Deep learning based vulnerability detection: Are we there yet?
Automated detection of software vulnerabilities is a fundamental problem in software
security. Existing program analysis techniques either suffer from high false positives or false …
security. Existing program analysis techniques either suffer from high false positives or false …
[HTML][HTML] Federated learning for 6G-enabled secure communication systems: a comprehensive survey
Abstract Machine learning (ML) and Deep learning (DL) models are popular in many areas,
from business, medicine, industries, healthcare, transportation, smart cities, and many more …
from business, medicine, industries, healthcare, transportation, smart cities, and many more …
Droidcat: Effective android malware detection and categorization via app-level profiling
Most existing Android malware detection and categorization techniques are static
approaches, which suffer from evasion attacks, such as obfuscation. By analyzing program …
approaches, which suffer from evasion attacks, such as obfuscation. By analyzing program …
{TESSERACT}: Eliminating experimental bias in malware classification across space and time
Is Android malware classification a solved problem? Published F1 scores of up to 0.99
appear to leave very little room for improvement. In this paper, we argue that results are …
appear to leave very little room for improvement. In this paper, we argue that results are …
DTMIC: Deep transfer learning for malware image classification
In the ever-changing cyber threat landscape, evolving malware threats demand a new
technique for their detection. This paper puts forward a strategy for distinguishing malware …
technique for their detection. This paper puts forward a strategy for distinguishing malware …
A survey of malware detection in Android apps: Recommendations and perspectives for future research
Android has dominated the smartphone market and has become the most popular operating
system for mobile devices. However, security threats in Android applications have also …
system for mobile devices. However, security threats in Android applications have also …
A survey of android application and malware hardening
In the age of increasing mobile and smart connectivity, malware poses an ever evolving
threat to individuals, societies and nations. Anti-malware companies are often the first and …
threat to individuals, societies and nations. Anti-malware companies are often the first and …
[HTML][HTML] Deep feature extraction and classification of android malware images
The Android operating system has gained popularity and evolved rapidly since the previous
decade. Traditional approaches such as static and dynamic malware identification …
decade. Traditional approaches such as static and dynamic malware identification …
Android malware detection through hybrid features fusion and ensemble classifiers: The AndroPyTool framework and the OmniDroid dataset
Cybersecurity has become a major concern for society, mainly motivated by the increasing
number of cyber attacks and the wide range of targeted objectives. Due to the popularity of …
number of cyber attacks and the wide range of targeted objectives. Due to the popularity of …
AMalNet: A deep learning framework based on graph convolutional networks for malware detection
X Pei, L Yu, S Tian - Computers & Security, 2020 - Elsevier
The increasing popularity of Android apps attracted widespread attention from malware
authors. Traditional malware detection systems suffer from some shortcomings; …
authors. Traditional malware detection systems suffer from some shortcomings; …