Biblio

Cybersecurity for the past decades has been in the front line of global attention as a critical threat to the information technology infrastructures. According to recent security reports, malicious software (a.k.a. malware) is rising at an alarming rate in numbers as well as harmful purposes to compromise security of computing systems. To address the high complexity and computational overheads of conventional software-based detection techniques, Hardware-Supported Malware Detection (HMD) has proved to be efficient for detecting malware at the processors' microarchitecture level with the aid of Machine Learning (ML) techniques applied on Hardware Performance Counter (HPC) data. Existing ML-based HMDs while accurate in recognizing known signatures of malicious patterns, have not explored detecting unknown (zero-day) malware data at run-time which is a more challenging problem, since its HPC data does not match any known attack applications' signatures in the existing database. In this work, we first present a review of recent ML-based HMDs utilizing built-in HPC registers information. Next, we examine the suitability of various standard ML classifiers for zero-day malware detection and demonstrate that such methods are not capable of detecting unknown malware signatures with high detection rate. Lastly, to address the challenge of run-time zero-day malware detection, we propose an ensemble learning-based technique to enhance the performance of the standard malware detectors despite using a small number of microarchitectural features that are captured at run-time by existing HPCs. The experimental results demonstrate that our proposed approach by applying AdaBoost ensemble learning on Random Forrest classifier as a regular classifier achieves 92% F-measure and 95% TPR with only 2% false positive rate in detecting zero-day malware using only the top 4 microarchitectural features.

In this paper, an Intrusion Detection System (IDS) in the Controller Area Network (CAN) bus of modern vehicles has been proposed. NESLIDS is an anomaly detection algorithm based on the supervised Deep Neural Network (DNN) architecture that is designed to counter three critical attack categories: Denial-of-service (DoS), fuzzy, and impersonation attacks. Our research scope included modifying DNN parameters, e.g. number of hidden layer neurons, batch size, and activation functions according to how well it maximized detection accuracy and minimized the false positive rate (FPR) for these attacks. Our methodology consisted of collecting CAN Bus data from online and in real-time, injecting attack data after data collection, preprocessing in Python, training the DNN, and testing the model with different datasets. Results show that the proposed IDS effectively detects all attack types for both types of datasets. NESLIDS outperforms existing approaches in terms of accuracy, scalability, and low false alarm rates.