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Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used…
When planning to change operations at ports there are two key stake holders with very different interests involved in the decision making processes. Port operators are attentive to their standards, a smooth service flow and economic…
Intrusion Detection Systems (IDS) are key components for securing critical infrastructures, capable of detecting malicious activities on networks or hosts. The procedure of implementing a IDS for Internet of Things (IoT) networks is not…
We review state-of-the-art formal methods applied to the emerging field of the verification of machine learning systems. Formal methods can provide rigorous correctness guarantees on hardware and software systems. Thanks to the availability…
Timing-based side and covert channels in processor caches continue to be a threat to modern computers. This work shows for the first time a systematic, large-scale analysis of Arm devices and the detailed results of attacks the processors…
Patch backporting, the process of migrating mainline security patches to older branches, is an essential task in maintaining popular open-source projects (e.g., Linux kernel). However, manual backporting can be labor-intensive, while…
Vulnerability detection plays a key role in secure software development. There are many different vulnerability detection tools and techniques to choose from, and insufficient information on which vulnerability detection techniques to use…
Intrusion Detection Systems are widely used to detect cyberattacks, especially on protocols vulnerable to hacking attacks such as SOME/IP. In this paper, we present a deep learning-based sequential model for offline intrusion detection on…
Modern organizations struggle with insurmountable number of vulnerabilities that are discovered and reported by their network and application vulnerability scanners. Therefore, prioritization and focus become critical, to spend their…
This research proposes a machine learning-based attack detection model for power systems, specifically targeting smart grids. By utilizing data and logs collected from Phasor Measuring Devices (PMUs), the model aims to learn system…
Dark web crawling is a complex process that involves specific methodologies and techniques to navigate the Tor network and extract data from hidden services. This study proposes a general dark web crawler designed to extract pages handling…
The security trade confidentiality, integrity and availability are the main pillar of the information systems as every organization emphasize of the security. From last few decades, digital data is the main asset for every digital or…
The increasing reliance on software in various applications has made the problem of software vulnerability detection more critical. Software vulnerabilities can lead to security breaches, data theft, and other negative outcomes. Traditional…
Passive operating system fingerprinting reveals valuable information to the defenders of heterogeneous private networks; at the same time, attackers can use fingerprinting to reconnoiter networks, so defenders need obfuscation techniques to…
Security patterns are a means to encapsulate and communicate proven security solutions. They are well-established approaches for introducing security into the software development process. Our objective is to explore the research efforts on…
Deep learning is increasingly used as a building block of security systems. Unfortunately, neural networks are hard to interpret and typically opaque to the practitioner. The machine learning community has started to address this problem by…
We present an application of Artificial Intelligence techniques to the field of Information Security. The problem of remote Operating System (OS) Detection, also called OS Fingerprinting, is a crucial step of the penetration testing…
One of the major and serious threats that the Internet faces today is the vast amounts of data and files which need to be evaluated for potential malicious intent. Malicious software, often referred to as a malware that are designed by…
Advanced Persistent Threats (APTs) are sophisticated multi-step attacks, planned and executed by skilled adversaries targeting modern government and enterprise networks. Intrusion Detection Systems (IDSs) and User and Entity Behavior…
Insider threats is the most concerned cybersecurity problem which is poorly addressed by widely used security solutions. Despite the fact that there have been several scientific publications in this area, but from our innovative study…