Adversarial training methods for deep learning: A systematic review
Deep neural networks are exposed to the risk of adversarial attacks via the fast gradient sign
method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms …
method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms …
Adversarial machine learning: A multilayer review of the state-of-the-art and challenges for wireless and mobile systems
Machine Learning (ML) models are susceptible to adversarial samples that appear as
normal samples but have some imperceptible noise added to them with the intention of …
normal samples but have some imperceptible noise added to them with the intention of …
Deep learning for android malware defenses: a systematic literature review
Malicious applications (particularly those targeting the Android platform) pose a serious
threat to developers and end-users. Numerous research efforts have been devoted to …
threat to developers and end-users. Numerous research efforts have been devoted to …
Robust malware defense in industrial IoT applications using machine learning with selective adversarial samples
Industrial Internet of Things (IIoT) deploys edge devices to act as intermediaries between
sensors and actuators and application servers or cloud services. Machine learning models …
sensors and actuators and application servers or cloud services. Machine learning models …
[HTML][HTML] Robust malware detection models: learning from adversarial attacks and defenses
The last decade witnessed an exponential growth of smartphones and their users, which
has drawn massive attention from malware designers. The current malware detection …
has drawn massive attention from malware designers. The current malware detection …
Malware detection in edge devices with fuzzy oversampling and dynamic class weighting
Abstract In Internet-of-things (IoT) domain, edge devices are used increasingly for data
accumulation, preprocessing, and analytics. Intelligent integration of edge devices with …
accumulation, preprocessing, and analytics. Intelligent integration of edge devices with …
A study on adversarial sample resistance and Defense Mechanism for Multimodal Learning-based phishing website detection
Recent advancements in Artificial Intelligence (AI) have greatly impacted cybersecurity,
particularly in detecting phishing websites. Traditional methods struggle to address evolving …
particularly in detecting phishing websites. Traditional methods struggle to address evolving …
Androidgyny: Reviewing clustering techniques for Android malware family classification
Thousands of malicious applications (apps) are created daily, modified with the aid of
automation tools, and released on the World Wide Web. Several techniques have been …
automation tools, and released on the World Wide Web. Several techniques have been …
When the guard failed the droid: A case study of android malware
Android malware is a persistent threat to billions of users around the world. As a
countermeasure, Android malware detection systems are occasionally implemented …
countermeasure, Android malware detection systems are occasionally implemented …
An Efficient Feature Extraction Method For Static Malware Analysis Using PE Header Files
O Hossain, ST Dhruba, F Jalal - 2023 - 103.82.172.44
Detecting malware is crucial for safeguarding various devices, ranging from per sonal
computers to large-scale systems, because computer systems continue to face serious …
computers to large-scale systems, because computer systems continue to face serious …