Detecting time-fragmented cache attacks against AES using Performance Monitoring Counters

Cache timing attacks use shared caches in multi-core processors as side channels to extract information from victim processes. These attacks are particularly dangerous in cloud infrastructures, in which the deployed countermeasures cause collateral effects in terms of performance loss and increase in energy consumption. We propose to monitor the victim process using an independent monitoring (detector) process, that continuously measures selected Performance Monitoring Counters (PMC) to detect the presence of an attack. Ad-hoc countermeasures can be applied only when such a risky situation arises. In our case, the victim process is the AES encryption algorithm and the attack is performed by means of random encryption requests. We demonstrate that PMCs are a feasible tool to detect the attack and that sampling PMCs at high frequencies is worse than sampling at lower frequencies in terms of detection capabilities, particularly when the attack is fragmented in time to try to be hidden from detection

Using the Pattern-of-Life in Networks to Improve the Effectiveness of Intrusion Detection Systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.As the complexity of cyber-attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of Intrusion Detection Systems (IDSs) should be able to adapt their detection characteristics based not only on the measureable network traffic, but also on the available high- level information related to the protected network to improve their detection results. We make use of the Pattern-of-Life (PoL) of a network as the main source of high-level information, which is correlated with the time of the day and the usage of the network resources. We propose the use of a Fuzzy Cognitive Map (FCM) to incorporate the PoL into the detection process. The main aim of this work is to evidence the improved the detection performance of an IDS using an FCM to leverage on network related contextual information. The results that we present verify that the proposed method improves the effectiveness of our IDS by reducing the total number of false alarms; providing an improvement of 9.68% when all the considered metrics are combined and a peak improvement of up to 35.64%, depending on particular metric combination

Resilience of multi-robot systems to physical masquerade attacks

The advent of autonomous mobile multi-robot systems has driven innovation in both the industrial and defense sectors. The integration of such systems in safety-and security-critical applications has raised concern over their resilience to attack. In this work, we investigate the security problem of a stealthy adversary masquerading as a properly functioning agent. We show that conventional multi-agent pathfinding solutions are vulnerable to these physical masquerade attacks. Furthermore, we provide a constraint-based formulation of multi-agent pathfinding that yields multi-agent plans that are provably resilient to physical masquerade attacks. This formalization leverages inter-agent observations to facilitate introspective monitoring to guarantee resilience.Accepted manuscrip

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

Detection of advanced persistent threat using machine-learning correlation analysis

As one of the most serious types of cyber attack, Advanced Persistent Threats (APT) have caused major concerns on a global scale. APT refers to a persistent, multi-stage attack with the intention to compromise the system and gain information from the targeted system, which has the potential to cause significant damage and substantial financial loss. The accurate detection and prediction of APT is an ongoing challenge. This work proposes a novel machine learning-based system entitled MLAPT, which can accurately and rapidly detect and predict APT attacks in a systematic way. The MLAPT runs through three main phases: (1) Threat detection, in which eight methods have been developed to detect different techniques used during the various APT steps. The implementation and validation of these methods with real traffic is a significant contribution to the current body of research; (2) Alert correlation, in which a correlation framework is designed to link the outputs of the detection methods, aims to identify alerts that could be related and belong to a single APT scenario; and (3) Attack prediction, in which a machine learning-based prediction module is proposed based on the correlation framework output, to be used by the network security team to determine the probability of the early alerts to develop a complete APT attack. MLAPT is experimentally evaluated and the presented sy