Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques

Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques

Banking malware poses a significant threat to users by infecting their computers and then attempting to perform malicious activities such as surreptitiously stealing confidential information from them. Banking malware variants are also continuing to evolve and have been increasing in numbers for man...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohamed Ali Kazi
Format:	Article
Language:	English
Published:	MDPI AG 2025-01-01
Series:	Journal of Cybersecurity and Privacy
Subjects:	banking malware Zeus malware variants machine learning binary classification algorithms automated feature selection manual feature selection
Online Access:	https://www.mdpi.com/2624-800X/5/1/4
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Method of anti-confusion texture feature descriptor for malware images
by: Yashu LIU, et al.
Published: (2018-11-01)

Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges
by: Harikha Manthena, et al.
Published: (2025-01-01)

Android Malware Category and Family Identification Using Parallel Machine Learning
by: Ahmed Hashem El Fiky, et al.
Published: (2022-07-01)

A novel feature selection technique: Detection and classification of Android malware
by: Sandeep Sharma, et al.
Published: (2025-03-01)

Integrating Information Gain and Chi-Square for Enhanced Malware Detection Performance
by: Fauzi Adi Rafrastara, et al.
Published: (2025-01-01)

Apk2Audio4AndMal: Audio Based Malware Family Detection Framework
by: Oguz Emre Kural, et al.
Published: (2023-01-01)

Application of deep learning in malware detection: a review
by: Yafei Song, et al.
Published: (2025-04-01)

A multi-tiered feature selection model for android malware detection based on Feature discrimination and Information Gain
by: Parnika Bhat, et al.
Published: (2022-11-01)

Two stage malware detection model in internet of vehicles (IoV) using deep learning-based explainable artificial intelligence with optimization algorithms
by: Manal Abdullah Alohali, et al.
Published: (2025-07-01)

Android malware detection method based on deep neural network
by: Fan CHAO, et al.
Published: (2020-10-01)

Machine learning techniques for imbalanced multiclass malware classification through adaptive feature selection
by: Binayak Panda, et al.
Published: (2025-03-01)

GCSA-ResNet: a deep neural network architecture for Malware detection
by: Yukang Fan, et al.
Published: (2025-07-01)

Systematic Approach for Malware Detection in IoT Devices: Enhancing Security and Performance
by: Vasudeva Pai, et al.
Published: (2025-07-01)

A Review of the Recent Trends in Mobile Malware Evolution, Detection, and Analysis
by: Seetah Almarri, et al.
Published: (2025-01-01)

CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES
by: abdullah alsraratee, et al.
Published: (2025-04-01)

MALVADA: A framework for generating datasets of malware execution traces
by: Razvan Raducu, et al.
Published: (2025-05-01)

Study on modern malware analysis system
by: Yi ZHAO, et al.
Published: (2014-10-01)

Study on modern malware analysis system
by: Yi ZHAO, et al.
Published: (2014-10-01)

Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques
by: Yadigar Imamverdiyev, et al.
Published: (2025-01-01)

Online analytical model of massive malware based on feature clusting
by: Xiao-lin XU, et al.
Published: (2013-08-01)

Enhancing the Sustainability of Machine Learning-Based Malware Detection Techniques for Android Applications
by: Seyeon Park, et al.
Published: (2025-01-01)

HoneyBow:an automated malware collection tool based on the high-interaction honeypot principle
by: ZHUGE Jian-wei1, et al.
Published: (2007-01-01)

I-MCM: IoT Malware Counter Measures for Cross-Architecture IoT Malware Detection
by: Ibrahim Gulatas, et al.
Published: (2025-01-01)

A Survey on Android Malware Detection Techniques Using Supervised Machine Learning
by: Safa J. Altaha, et al.
Published: (2024-01-01)

FLSH: A Framework Leveraging Similarity Hashing for Android Malware and Variant Detection
by: Hassan Jalil Hadi, et al.
Published: (2025-01-01)

AOAFS: A Malware Detection System Using an Improved Arithmetic Optimization Algorithm
by: Rafat Alshorman, et al.
Published: (2025-04-01)

A Robust and Efficient Machine Learning Framework for Enhancing Early Detection of Android Malware
by: Fandi Kurniawan, et al.
Published: (2025-01-01)

A Survey on Adversarial Attacks for Malware Analysis
by: Kshitiz Aryal, et al.
Published: (2025-01-01)

Malware Detection and Classification in Android Application Using Simhash-Based Feature Extraction and Machine Learning
by: Wafaa Al-Kahla, et al.
Published: (2024-01-01)

A Review of State-of-the-Art Malware Attack Trends and Defense Mechanisms
by: Jannatul Ferdous, et al.
Published: (2023-01-01)

Systematic Review: Malware Detection and Classification in Cybersecurity
by: Sebastian Berrios, et al.
Published: (2025-07-01)

Hybrid Android Malware Detection and Classification Using Deep Neural Networks
by: Muhammad Umar Rashid, et al.
Published: (2025-03-01)

Malware-SeqGuard: An Approach Utilizing LSTM and GRU for Effective Detection of Evolving Malware in Android Environments
by: Muhammad Usama Tanveer, et al.
Published: (2025-01-01)

Review of malware detection and classification visualization techniques
by: Jinwei WANG, et al.
Published: (2023-10-01)

ISAnWin: inductive generalized zero-shot learning using deep CNN for malware detection across windows and android platforms
by: Umm-e-Hani Tayyab, et al.
Published: (2024-12-01)

An extrinsic random-based ensemble approach for android malware detection
by: Nektaria Potha, et al.
Published: (2021-10-01)

A Deep Learning-Based Ensemble Framework for Robust Android Malware Detection
by: Sainag Nethala, et al.
Published: (2025-01-01)

Android malware detection based on improved random forest
by: Hong-yu YANG, et al.
Published: (2017-04-01)

MalGEA: A malware analysis framework via matrix factorization based node embedding and graph external attention
by: Ruisheng Li, et al.
Published: (2025-09-01)

An Adversarial Attack on ML-Based IoT Malware Detection Using Binary Diversification Techniques
by: Maina Bernard Mwangi, et al.
Published: (2024-01-01)