[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation

AA Khan, O Chaudhari, R Chandra - Expert Systems with Applications, 2024‏ - Elsevier
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

[HTML][HTML] The impact of artificial intelligence on organisational cyber security: An outcome of a systematic literature review

I Jada, TO Mayayise - Data and Information Management, 2024‏ - Elsevier
As digital transformation continues to advance, organisations are becoming increasingly
aware of the benefits that modern technologies offer. However, with greater technology …

[HTML][HTML] IDS-INT: Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

F Ullah, S Ullah, G Srivastava, JCW Lin - Digital Communications and …, 2024‏ - Elsevier
A network intrusion detection system is critical for cyber security against illegitimate attacks.
In terms of feature perspectives, network traffic may include a variety of elements such as …

CNN-LSTM and transfer learning models for malware classification based on opcodes and API calls

A Bensaoud, J Kalita - Knowledge-Based Systems, 2024‏ - Elsevier
In this paper, we propose a novel model for a malware classification system based on
Application Programming Interface (API) calls and opcodes, to improve classification …

GA-StackingMD: Android malware detection method based on genetic algorithm optimized stacking

N **e, Z Qin, X Di - Applied Sciences, 2023‏ - mdpi.com
With the rapid development of network and mobile communication, intelligent terminals such
as smartphones and tablet computers have changed people's daily life and work. However …

Optimized and efficient image-based IoT malware detection method

A El-Ghamry, T Gaber, KK Mohammed, AE Hassanien - Electronics, 2023‏ - mdpi.com
With the widespread use of IoT applications, malware has become a difficult and
sophisticated threat. Without robust security measures, a massive volume of confidential and …

Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review

NHA Mutalib, AQM Sabri, AWA Wahab… - Artificial Intelligence …, 2024‏ - Springer
Abstract In recent years, Advanced Persistent Threat (APT) attacks on network systems have
increased through sophisticated fraud tactics. Traditional Intrusion Detection Systems (IDSs) …

Enhanced network intrusion detection system for internet of things security using multimodal big data representation with transfer learning and game theory

F Ullah, A Turab, S Ullah, D Cacciagrano, Y Zhao - Sensors, 2024‏ - mdpi.com
Internet of Things (IoT) applications and resources are highly vulnerable to flood attacks,
including Distributed Denial of Service (DDoS) attacks. These attacks overwhelm the …

A hybrid approach for Android malware detection using improved multi-scale convolutional neural networks and residual networks

X Fu, C Jiang, C Li, J Li, X Zhu, F Li - Expert Systems with Applications, 2024‏ - Elsevier
The open-source nature of Android, along with its coarse-grained permission management
and widespread use, has heightened its vulnerability to malware threats. However, many …

[HTML][HTML] Enhancing android malware detection explainability through function call graph APIs

D Soi, A Sanna, D Maiorca, G Giacinto - Journal of Information Security and …, 2024‏ - Elsevier
Nowadays, mobile devices are massively used in everyday activities. Thus, they contain
sensitive data targeted by threat actors like bank accounts and personal information …