Related papers: Eradicating Attacks on the Internal Network with I…

Storage networking technology has enjoyed strong growth in recent years, but security concerns and threats facing networked data have grown equally fast. Today, there are many potential threats that are targeted at storage networks,…

This paper deals with denial of service attack. Overview of the existing attacks and methods is proposed. Classification scheme is presented for a different denial of service attacks. There is considered agent-based intrusion detection…

Although deep neural networks (DNNs) have achieved a great success in various computer vision tasks, it is recently found that they are vulnerable to adversarial attacks. In this paper, we focus on the so-called \textit{backdoor attack},…

The significance of the DDoS problem and the increased occurrence, sophistication and strength of attacks has led to the dawn of numerous prevention mechanisms. Each proposed prevention mechanism has some unique advantages and disadvantages…

Backdoor attacks have severely threatened deep neural network (DNN) models in the past several years. These attacks can occur in almost every stage of the deep learning pipeline. Although the attacked model behaves normally on benign…

Several randomization mechanisms for local differential privacy (LDP) (e.g., randomized response) are well-studied to improve the utility. However, recent studies show that LDP is generally vulnerable to malicious data providers in nature.…

Current best practices heavily control user permissions on network systems. This effectively mitigates many insider threats regarding the collection and exfiltration of data. Many methods of covert communication involve crafting custom…

Cloud providers' support for network evasion techniques that misrepresent the server's domain name is more prevalent than previously believed, which has serious implications for security and privacy due to the reliance on domain names in…

We develop and study new adversarial perturbations that enable an attacker to gain control over decisions in generic Artificial Intelligence (AI) systems including deep learning neural networks. In contrast to adversarial data modification,…

Central nodes are critical in establishing structural connectivity in a complex network. Attacking such nodes can create real havoc in a complex system. We propose attack strategies based on four types of centers, namely betweenness center,…

Deception is being increasingly explored as a cyberdefense strategy to protect operational systems. We are studying implementation of deception-in-depth strategies with initially three logical layers: network, host, and data. We draw ideas…

Distributed Reflective Denial of Service (DRDoS) attacks are an immanent threat to Internet services. The potential scale of such attacks became apparent in March 2018 when a memcached-based attack peaked at 1.7 Tbps. Novel services built…

Security is important for many sensor network applications. Wireless Sensor Networks (WSN) are often deployed in hostile environments as static or mobile, where an adversary can physically capture some of the nodes. once a node is captured,…

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…

With the broad application of deep neural networks (DNNs), backdoor attacks have gradually attracted attention. Backdoor attacks are insidious, and poisoned models perform well on benign samples and are only triggered when given specific…

Deep neural networks (DNNs) are vulnerable to backdoor attacks. The backdoor adversaries intend to maliciously control the predictions of attacked DNNs by injecting hidden backdoors that can be activated by adversary-specified trigger…

Focus on Deep Neural Network based malicious and normal computer Network Traffic classification. (such as attacks, phishing, any other illegal activity and normal traffic identification). In this paper, the main idea is to review, existed…

By developing communications and increase of access points, computer networks have been vulnerable considerably against wide range of information attacks, specially new and complicated attacks. Every day, replication attacks attack millions…

We have witnessed the continuing arms race between backdoor attacks and the corresponding defense strategies on Deep Neural Networks (DNNs). Most state-of-the-art defenses rely on the statistical sanitization of the "inputs" or "latent DNN…

From tiny pacemaker chips to aircraft collision avoidance systems, the state-of-the-art Cyber-Physical Systems (CPS) have increasingly started to rely on Deep Neural Networks (DNNs). However, as concluded in various studies, DNNs are highly…