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The landscape of automotive vehicle attack surfaces continues to grow, and vulnerabilities in the controller area network (CAN) expose vehicles to cyber-physical risks and attacks that can endanger the safety of passengers and pedestrians. Intrusion detection systems (IDS) for CAN have emerged as a key mitigation approach for these risks, but uniform methods to compare proposed IDS techniques are lacking. In this paper, we present a framework for comparative performance analysis of state-of-the-art IDSs for CAN bus to provide a consistent methodology to evaluate and assess proposed approaches. This framework relies on previously published datasets comprising message logs recorded from a real vehicle CAN bus coupled with traditional classifier performance metrics to reduce the discrepancies that arise when comparing IDS approaches from disparate sources. 

Malware detection and analysis can be a burdensome task for incident responders. As such, research has turned to machine learning to automate malware detection and malware family classification. Existing work extracts and engineers static and dynamic features from the malware sample to train classifiers. Despite promising results, such techniques assume that the analyst has access to the malware executable file. Self-deleting malware invalidates this assumption and requires analysts to find forensic evidence of malware execution for further analysis. In this paper, we present and evaluate an approach to detecting malware that executed on a Windows target and further classify the malware into its associated family to provide semantic insight. Specifically, we engineer features from the Windows prefetch file, a file system forensic artifact that archives process information. Results show that it is possible to detect the malicious artifact with 99% accuracy; furthermore, classifying the malware into a fine-grained family has comparable performance to techniques that require access to the original executable. We also provide a thorough security discussion of the proposed approach against adversarial diversity. 

Network intrusion detection systems (NIDS) today must quickly provide visibility into anomalous behavior on a growing amount of data. Meanwhile different data models have evolved over time, each providing a different set of features to classify attacks. Defenders have limited time to retrain classifiers, while the scale of data and feature mismatch between data models can affect the ability to periodically retrain. Much work has focused on classification accuracy yet feature selection is a key part of machine learning that, when optimized, reduces the training time and can increase accuracy by removing poorly performing features that introduce noise. With a larger feature space, the pursuit of more features is not as valuable as selecting better features. In this paper, we use an ensemble approach of filter methods to rank features followed by a voting technique to select a subset of features. We evaluate our approach using three datasets to show that, across datasets and network topologies, similar features have a trivial effect on classifier accuracy after removal. Our approach identifies poorly performing features to remove in a classifier-agnostic manner that can significantly save time for periodic retraining of production NIDS. 

In a network of mining pools that secure Bitcoin-like blockchains, it is known that a self-interested mining pool can dishonestly siphon off another pool’s mining rewards by executing a block withholding (BWH) attack. In this paper, we show that a BWH attack is always unprofitable for an initial startup period which is at least one difficulty retarget interval (approximately 14 days for Bitcoin). Furthermore, we prove that the payback period to recoup this initial startup cost is always at least as long as the initial unprofitable startup interval, and we show numerically that it can be substantially longer. Thus, the decision of whether or not to execute a BWH attack is not a dominant strategy, and the so called Miner’s Dilemma is not in fact a dilemma. 

The secure functioning of automotive systems is vital to the safety of their passengers and other roadway users. One of the critical functions for safety is the controller area network (CAN), which interconnects the safety-critical electronic control units (ECUs) in the majority of ground vehicles. Unfortunately CAN is known to be vulnerable to several attacks. One such attack is the bus-off attack, which can be used to cause a victim ECU to disconnect itself from the CAN bus and, subsequently, for an attacker to masquerade as that ECU. A limitation of the bus-off attack is that it requires the attacker to achieve tight synchronization between the transmission of the victim and the attacker’s injected message. In this paper, we introduce a schedule-based attack framework for the CAN bus-off attack that uses the real-time schedule of the CAN bus to predict more attack opportunities than previously known. We describe a ranking method for an attacker to select and optimize its attack injections with respect to criteria such as attack success rate, bus perturbation, or attack latency. The results show that vulnerabilities of the CAN bus can be enhanced by schedulebased attacks.