Results 91 to 100 of about 11,847 (201)

Deep Belief Networks-based framework for malware detection in Android systems

Alexandria Engineering Journal, 2018
Malware is the umbrella term that denotes attacking any system by malicious software. During the last few years, the popularity of Android smartphones led to the sneak of several malware applications into different Android markets without any difficulty.
Dina Saif, S.M. El-Gokhy, E. Sallam
doaj   +1 more source

Mobile SDNs: Associating End‐User Commands with Network Flows in Android Devices

IET Communications, Volume 19, Issue 1, January/December 2025.
In our research, we combine user interface context with network flow data to improve network profiling on Android, achieving over 98.5% accuracy. We create “AppJudicator”, an Android access control app using host‐based SDN and default Android APIs, effectively addressing security concerns in enterprise networks.
Shuwen Liu, Craig A. Shue, Joseph P. Petitti, Yunsen Lei, Yu Liu   +4 more
wiley   +1 more source

Towards autonomous device protection using behavioural profiling and generative artificial intelligence

IET Cyber-Physical Systems: Theory &Applications, Volume 10, Issue 1, January/December 2025.
The article proposes a novel concept of autonomous device protection based on behavioural profiling by continuously monitoring internal resource usage and exploiting a large language model to distinguish between benign and malicious behaviour. Abstract Demand for autonomous protection in computing devices cannot go unnoticed, considering the rapid ...
Sandeep Gupta, Bruno Crispo
wiley   +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

OpCode-Level Function Call Graph Based Android Malware Classification Using Deep Learning

Sensors, 2020
Due to the openness of an Android system, many Internet of Things (IoT) devices are running the Android system and Android devices have become a common control terminal for IoT devices because of various sensors on them.
Weina Niu, Rong Cao, Xiaosong Zhang, Kangyi Ding, Kaimeng Zhang, Ting Li   +5 more
doaj   +1 more source

A family of droids -- Android malware detection via behavioral modeling: static vs dynamic analysis [PDF]

, 2019
Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause harm to the device's owner.
Almeida, Mario, Blackburn, Jeremy, Cristofaro, Emiliano De, Mariconti, Enrico, Onwuzurike, Lucky, Stringhini, Gianluca   +5 more
core  

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

Malware Detection in Android Applications

, 2019
Android is a Linux based operating system used for smart phone devices. Since 2008, Android devices gained huge market share due to its open architecture and popularity. Increased popularity of the Android devices and associated primary benefits attracted the malware developers. Rate of Android malware applications increased between 2008 and 2016.
Mr. Tushar Patil, Prof. Bharti Dhote
openaire   +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

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