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Ransomware attacks continue to wreak havoc across the globe, with public reports of total ransomware payments topping billions of dollars annually. While the use of cryptocurrency presents an avenue to understand the tactics of ransomware…

As the number of cyberattacks and their particualr nature escalate, the need for effective intrusion detection systems (IDS) has become indispensable for ensuring the security of contemporary networks. Adaptive and more sophisticated…

The increasing sophistication of encryption-based ransomware has demanded innovative approaches to detection and mitigation, prompting the development of a hierarchical framework grounded in probabilistic cryptographic analysis. By focusing…

Malware detection have used machine learning to detect malware in programs. These applications take in raw or processed binary data to neural network models to classify as benign or malicious files. Even though this approach has proven…

The rapid growth of Cloud Computing and Internet of Things (IoT) has significantly increased the interconnection of computational resources, creating an environment where malicious software (malware) can spread rapidly. To address this…

Modern threat landscapes continue to evolve with increasing sophistication, challenging traditional detection methodologies and necessitating innovative solutions capable of addressing complex adversarial tactics. A novel framework was…

Machine learning is a popular approach to signatureless malware detection because it can generalize to never-before-seen malware families and polymorphic strains. This has resulted in its practical use for either primary detection engines…

In the face of increasing cyber threats, particularly ransomware attacks, there is a pressing need for advanced detection and analysis systems that adapt to evolving malware behaviours. Throughout the literature, using machine learning (ML)…

Ransomware has emerged as an infamous malware that has not escaped a lot of myths and inaccuracies from media hype. Victims are not sure whether or not to pay a ransom demand without fully understanding the lurking consequences. In this…

In the modern era, malware is experiencing a significant increase in both its variety and quantity, aligning with the widespread adoption of the digital world. This surge in malware has emerged as a critical challenge in the realm of…

Encrypted behavioral patterns provide a unique avenue for classifying complex digital threats without reliance on explicit feature extraction, enabling detection frameworks to remain effective even when conventional static and behavioral…

In the age of the Internet, people's lives are increasingly dependent on today's network technology. Maintaining network integrity and protecting the legitimate interests of users is at the heart of network construction. Threat detection is…

In recent years, ransomware has been one of the most notorious malware targeting end users, governments, and business organizations. It has become a very profitable business for cybercriminals with revenues of millions of dollars, and a…

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by…

Malware writers frequently try to hide the activities of their agents within tunnelled traffic. Within the Kill Chain model the infection time is often measured in seconds, and if the infection is not detected and blocked, the malware…

Malware is one of the most common and severe cyber-attack today. Malware infects millions of devices and can perform several malicious activities including mining sensitive data, encrypting data, crippling system performance, and many more.…

In a context of malicious software detection, machine learning (ML) is widely used to generalize to new malware. However, it has been demonstrated that ML models can be fooled or may have generalization problems on malware that has never…

Machine-learning methods have already been exploited as useful tools for detecting malicious executable files. They leverage data retrieved from malware samples, such as header fields, instruction sequences, or even raw bytes, to learn…

A serious threat today is malicious executables. It is designed to damage computer system and some of them spread over network without the knowledge of the owner using the system. Two approaches have been derived for it i.e. Signature Based…

The increasing number of sophisticated malware poses a major cybersecurity threat. Portable executable (PE) files are a common vector for such malware. In this work we review and evaluate machine learning-based PE malware detection…