Related papers: Attack detection based on machine learning algorit…

Speculative execution enhances processor performance by predicting intermediate results and executing instructions based on these predictions. However, incorrect predictions can lead to security vulnerabilities, as speculative instructions…

Speculative execution is crucial in enhancing modern processor performance but can introduce Spectre-type vulnerabilities that may leak sensitive information. Detecting Spectre gadgets from programs has been a research focus to enhance the…

In large-scale networks, communication links between nodes are easily injected with false data by adversaries. This paper proposes a novel security defense strategy from the perspective of attack detection scheduling to ensure the security…

In this work we examine how the updates addressing Meltdown and Spectre vulnerabilities impact the performance of HPC applications. To study this we use the application kernel module of XDMoD to test the performance before and after the…

Side-channel attacks are a security exploit that take advantage of information leakage. They use measurement and analysis of physical parameters to reverse engineer and extract secrets from a system. Power analysis attacks in particular,…

Security issues in MANET are a challenging task nowadays. MANETs are vulnerable to passive attacks and active attacks because of a limited number of resources and lack of centralized authority. Blackhole attack is an attack in network layer…

Transient execution attacks that exploit speculation have raised significant concerns in computer systems. Typically, branch predictors are leveraged to trigger mis-speculation in transient execution attacks. In this work, we demonstrate a…

Existing speculative execution attacks are limited to breaching confidentiality of data beyond privilege boundary, the so-called spectre-type attacks. All of them utilize the changes in microarchitectural buffers made by the speculative…

Given the increased growing of Internet of Things networks and their presence in critical aspects of human activities, the security of devices connected to these networks becomes critical. Machine Learning approaches are becoming prominent…

Packing is an obfuscation technique widely used by malware to hide the content and behavior of a program. Much prior research has explored how to detect whether a program is packed. This research includes a broad variety of approaches such…

This article applies Machine Learning techniques to solve Intrusion Detection problems within computer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a…

Adversarial machine learning attacks on video action recognition models is a growing research area and many effective attacks were introduced in recent years. These attacks show that action recognition models can be breached in many ways.…

The security of computer systems fundamentally relies on memory isolation, e.g., kernel address ranges are marked as non-accessible and are protected from user access. In this paper, we present Meltdown. Meltdown exploits side effects of…

Runahead execution is a continuously evolving microarchitectural technique for processor performance. This paper introduces the first transient execution attack on the runahead execution, called SPECRUN, which exploits the unresolved branch…

Cache side channel attacks are increasingly alarming in modern processors due to the recent emergence of Spectre and Meltdown attacks. A typical attack performs intentional cache access and manipulates cache states to leak secrets by…

The transient-execution attack Meltdown leaks sensitive information by transiently accessing inaccessible data during out-of-order execution. Although Meltdown is fixed in hardware for recent CPU generations, most currently-deployed CPUs…

Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks were studied in this paper. The identification of attacks in computer networks is divided in to two categories of intrusion detection…

The growing cybersecurity threats make it essential to use high-quality data to train Machine Learning (ML) models for network traffic analysis, without noisy or missing data. By selecting the most relevant features for cyber-attack…

Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management. However, the algorithmic backbone of neural networks is brittle to imperceptible…

In the last two decades, the evolving cyber-threat landscape has brought to center stage the contentious tradeoffs between the security and performance of modern microprocessors. The guarantees provided by the hardware to ensure no…