Results 91 to 100 of about 2,904 (224)

Emulation vs Instrumentation for Android Malware Detection

, 2019
In resource constrained devices, malware detection is typically based on offline analysis using emulation. In previous work it has been claimed that such emulation fails for a significant percentage of Android malware because well-designed malware ...
Sinha, Anukriti
core   +1 more source

A Systematic Review of Sensor Vulnerabilities and Cyber‐Physical Threats in Industrial Robotic Systems

IET Cyber-Physical Systems: Theory &Applications, Volume 10, Issue 1, January/December 2025.
This paper addresses a critical gap in the literature of industrial robotics cybersecurity by presenting a comprehensive analysis of vulnerabilities in the sensing systems of industrial robots. In particular, we systematically explore how sensor performance limits, faults and biases can be exploited by attackers who can then turn these inherent ...
Abdul Kareem Shaik, Alireza Mohammadi, Hafiz Malik   +2 more
wiley   +1 more source

A Malware Detection System For Android

, 2015
Android security is built upon a permission-based mechanism, which restricts access of third-party Android applications to critical resources on an Android device.
Tchakounté, Franklin
core  

Analysis of Bayesian classification-based approaches for Android malware detection

, 2014
Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform.
McWilliams, Gavin, Yerima, Suleiman Y., Sezer, Sakir   +2 more
core   +1 more source

Android malware Detection using Machine learning: A Review

, 2023
Malware for Android is becoming increasingly dangerous to the safety of mobile devices and the data they hold. Although machine learning techniques have been shown to be effective at detecting malware for Android, a comprehensive analysis of the methods ...
Hamdy Soliman (15213975), Mohammad Sahinur Hossen (15213980), Md Naseef-Ur-Rahman Chowdhury (14812855), Ahshanul Haque (15199219), Imtiaz Ahmed (15213985), Tanjim Fatima (15213988)   +5 more
core   +1 more source

GNSTAM: Integrating Graph Networks With Spatial and Temporal Signature Analysis for Enhanced Android Malware Detection

IEEE Access
The sophistication of Android malware poses significant threats to user security and privacy. Traditional detection methods struggle with rapid malware evolution and benign application diversity, leading to high false positive rates and limited ...
Yogesh Kumar Sharma, Deepak Singh Tomar, R. K. Pateriya, Surendra Solanki   +3 more
doaj   +1 more source

AEDroid: Adaptive Enhanced Android Malware Detection‐Based on Interpretability of Deep Learning

IET Information Security, Volume 2025, Issue 1, 2025.
As the most widely used operating system in the world, Android has naturally become the main target of malicious hackers. The current research on Android malware detection relies on manually defined sensitive API feature sets. With the continuous innovation and change of malicious behavior, new threats and attack methods have emerged.
Pengfei Liu, Guangquan Xu, Jian Sun, Wenxia Wang, Jie Chen, Peican Zhu   +5 more
wiley   +1 more source

Fighting fire with fire – a Pre-emptive approach to restore control over IT assets from malware infection

, 2012
Malware is a major threat as they induce multiple risks to infected organizations. Current Anti-Malware solutions meant to keep Malware away are challenged on how to keep the risks at bay effectively. When a Malware manages to penetrate an organization’s
Pan, J.Y.
core  

CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES

Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ
Android malware has grown steadily into a major internet threat. Despite efforts to identify and categorize malware in seemingly safe Android apps, addressing this issue is still lacking.
abdullah alsraratee, Ahmed Al-Azawei
doaj   +1 more source

DyBAnd: Dynamic Behavior Based Android Malware Detection

, 2023
Android is the most popular widely accessible smartphone operating system, yet its permission declaration and access control systems cannot detect malicious activities. Advanced malware uses cutting-edge obfuscation techniques to mask its true intentions
Sihag, Vikas, Jaiswal, Shashank, Dragoni, Nicola, Choudhary, Gaurav   +3 more
core   +1 more source