Side-channel attacks on memory (SCAM) exploit unintended data leaks from memory subsystems to infer sensitive information, posing significant threats to system security. These attacks exploit vulnerabilities in memory access patterns, cache behaviors, and other microarchitectural features to bypass traditional security measures. The purpose of this research is to examine SCAM, classify various attack techniques, and evaluate existing defense mechanisms. It guides researchers and industry professionals in improving memory security and mitigating emerging threats. We begin by identifying the major vulnerabilities in the memory system that are frequently exploited in SCAM, such as cache timing, speculative execution, Rowhammer , and other sophisticated approaches. Next, we outline a comprehensive taxonomy that systematically classifies these attacks based on their types, target systems, attack vectors, and adversarial capabilities required to execute them. In addition, we review the current landscape of mitigation strategies, emphasizing their strengths and limitations. This work aims to provide a comprehensive overview of memory-based side-channel attacks with the goal of providing significant insights for researchers and practitioners to better understand, detect, and mitigate SCAM risks.

Network-based Intrusion Detection Systems (NIDSs) automate monitoring of events in networks and analyze them for signatures of cyberattacks. With the advancement of machine learning algorithms, more organizations started using machine learning based IDSs (ML-IDSs) to identify and mitigate cyberattacks. However, the lack of training datasets is a major challenge when implementing ML-IDSs. Therefore, using training data from external sources or transfer learning models are some solutions to overcome this challenge. However, using training data from external sources introduces the risk of backdoored datasets, specifically, when the adversaries also have background knowledge on data sources inside the target organization. This work investigates the role of backdoor attacks on intrusion detection techniques trained using multi-source data. The backdoor examples are injected into one or more training data sources. Transfer learning models are then created by projecting data from different sources into a new subspace containing all source data. The backdoor is then triggered in the target data. An anomaly-based intrusion detection classifier is applied to examine the effectiveness of the introduced backdoors. The results have shown that backdoor attacks on multis-source transfer learning models are feasible, although having less impact compared to backdoors on traditional machine learning models.