Mehrdad Hajizadeh

Technische Universität Chemnitz · Department of Electrical Engineering and Information Technology

Neural networks (NNs) have been extensively adapted to various security tasks, such as spam detection, phishing, and intrusion detection. Particularly in IDS, NNs face significant vulnerabilities to adversarial attacks, where the adversary attempts to exploit the fragilities within machine-learning (ML) models. This study introduces a novel approac...

The rise of Large Language Models (LLMs) has revolutionized our comprehension of intelligence bringing us closer to Artificial Intelligence. Since their introduction, researchers have actively explored the applications of LLMs across diverse fields, significantly elevating capabilities. Cybersecurity, traditionally resistant to data-driven solution...

As the first line of defense, an Intrusion Detection System (IDS) plays a crucial role in early cyber threat detection and successful mitigation operations. IDSs are increasingly adopting machine learning (ML)-based methods to enhance their detection engines by learning network traffic characteristics and improving the scalability. Although ML-based...

In this paper, we propose a destination-aware adaptive traffic flow rule aggregation (DATA) mechanism for facilitating traffic flow monitoring in SDN-based networks. This method adapts the number of flow table entries in SDN switches according to the level of detail of traffic flow information that other mechanisms (e.g. for traffic engineering, tr...

Risk assessment plays a key role in the information security management of organizations to protect their assets against cyber threats and reduce the risk of attacks to their infrastructures. One key element in the risk assessment process is the determination of the probability of occurrence of elementary attack actions. For that, this paper sugges...