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Android Malware Detection Systems Review

Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 2017
With the smartphones entering our lives, the number of smartphones continues to increase day by day. The reason why smartphones are in so demand is that people can easily do what they want.
Ömer Kiraz, İbrahim Alper Doğru
doaj  

Embedding-Driven Synthetic Malware Generation with Autoencoders and Cluster-Tangent Diffusion

Applied Sciences
Malware has become increasingly sophisticated over the years, with zero-day attacks emerging at an alarming pace. Effective detection and analysis demand real malware samples, which are expensive and skill-dependent to extract.
Gunnika Kapoor, Sathvika Nadipalli, Fabio Di Troia   +2 more
doaj   +1 more source

An Intelligent Spam Detection Model Based on Artificial Immune System

Information, 2019
Spam emails, also known as non-self, are unsolicited commercial or malicious emails, sent to affect either a single individual or a corporation or a group of people. Besides advertising, these may contain links to phishing or malware hosting websites set
Abdul Jabbar Saleh, Asif Karim, Bharanidharan Shanmugam, Sami Azam, Krishnan Kannoorpatti, Mirjam Jonkman, Friso De Boer   +6 more
doaj   +1 more source

A BERT and PSO framework for Android malware detection using real permissions and API calls

Discover Computing
The rapid expansion of mobile connectivity and the global reliance on smartphones have positioned Android as the leading platform, driven by its affordability and open source framework.
Abhinandan Banik, Jyoti Prakash Singh
doaj   +1 more source

Adversarial Robustness of Deep Learning-Based Malware Detectors via (De)Randomized Smoothing

IEEE Access
Deep learning-based malware detectors have been shown to be susceptible to adversarial malware examples, i.e. malware examples that have been deliberately manipulated in order to avoid detection.
Daniel Gibert, Giulio Zizzo, Quan Le, Jordi Planes   +3 more
doaj   +1 more source

Binary Code Analysis for Cybersecurity: A Systematic Review of Forensic Techniques in Vulnerability Detection and Anti-Evasion Strategies

IEEE Access
Binary code analysis is essential in modern cybersecurity, examining compiled program outputs to identify vulnerabilities, detect malware, and ensure software security compliance.
Haseeb Javed, Farman Ali, Babar Shah, Daehan Kwak   +3 more
doaj   +1 more source

OPTISTACK: A Hybrid Ensemble Learning and XAI-Based Approach for Malware Detection in Compressed Files

IEEE Access
The increasing reliance on compressed file formats for data storage and transmission has made them attractive vectors for malware propagation, as their structural complexity enables evasion of conventional detection mechanisms.
Khaled Mahmud Sujon, Rohayanti Binti Hassan, M. Abdullah-Al-Wadud, Jia Uddin   +3 more
doaj   +1 more source

Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection

Future Internet
During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems.
Muhammad Imran, Annalisa Appice, Donato Malerba   +2 more
doaj   +1 more source

Lightweight DDoS Attack Detection Using Bayesian Space-Time Correlation

IEEE Access
DDoS attacks are still one of the primary sources of problems on the Internet and continue to cause significant financial losses for organizations. To mitigate their impact, detection should preferably occur close to the attack origin, e.g., at home ...
Gabriel Mendonca, Rosa M. M. Leao, Edmundo De Souza E. Silva, Don Towsley   +3 more
doaj   +1 more source

Peer Review #2 of "Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) (v0.3)"

, 2021
  +5 more sources