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Deep learning (DL) has proven to be effective in detecting sophisticated malware that is constantly evolving. Even though deep learning has alleviated the feature engineering problem, finding the most optimal DL model, in terms of neural…
Robust network security systems are essential to prevent and mitigate the harming effects of the ever-growing occurrence of network attacks. In recent years, machine learning-based systems have gain popularity for network security…
Machine learning based malware detection techniques rely on grayscale images of malware and tends to classify malware based on the distribution of textures in graycale images. Albeit the advancement and promising results shown by machine…
As computing systems become increasingly advanced and as users increasingly engage themselves in technology, security has never been a greater concern. In malware detection, static analysis, the method of analyzing potentially malicious…
Malware is a piece of software that was written with the intent of doing harm to data, devices, or people. Since a number of new malware variants can be generated by reusing codes, malware attacks can be easily launched and thus become…
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…
As cyber attacks continue to increase in frequency and sophistication, detecting malware has become a critical task for maintaining the security of computer systems. Traditional signature-based methods of malware detection have limitations…
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.…
Malware detection in Android systems requires both cybersecurity expertise and machine learning (ML) techniques. Automated Machine Learning (AutoML) has emerged as an approach to simplify ML development by reducing the need for specialized…
The current pandemic situation has increased cyber-attacks drastically worldwide. The attackers are using malware like trojans, spyware, rootkits, worms, ransomware heavily. Ransomware is the most notorious malware, yet we did not have any…
Malware for Android is becoming increasingly dangerous to the safety of mobile devices and the data they hold. Although machine learning(ML) techniques have been shown to be effective at detecting malware for Android, a comprehensive…
Analysing malware is important to understand how malicious software works and to develop appropriate detection and prevention methods. Dynamic analysis can overcome evasion techniques commonly used to bypass static analysis and provide…
It is well-known that malware constantly evolves so as to evade detection and this causes the entire malware population to be non-stationary. Contrary to this fact, prior works on machine learning based Android malware detection have…
Digital systems find it challenging to keep up with cybersecurity threats. The daily emergence of more than 560,000 new malware strains poses significant hazards to the digital ecosystem. The traditional malware detection methods fail to…
In today's interconnected digital landscape, the proliferation of malware poses a significant threat to the security and stability of computer networks and systems worldwide. As the complexity of malicious tactics, techniques, and…
The continued evolution and diversity of malware constitutes a major threat in modern systems. It is well proven that security defenses currently available are ineffective to mitigate the skills and imagination of cyber-criminals…
Malware has become a widely used means in cyber attacks in recent decades because of various new obfuscation techniques used by malwares. In order to protect the systems, data and information, detection of malware is needed as early as…
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…
Identifying the tasks a given piece of malware was designed to perform (e.g. logging keystrokes, recording video, establishing remote access, etc.) is a difficult and time-consuming operation that is largely human-driven in practice. In…
In an era of escalating cyber threats, malware poses significant risks to individuals and organizations, potentially leading to data breaches, system failures, and substantial financial losses. This study addresses the urgent need for…