Related papers: Predictive Cyber-security Analytics Framework: A n…

Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone to evasion techniques such as binary obfuscation, while…

Cyber threat intelligence is one of the emerging areas of focus in information security. Much of the recent work has focused on rule-based methods and detection of network attacks using Intrusion Detection algorithms. In this paper we…

Malware represents a significant security concern in today's digital landscape, as it can destroy or disable operating systems, steal sensitive user information, and occupy valuable disk space. However, current malware detection methods,…

With the development of information technology, the border of the cyberspace gets much broader, exposing more and more vulnerabilities to attackers. Traditional mitigation-based defence strategies are challenging to cope with the current…

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…

Traditional cybersecurity methodologies target deterministic systems and fail to address the probabilistic nature of AI, leaving systems vulnerable to attack vectors such as model inversion, data poisoning, and prompt injection. Recent…

This paper is devoted to measuring the security of cyber networks under advanced persistent threats (APTs). First, an APT-based cyber attack-defense process is modeled as an individual-level dynamical system. Second, the dynamic model is…

We present a novel malware detection approach based on metrics over quantitative data flow graphs. Quantitative data flow graphs (QDFGs) model process behavior by interpreting issued system calls as aggregations of quantifiable data…

Cybersecurity is the security cornerstone of digital transformation of the power grid and construction of new power systems. The traditional network security situation quantification method only analyzes from the perspective of network…

While most security projects have focused on fending off attacks coming from outside the organizational boundaries, a real threat has arisen from the people who are inside those perimeter protections. Insider threats have shown their power…

In cyberspace, evolutionary strategies are commonly used by both attackers and defenders. For example, an attacker's strategy often changes over the course of time, as new vulnerabilities are discovered and/or mitigated. Similarly, a…

In today's increasingly interconnected and fast-paced digital ecosystem, mobile networks, such as 5G and future generations such as 6G, play a pivotal role and must be considered as critical infrastructures. Ensuring their security is…

Many techniques have been proposed for quickly detecting and containing malware-generated network attacks such as large-scale denial of service attacks; unfortunately, much damage is already done within the first few minutes of an attack,…

Machine learning and data mining techniques are utiized for enhancement of the security of any network. Researchers used machine learning for pattern detection, anomaly detection, dynamic policy setting, etc. The methods allow the program…

In this paper, we consider the problem of safety assessment for Markov decision processes without explicit knowledge of the model. We aim to learn probabilistic safety specifications associated with a given policy without compromising the…

Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network…

The new generation of cyber threats leverages advanced AI-aided methods, which make them capable to launch multi-stage, dynamic, and effective attacks. Current cyber-defense systems encounter various challenges to defend against such new…

We study the problem of estimating a temporally varying coefficient and varying structure (VCVS) graphical model underlying nonstationary time series data, such as social states of interacting individuals or microarray expression profiles…

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…

The emergence of quantum computing poses a formidable security challenge to network protocols traditionally safeguarded by classical cryptographic algorithms. This paper provides an exhaustive analysis of vulnerabilities introduced by…