Deep reinforcement learning in the advanced cybersecurity threat detection and protection
The cybersecurity threat landscape has lately become overly complex. Threat actors
leverage weaknesses in the network and endpoint security in a very coordinated manner to …
leverage weaknesses in the network and endpoint security in a very coordinated manner to …
Deep reinforcement learning for cybersecurity threat detection and protection: A review
The cybersecurity threat landscape has lately become overly complex. Threat actors
leverage weaknesses in the network and endpoint security in a very coordinated manner to …
leverage weaknesses in the network and endpoint security in a very coordinated manner to …
[PDF][PDF] Two-stage hybrid malware detection using deep learning
With the increasing number and variety of Internet of Things (IoT) devices supporting a wide
range of services such as smart homes, smart transportation, and smart factories in smart …
range of services such as smart homes, smart transportation, and smart factories in smart …
Yamme: a yara-byte-signatures metamorphic mutation engine
Recognition of known malicious patterns through signature-based systems is unsuccessful
against malware for which no known signature exists to identify them. These include not only …
against malware for which no known signature exists to identify them. These include not only …
Android malware detection methods based on convolutional neural network: A survey
Android malware detection (AMD) is a challenging task requiring many factors to be
considered during detection, such as feature extraction and processing, performance …
considered during detection, such as feature extraction and processing, performance …
[HTML][HTML] Adversarial superiority in android malware detection: Lessons from reinforcement learning based evasion attacks and defenses
Today, android smartphones are being used by billions of users and thus have become a
lucrative target of malware designers. Therefore being one step ahead in this zero-sum …
lucrative target of malware designers. Therefore being one step ahead in this zero-sum …
A Novel Feature Encoding Scheme for Machine Learning Based Malware Detection Systems.
V Das, BB Nair, R Thiruvengadathan - IEEE Access, 2024 - ieeexplore.ieee.org
Malware detection is an ever-evolving area given that the strides in the detection capabilities
being matched by radical attempts to bypass the detection. As the sophistication of malware …
being matched by radical attempts to bypass the detection. As the sophistication of malware …
Android malware detection based on static analysis and data mining techniques: A systematic literature review
Android applications are proliferating, which has led to the rise of android malware. Many
research studies have proposed various detection frameworks for android malware …
research studies have proposed various detection frameworks for android malware …
Deep counterstrike: Counter adversarial deep reinforcement learning for defense against metamorphic ransomware swarm attack
Ransomware, create a devastating impact when it infects a system. Fortunately, post the
initial breach, such ransomware could be detected using advanced machine learning …
initial breach, such ransomware could be detected using advanced machine learning …
Are Malware Detection Classifiers Adversarially Vulnerable to Actor-Critic based Evasion Attacks?
Android devices like smartphones and tablets have become immensely popular and are an
integral part of our daily lives. However, it has also attracted malware developers to design …
integral part of our daily lives. However, it has also attracted malware developers to design …