Results 71 to 80 of about 2,901,102 (167)
Alexandria Engineering JournalWith the fast growth of mobile phone usage, malicious threats against Android mobile devices are enhanced. The Android system utilizes a wide range of sensitive apps like banking apps; thus, it develops the aim of malware that uses the vulnerability of ...
Shoayee Dlaim Alotaibi +7 more
doaj +1 more source
Cipher‐Guard: A Machine Learning Model for Adaptive and Context‐Aware Password Security
IET Information Security, Volume 2026, Issue 1, 2026.This research aims to enhance password security by designing, training and testing Cipher‐Guard, a machine learning (ML) algorithm that incorporates complex features and techniques derived from proven feature engineering. The current model is inherently built on the equations of mathematical modelling concerning complexity metrics of passwords and ...
Mohammed Naif Alatawi, Xueqin Liang
wiley +1 more source
Devious chatbots - interactive malware with a plot [PDF]
, 2009 Many social robots in the forms of conversation agents or Chatbots have been put to practical use in recent years. Their typical roles are online help or acting as a cyber agent representing an organisation.
Wong, K.W., Fung, C.C., Pan, J.Y.
core
Contaminant removal for Android malware detection systems [PDF]
2017 IEEE International Conference on Big Data (Big Data), 2017 2017 IEEE International Conference on Big ...
Lichao Sun 0001 +5 more
openaire +4 more sources
AI‐Powered Defense: Leveraging Deep Learning for Effective Malware Detection
Applied Computational Intelligence and Soft Computing, Volume 2026, Issue 1, 2026.Traditional malware detection techniques frequently fail to detect and stop malicious activity in an era where cyber threats are becoming more complex. Any software that enters a computer system without the administrator’s consent is considered malicious software.
Nancy Awadallah Awad +1 more
wiley +1 more source
Federated Adversarial Selective Vision Transformers for Visual‐Based Malware Classification
Applied Computational Intelligence and Soft Computing, Volume 2026, Issue 1, 2026.Malware detection remains challenging because of privacy constraints, heterogeneous data distributions, and vulnerability to adversarial attacks in distributed environments. We proposed a federated adversarial learning framework for robust, privacy‐preserving malware classification.
Mohamad Mulham Belal +4 more
wiley +1 more source
Emulation vs Instrumentation for Android Malware Detection [PDF]
, 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
Robust AI‐SCORE Framework: Independent and Adversarial Validation for Malware Detection
Journal of Engineering, Volume 2026, Issue 1, 2026.Traditional malware detection methods such as signature‐based approaches and statistical analysis are becoming less effective in detecting the new breed of malware, which is holding high levels of complexity in terms of the number of code versions, compilation patterns, time to live (TTL), and jumping through evasion techniques.
Hafiz Talha Arif Zuberi +7 more
wiley +1 more source
Comment on "AndrODet: An adaptive Android obfuscation detector"
, 2020 We have identified a methodological problem in the empirical evaluation of the string encryption detection capabilities of the AndrODet system described by Mirzaei et al. in the recent paper "AndrODet: An adaptive Android obfuscation detector".
Mohammadinodooshan, Alireza, +2 more
core
A Comprehensive Study of Malware Detection in Android Operating Systems
, 2021 Android is now the world\u27s (or one of the world’s) most popular operating system. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available.
Ahmed, Omar M. +9 more
core +1 more source