Crypto-preserving investigation framework for deep learning based malware attack detection for network forensics
S Bhardwaj, M Dave - Wireless Personal Communications, 2022 - Springer
The exponential growth in technology observed over the past decade has introduced newer
ways to exploit network and cyber-physical system-related vulnerabilities. Cybercriminals …
ways to exploit network and cyber-physical system-related vulnerabilities. Cybercriminals …
Malware analysis using machine learning and deep learning techniques
R Patil, W Deng - 2020 SoutheastCon, 2020 - ieeexplore.ieee.org
In this era, where the volume and diversity of malware is rising exponentially, new
techniques need to be employed for faster and accurate identification of the malwares …
techniques need to be employed for faster and accurate identification of the malwares …
Visualization of Malwares for Classification Through Deep Learning
H Kim, S Han, S Lee, JR Lee - Journal of Internet Computing and …, 2018 - koreascience.kr
Abstract According to Symantec's Internet Security Threat Report (2018), Internet security
threats such as Cryptojackings, Ransomwares, and Mobile malwares are rapidly increasing …
threats such as Cryptojackings, Ransomwares, and Mobile malwares are rapidly increasing …
Feature mining for encrypted malicious traffic detection with deep learning and other machine learning algorithms
The popularity of encryption mechanisms poses a great challenge to malicious traffic
detection. The reason is traditional detection techniques cannot work without the decryption …
detection. The reason is traditional detection techniques cannot work without the decryption …
Machine learning approaches in cybersecurity
MNR Khan, J Ara, S Yesmin, MZ Abedin - Data Intelligence and Cognitive …, 2022 - Springer
Abstract Machine Learning is one of the effective responses to trivial attacks, starting with the
Internet Protocol traffic classification and the filtering of mistreatment traffic for intrusion …
Internet Protocol traffic classification and the filtering of mistreatment traffic for intrusion …
Deep learning approach for intelligent intrusion detection system
R Vinayakumar, M Alazab, KP Soman… - Ieee …, 2019 - ieeexplore.ieee.org
Machine learning techniques are being widely used to develop an intrusion detection
system (IDS) for detecting and classifying cyberattacks at the network-level and the host …
system (IDS) for detecting and classifying cyberattacks at the network-level and the host …
A survey of the recent trends in deep learning based malware detection
Monitoring Indicators of Compromise (IOC) leads to malware detection for identifying
malicious activity. Malicious activities potentially lead to a system breach or data …
malicious activity. Malicious activities potentially lead to a system breach or data …
Deep learning-based malware classification methodology of comprehensive study
The increasing connectivity of devices and systems in today's world has led to a significant
increase in the spread of malware. The rapid advancements in technology have made it …
increase in the spread of malware. The rapid advancements in technology have made it …
Cybersecurity deep: approaches, attacks dataset, and comparative study
Cyber attacks are increasing rapidly due to advanced digital technologies used by hackers.
In addition, cybercriminals are conducting cyber attacks, making cyber security a rapidly …
In addition, cybercriminals are conducting cyber attacks, making cyber security a rapidly …
Cybersecurity threats and their mitigation approaches using Machine Learning—A Review
Machine learning is of rising importance in cybersecurity. The primary objective of applying
machine learning in cybersecurity is to make the process of malware detection more …
machine learning in cybersecurity is to make the process of malware detection more …