Related papers: A Survey on Data-driven Software Vulnerability Ass…
Open Source Software (OSS) security and resilience are worldwide phenomena hampering economic and technological innovation. OSS vulnerabilities can cause unauthorized access, data breaches, network disruptions, and privacy violations,…
Cybersecurity in robotics is an emerging topic that has gained significant traction. Researchers have demonstrated some of the potentials and effects of cyber attacks on robots lately. This implies safety related adverse consequences…
The rapid advancement of pre-trained language models (PLMs) has demonstrated promising results for various code-related tasks. However, their effectiveness in detecting real-world vulnerabilities remains a critical challenge. While existing…
Software supply chains (SSCs) are complex systems composed of dynamic, heterogeneous technical and social components which collectively achieve the production and maintenance of software artefacts. Attacks on SSCs are increasing, yet…
Vulnerability detection is crucial for identifying security weaknesses in software systems. However, training effective machine learning models for this task is often constrained by the high cost and expertise required for data annotation.…
Effective prioritization of issue reports is crucial in software engineering to optimize resource allocation and address critical problems promptly. However, the manual classification of issue reports for prioritization is laborious and…
Accurately predicting faulty software units helps practitioners target faulty units and prioritize their efforts to maintain software quality. Prior studies use machine-learning models to detect faulty software code. We revisit past studies…
As machine-learning (ML) based systems for malware detection become more prevalent, it becomes necessary to quantify the benefits compared to the more traditional anti-virus (AV) systems widely used today. It is not practical to build an…
Malicious software is abundant in a world of innumerable computer users, who are constantly faced with these threats from various sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can…
These days, cyber-criminals target humans rather than machines since they try to accomplish their malicious intentions by exploiting the weaknesses of end users. Thus, human vulnerabilities pose a serious threat to the security and…
Background: Timely prioritising and remediating vulnerabilities are paramount in the dynamic cybersecurity field, and one of the most widely used vulnerability scoring systems (CVSS) does not address the increasing likelihood of emerging an…
Self-supervised learning (SSL) has emerged as a promising solution for addressing the challenge of limited labeled data in deep neural networks (DNNs), offering scalability potential. However, the impact of design dependencies within the…
The National Vulnerability Database (NVD) is a major vulnerability database that is free to use for everyone. It provides information about vulnerabilities and further useful resources such as linked advisories and patches. The NVD is often…
Improvements in software defined networking allow for policy to be informed and modified by data-driven applications that can adjust policy to accommodate fluctuating requirements at line speed. However, there is some concern that…
With the advent of Software Defined Networks (SDNs), there has been a rapid advancement in the area of cloud computing. It is now scalable, cheaper, and easier to manage. However, SDNs are more prone to security vulnerabilities as compared…
Research shows that over the last decade, malware has been growing exponentially, causing substantial financial losses to various organizations. Different anti-malware companies have been proposing solutions to defend attacks from these…
Software Product Lines (SPL) are inherently difficult to test due to the combinatorial explosion of the number of products to consider. To reduce the number of products to test, sampling techniques such as combinatorial interaction testing…
The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative…
As bug-finding methods improve, bug-fixing capabilities are exceeded, resulting in an accumulation of potential vulnerabilities. There is thus a need for efficient and precise bug prioritization based on exploitability. In this work, we…
Data poisoning is a training-time attack that undermines the trustworthiness of learned models. In a targeted data poisoning attack, an adversary manipulates the training dataset to alter the classification of a targeted test point. Given…