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Malware presents a persistent threat to user privacy and data integrity. To combat this, machine learning-based (ML-based) malware detection (MD) systems have been developed. However, these systems have increasingly been attacked in recent…

Computers may control safety-critical operations in machines having embedded software. This memoir proposes a regimen to verify such algorithms at prescribed levels of statistical confidence. The United States Department of Defense standard…

Key components of current cybersecurity methods are the Intrusion Detection Systems (IDSs) were different techniques and architectures are applied to detect intrusions. IDSs can be based either on cross-checking monitored events with a…

Cyber threats affect all kinds of organisations. Risk analysis is an essential methodology for cybersecurity as it allows organisations to deal with the cyber threats potentially affecting them, prioritise the defence of their assets and…

Machine learning based malware detectors become obsolete over time due to concept drift in benign and malware applications. Recent methods rely on fully labeled data and use hierarchical contrastive loss (HCL) with active learning to…

Societies' norms of operation relies on the proper and secure functioning of several critical infrastructures, particularly modern power grid which is also known as smart grid. Smart grid is interwoven with the information and communication…

In this document, we define the NADA security architecture based on refined use case scenarios, a derived high level model and security analysis. For the architecure design and verification we are applying the well known STRIDE model.

Previous studies have revealed that artificial intelligence (AI) systems are vulnerable to adversarial attacks. Among them, model extraction attacks fool the target model by generating adversarial examples on a substitute model. The core of…

As the number of connected IoT devices continues to grow, securing these systems against cyber threats remains a major challenge, especially in environments with limited computational and energy resources. This paper presents an…

The rapid proliferation of network applications has led to a significant increase in network attacks. According to the OWASP Top 10 Projects report released in 2021, injection attacks rank among the top three vulnerabilities in software…

As a result of decades of research, Windows malware detection is approached through a plethora of techniques. However, there is an ongoing mismatch between academia -- which pursues an optimal performances in terms of detection rate and low…

Cybersecurity threats are increasingly marked by interdependence, uncertainty, and evolving complexity challenges that traditional assessment methods such as CVSS, STRIDE, and attack trees fail to adequately capture. This paper reviews the…

Edge computing pushes computation closer to data sources, but it also expands the attack surface on resource-constrained devices. This work explores the deployment of the Lightweight Deep Anomaly Detection for Network Traffic (LDPI)…

In the dynamic cyber threat landscape, effective decision-making under uncertainty is crucial for maintaining robust information security. This paper introduces the Cyber Resilience Index (CRI), a threat-informed probabilistic approach to…

The growing number of Internet users and the prevalence of web applications make it necessary to deal with very complex software and applications in the network. This results in an increasing number of new vulnerabilities in the systems,…

Insider threats continue to present a major challenge for the information security community. Despite constant research taking place in this area; a substantial gap still exists between the requirements of this community and the solutions…

Backdoor learning is a critical research topic for understanding the vulnerabilities of deep neural networks. While the diffusion model (DM) has been broadly deployed in public over the past few years, the understanding of its backdoor…

In contemporary edge computing systems, decentralized edge nodes aggregate unprocessed data and facilitate data analytics to uphold low transmission latency and real-time data processing capabilities. Recently, these edge nodes have evolved…

As autonomous driving technology continues to advance, end-to-end models have attracted considerable attention owing to their superior generalisation capability. Nevertheless, such learning-based systems entail numerous safety risks…

Wireless sensor network (WSN) based SHM systems have shown significant improvement as compared to traditional wired-SHM systems in terms of cost, accuracy, and reliability of the monitoring. However, due to the resource-constrained nature…