiling key players (owners, domains, IPs, organizations, malware families, etc.) and the relationships between these players. To this end, we propose metrics to measure the badness of different infrastructure elements using graph theoretic concepts such as centrality concepts and Google PageRank. In addition, we quantify the sharing of infrastructure elements among different malware samples and families to unveil potential groups that are behind specific attacks. Moreover, we study the evolution of cyber-threat infrastructures over time to infer patterns of cyber-criminal activities. The proposed study provides the capability to derive insights and intelligence about cyber-threat infrastructures. Using one year dataset, we generate notable results regarding emerging threats and campaigns, important players behind threats, linkages between cyber-threat infrastructure elements, patterns of cyber-crimes, etc

Forensic Data Analytics for Anomaly Detection in Evolving Networks

In the prevailing convergence of traditional infrastructure-based deployment (i.e., Telco and industry operational networks) towards evolving deployments enabled by 5G and virtualization, there is a keen interest in elaborating effective security controls to protect these deployments in-depth. By considering key enabling technologies like 5G and virtualization, evolving networks are democratized, facilitating the establishment of point presences integrating different business models ranging from media, dynamic web content, gaming, and a plethora of IoT use cases. Despite the increasing services provided by evolving networks, many cybercrimes and attacks have been launched in evolving networks to perform malicious activities. Due to the limitations of traditional security artifacts (e.g., firewalls and intrusion detection systems), the research on digital forensic data analytics has attracted more attention. Digital forensic analytics enables people to derive detailed information and comprehensive conclusions from different perspectives of cybercrimes to assist in convicting criminals and preventing future crimes. This chapter presents a digital analytics framework for network anomaly detection, including multi-perspective feature engineering, unsupervised anomaly detection, and comprehensive result correction procedures. Experiments on real-world evolving network data show the effectiveness of the proposed forensic data analytics solution.Comment: Electronic version of an article published as [Book Series: World Scientific Series in Digital Forensics and Cybersecurity, Volume 2, Innovations in Digital Forensics, 2023, Pages 99-137] [DOI:10.1142/9789811273209_0004] \c{opyright} copyright World Scientific Publishing Company [https://doi.org/10.1142/9789811273209_0004

Security Evaluation and Hardening of Free and Open Source Software (FOSS)

Recently, Free and Open Source Software (FOSS) has emerged as an alternative to Commercial-Off- The-Shelf (COTS) software. Now, FOSS is perceived as a viable long-term solution that deserves careful consideration because of its potential for significant cost savings, improved reliability, and numerous advantages over proprietary software. However, the secure integration of FOSS in IT infrastructures is very challenging and demanding. Methodologies and technical policies must be adapted to reliably compose large FOSS-based software systems. A DRDC Valcartier-Concordia University feasibility study completed in March 2004 concluded that the most promising approach for securing FOSS is to combine advanced design patterns and Aspect-Oriented Programming (AOP). Following the recommendations of this study a three years project have been conducted as a collaboration between Concordia University, DRDC Valcartier, and Bell Canada. This paper aims at presenting the main contributions of this project. It consists of a practical framework with the underlying solid semantic foundations for the security evaluation and hardening of FOSS

Defaming botnet toolkits: A bottom-up approach to mitigating the threat

Abstract-Botnets have become one of the most prevailing threats to today's Internet partly due to the underlying economic incentives of operating one. Botnet toolkits sold by their authors allow any layman to generate his/her own customized botnet and become a botmaster; botnet services sold by botmasters allow any criminal to steal identities and credit card information; finally, such stolen credentials are sold to end-users to make unauthorized transactions. Many existing botnet countermeasures meet inherent difficulties when they choose to target the botmasters or authors of toolkits, because those at the highest levels of this food chain are also the most technology-savvy and elusive. In this paper, we propose a different, bottom-up approach. That is, we defame botnet toolkits through discouraging or prosecuting the end-users of the stolen credentials. To make the concept concrete, we present a case study of applying the approach to a popular botnet toolkit, Zeus, with two methodologies, namely, reverse engineering and behavioural analysis