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Denial-of-Service (DoS) and Distributed DoS (DDoS) attacks can cause serious problems in wireless networks due to limited network and host resources. Attacker traceback is a promising solution to take a proper countermeasure near the attack…
Peer-to-peer (P2P) networks are a cornerstone of modern computing, and their security is an active area of research. Many defenses with strong security guarantees have been proposed; however, the most-recent survey is over a decade old.…
Adversarial attacks on data-driven algorithms applied in the power system will be a new type of threat to grid security. Literature has demonstrated that the adversarial attack on the deep-neural network can significantly mislead the load…
The performance of control systems with packet loss as a result of an attack over the actuation communication channel is analysed. The operator is assumed to monitor the state of the channel by measuring the average number of packet losses…
In this paper we investigate the feasibility of denial-of-service (DoS) attacks on shared caches in multicore platforms. With carefully engineered attacker tasks, we are able to cause more than 300X execution time increases on a victim task…
Many IoT use cases involve constrained battery-powered devices offering services in a RESTful manner to their communication partners. Such services may involve, e.g., costly computations or actuator/sensor usage, which may have significant…
Mobile communication systems now constitute an essential part of life throughout the world. Fourth generation "Long Term Evolution" (LTE) mobile communication networks are being deployed. The LTE suite of specifications is considered to be…
The services of internet place a key role in the daily life by enabling the in sequence from anywhere. To provide somewhere to stay the communication and management in applications the web services has stimulated to multitier design. In…
Adversarial attacks can drastically degrade performance of recommenders and other machine learning systems, resulting in an increased demand for defence mechanisms. We present a new line of defence against attacks which exploit a…
The incremental diffusion of machine learning algorithms in supporting cybersecurity is creating novel defensive opportunities but also new types of risks. Multiple researches have shown that machine learning methods are vulnerable to…
The widespread adoption of Large Language Models (LLMs), exemplified by OpenAI's ChatGPT, brings to the forefront the imperative to defend against adversarial threats on these models. These attacks, which manipulate an LLM's output by…
Cybersecurity is a big challenge as hackers are always trying to find new methods to attack and exploit system vulnerabilities. Cybersecurity threats and risks have increased in recent years, due to the increasing number of devices and…
A distributed denial-of-service (DDoS) attack can flood a victim site with malicious traffic, causing service disruption or even complete failure. Public-access sites like amazon or ebay are particularly vulnerable to such attacks, because…
The severity of amplification attacks has grown in recent years. Since 2013 there have been at least two attacks which involved over 300Gbps of attack traffic. This paper offers an analysis of these and many other amplification attacks. We…
Large Language Models (LLMs) are increasingly used in applications where the model selects from competing third-party content, such as in LLM-powered search engines or chatbot plugins. In this paper, we introduce Preference Manipulation…
Denial of service attacks (DoS) can cause significant financial damages. Flooding and Malicious packets are two kinds of DoS attacks. This paper presents a new security approach which stops malicious packets and prevents flooding in the…
Adversarial extraction attacks constitute an insidious threat against Deep Learning (DL) models in-which an adversary aims to steal the architecture, parameters, and hyper-parameters of a targeted DL model. Existing extraction attack…
In addition to their security properties, adversarial machine-learning attacks and defenses have political dimensions. They enable or foreclose certain options for both the subjects of the machine learning systems and for those who deploy…
Algorithmic complexity vulnerabilities are a class of security problems that enables attackers to trigger the worst-case complexity of certain algorithms. Such vulnerabilities can be leveraged to deploy low-volume, asymmetric, CPU-based…
A number of important real-world protocols including the Transport Layer Security (TLS) protocol have the ability to negotiate various security-related choices such as the protocol version and the cryptographic algorithms to be used in a…