Unveiling the Spectre Vulnerabilities Analysis, Detection, and Countermeasures

This research study looks at the use of machine learning methods to identify Spectre attacks, a family of serious security flaws that prey on processor flaws in Linux-based systems. Data security is seriously threatened by Spectre attacks, which call for sophisticated detection techniques. In order to discriminate between typical system behavior and Spectre assaults, the research examines how well three machine learning algorithms-Support Vector Machine (SVM), Decision Tree, and AdaBoost Classifier-perform. The results provide a complex assessment of these algorithms' effectiveness. With high accuracy, recall, precision, F1 score, and AUC score, SVM emerges as a strong competitor, highlighting its capacity to accurately detect Spectre threats while reducing false positives. AdaBoostClassifier also performs well, providing a well-balanced strategy between recall and accuracy. While decision trees are excellent at accuracy, recall is where they fall short. This study emphasizes how critical it is to strike a balance between accuracy and recall while detecting Spectre attacks since doing so affects the likelihood of both false positives and false negatives. The study encourages additional research in this dynamic and constantly changing field of cybersecurity and lays the road for the use of machine learning to improve Linux-based system security.