Modern networks are increasingly targeted by sophisticated cyber threats, making effective intrusion detection a critical challenge. Traditional intrusion detection systems (IDSs) often struggle with capturing the complex relationships within network traffic and suffer from a lack of explainability in their decision-making processes. To address these limitations, we propose X-GANet, Explainable Graph Anomaly Network, a novel graph-based intrusion detection framework that models network traffic as a graph and leverages deep representation learning for precise threat identification. The model extracts both flow-based and structural features, aligns multi-view representations through contrastive learning, and employs a transformer-based embedding module for enhanced feature extraction. An adaptive graph detection fusion mechanism ensures effective graph-level representation and detection techniques for robust attack classification. Experimental results obtained from benchmark intrusion detection datasets demonstrate that X-GANet significantly improves detection performance while maintaining interpretability, making it a promising solution for real-world cybersecurity applications.

In recent years, significant research has been directed towards the taxonomy of malware variants. Nevertheless, certain challenges persist, including the inadequate accuracy of sample classification within similar malware families, elevated false-negative rates, and significant processing time and resource consumption. Malware developers have effectively evaded signature-based detection methods. The predominant static analysis methodologies employ algorithms to convert the files. The analytic process is contingent upon the tool’s functionality; if the tool malfunctions, the entire process is obstructed. Most dynamic analysis methods necessitate the execution of a binary file within a sandboxed environment to examine its behavior. When executed within a virtual environment, the detrimental actions of the file might be easily concealed. This research examined a novel method for depicting malware as images. Subsequently, we trained a classifier to categorize new malware files into their respective classifications utilizing established neural network methodologies for detecting malware images. Through the process of transforming the file into an image representation, we have made our analytical procedure independent of any software, and it has also become more effective. To counter such adversaries, we employ a recognized technique called involution to extract location-specific and channel-agnostic features of malware data, utilizing a deep residual block. The proposed approach achieved remarkable accuracy of 99.5%, representing an absolute improvement of 95.65% over the equal probability benchmark.

Malware classification is a crucial step in defending against potential malware attacks. Despite the significance of a robust malware classifier, existing approaches reveal notable limitations in achieving high performance in malware classification. This study focuses on image-based malware detection, where malware binaries are transformed into visual representations to leverage image classification techniques. We propose a two-branch deep network designed to capture salient features from these malware images. The proposed network integrates faster asymmetric spatial attention to refine the extracted features of its backbone. Additionally, it incorporates an auxiliary feature branch to learn missing information about malware images. The feasibility of the proposed method has been thoroughly examined and compared with state-of-the-art deep learning-based classification methods. The experimental results demonstrate that the proposed method can surpass its counterparts across various evaluation metrics.