Related papers: Non-collaborative Attackers and How and Where to D…

Everyone is concerned about the Internet security, yet most traffic is not cryptographically protected. The usual justification is that most attackers are only off-path and cannot intercept traffic; hence, challenge-response mechanisms…

The recent application of Federated Learning algorithms in IOT and Wireless vehicular networks have given rise to newer cyber threats in the mobile environment which hitherto were not present in traditional fixed networks. These threats…

Computer network defence is a complicated task that has necessitated a high degree of human involvement. However, with recent advancements in machine learning, fully autonomous network defence is becoming increasingly plausible. This paper…

The congestion control algorithm of TCP relies on correct feedback from the receiver to determine the rate at which packets should be sent into the network. Hence, correct receiver feedback (in the form of TCP acknowledgements) is essential…

Serverless computing is an emerging cloud paradigm with serverless functions at its core. While serverless environments enable software developers to focus on developing applications without the need to actively manage the underlying…

In recent years, non-control-data attacks have be come a research hotspot in the field of network security, driven by the increasing number of defense methods against control-flow hijacking attacks. These attacks exploit memory…

Federated learning enables learning from decentralized data sources without compromising privacy, which makes it a crucial technique. However, it is vulnerable to model poisoning attacks, where malicious clients interfere with the training…

In order to detect unknown intrusions and runtime errors of computer programs, the cyber-security community has developed various detection techniques. Anomaly detection is an approach that is designed to profile the normal runtime behavior…

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…

This paper investigates the performance of various Border Gateway Protocol (BGP) security policies against multiple attack scenarios using different deployment strategies. Through extensive simulations, we evaluate the effectiveness of…

The rapid development of information and network technologies motivates the emergence of various new computing paradigms, such as distributed computing, and edge computing. This also enables more and more network enterprises to provide…

We address the problem of securing distributed storage systems against eavesdropping and adversarial attacks. An important aspect of these systems is node failures over time, necessitating, thus, a repair mechanism in order to maintain a…

In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can…

Federated learning is known to be vulnerable to both security and privacy issues. Existing research has focused either on preventing poisoning attacks from users or on concealing the local model updates from the server, but not both.…

The existence of adversarial attacks on convolutional neural networks (CNN) questions the fitness of such models for serious applications. The attacks manipulate an input image such that misclassification is evoked while still looking…

The need for secure and private Artificial Intelligence (AI) and Machine Learning (ML) on edge and mobile devices has increased the necessity of protecting the architecture of these systems from threats to both security and privacy. With an…

Machine learning has been successfully applied to complex network analysis in various areas, and graph neural networks (GNNs) based methods outperform others. Recently, adversarial attack on networks has attracted special attention since…

Though deep neural networks have achieved state-of-the-art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversarial examples. Small and often imperceptible perturbations to…

Repair operations in distributed storage systems potentially expose the data to malicious acts of passive eavesdroppers or active adversaries, which can be detrimental to the security of the system. This paper presents erasure codes and…

This paper considers the problem of security allocation in a networked control system under stealthy attacks. The system is comprised of interconnected subsystems represented by vertices. A malicious adversary selects a single vertex on…