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This paper investigates the temporal analysis of NetFlow datasets for machine learning (ML)-based network intrusion detection systems (NIDS). Although many previous studies have highlighted the critical role of temporal features, such as…

A system vulnerability analysis technique (SVAT) for complex mission critical systems (CMCS) was developed in response to the need to be able to conduct penetration testing on large industrial systems which cannot be taken offline or risk…

Verifying integrity of software execution in low-end micro-controller units (MCUs) is a well-known open problem. The central challenge is how to securely detect software exploits with minimal overhead, since these MCUs are designed for low…

Most intrusion detection systems still identify attacks only after significant damage has occurred, detecting late-stage tactics rather than early indicators of compromise. This paper introduces a temporal analysis framework and taxonomy…

Predicting drug-target binding affinity (DTA) is essential for identifying potential therapeutic candidates in drug discovery. However, most existing models rely heavily on static protein structures, often overlooking the dynamic nature of…

As Artificial Intelligence (AI) technologies continue to gain traction in the modern-day world, they ultimately pose an immediate threat to current cybersecurity systems via exploitative methods. Prompt engineering is a relatively new field…

IDS aims to protect computer networks from security threats by detecting, notifying, and taking appropriate action to prevent illegal access and protect confidential information. As the globe becomes increasingly dependent on technology and…

The Cyber world is plagued with ever-evolving malware that readily infiltrates all defense mechanisms, operates viciously unbeknownst to the user and surreptitiously exfiltrate sensitive data. Understanding the inner workings of such…

Timely response of Network Intrusion Detection Systems (NIDS) is constrained by the flow generation process which requires accumulation of network packets. This paper introduces Multivariate Time Series (MTS) early detection into NIDS to…

The emerging wide area monitoring systems (WAMS) have brought significant improvements in electric grids' situational awareness. However, the newly introduced system can potentially increase the risk of cyber-attacks, which may be disguised…

Traditional intrusion detection systems (IDSs) often rely on either network traffic or process data, but this single-source approach may miss complex attack patterns that span multiple layers within industrial control systems (ICSs) or…

Machine learning (ML) methods provide advanced means for understanding inherent patterns within large and complex datasets. Here, we employ the principal component analysis (PCA) and the diffusion map (DM) techniques to evaluate the glass…

Machine learning (ML) methods are widely used in industrial applications, which usually require a large amount of training data. However, data collection needs extensive time costs and investments in the manufacturing system, and data…

In cybersecurity it is often the case that malicious or anomalous activity can only be detected by combining many weak indicators of compromise, any one of which may not raise suspicion when taken alone. The path that such indicators take…

The effectiveness of Intrusion Detection Systems (IDS) is critical in an era where cyber threats are becoming increasingly complex. Machine learning (ML) and deep learning (DL) models provide an efficient and accurate solution for…

Software-based attacks exploit bugs or vulnerabilities to get unauthorized access or leak confidential information. Dynamic information flow tracking (DIFT) is a security technique to track spurious information flows and provide strong…

In response to Distributed Denial of Service (DDoS) attacks, recent research efforts increasingly rely on Machine Learning (ML)-based solutions, whose effectiveness largely depends on the quality of labeled training datasets. To address the…

The in-vehicle diagnostic and software update system, which supports remote diagnostic and Over-The-Air (OTA) software updates, is a critical attack goal in automobiles. Adversaries can inject malicious software into vehicles or steal…

Over the past decade, industrial control systems have experienced a massive integration with information technologies. Industrial networks have undergone numerous technical transformations to protect operational and production processes,…

ML-based systems are software systems that incorporates machine learning components such as Deep Neural Networks (DNNs) or Large Language Models (LLMs). While such systems enable advanced features such as high performance computer vision,…