Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks

Malware still constitutes a major threat in the cybersecurity landscape, also due to the widespread use of infection vectors such as documents. These infection vectors hide embedded malicious code to the victim users, facilitating the use of social engineering techniques to infect their machines. Research showed that machine-learning algorithms provide effective detection mechanisms against such threats, but the existence of an arms race in adversarial settings has recently challenged such systems. In this work, we focus on malware embedded in PDF files as a representative case of such an arms race. We start by providing a comprehensive taxonomy of the different approaches used to generate PDF malware, and of the corresponding learning-based detection systems. We then categorize threats specifically targeted against learning-based PDF malware detectors, using a well-established framework in the field of adversarial machine learning. This framework allows us to categorize known vulnerabilities of learning-based PDF malware detectors and to identify novel attacks that may threaten such systems, along with the potential defense mechanisms that can mitigate the impact of such threats. We conclude the paper by discussing how such findings highlight promising research directions towards tackling the more general challenge of designing robust malware detectors in adversarial settings

Binary Exploitation in Industrial Control Systems: Past, Present and Future

Despite being a decades-old problem, binary exploitation still remains a serious issue in computer security. It is mainly due to the prevalence of memory corruption errors in programs written with notoriously unsafe but yet indispensable programming languages like C and C++. For the past 30 years, the nip-and-tuck battle in memory between attackers and defenders has been getting more technical, versatile, and automated. With raised bar for exploitation in common information technology (IT) systems owing to hardened mitigation techniques, and with unintentionally opened doors into industrial control systems (ICS) due to the proliferation of industrial internet of things (IIoT), we argue that we will see an increased number of cyber attacks leveraging binary exploitation on ICS in the near future. However, while this topic generates a very rich and abundant body of research in common IT systems, there is a lack of systematic study targeting this topic in ICS. The present work aims at filling this gap and serves as a comprehensive walkthrough of binary exploitation in ICS. Apart from providing an analysis of the past cyber attacks leveraging binary exploitation on ICS and the ongoing attack surface transition, we give a review of the attack techniques and mitigation techniques on both general-purpose computers and embedded devices. At the end, we conclude this work by stressing the importance of network-based intrusion detection, considering the dominance of resource-constrained real-time embedded devices, low-end embedded devices in ICS, and the limited ability to deploy arbitrary defense mechanism directly on these devices

ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks

Despite the fact that cyberattacks are constantly growing in complexity, the research community still lacks effective tools to easily monitor and understand them. In particular, there is a need for techniques that are able to not only track how prominently certain malicious actions, such as the exploitation of specific vulnerabilities, are exploited in the wild, but also (and more importantly) how these malicious actions factor in as attack steps in more complex cyberattacks. In this paper we present ATTACK2VEC, a system that uses temporal word embeddings to model how attack steps are exploited in the wild, and track how they evolve. We test ATTACK2VEC on a dataset of billions of security events collected from the customers of a commercial Intrusion Prevention System over a period of two years, and show that our approach is effective in monitoring the emergence of new attack strategies in the wild and in flagging which attack steps are often used together by attackers (e.g., vulnerabilities that are frequently exploited together). ATTACK2VEC provides a useful tool for researchers and practitioners to better understand cyberattacks and their evolution, and use this knowledge to improve situational awareness and develop proactive defenses