TL;DR
The machine-learning model proposed in the paper has improved performance (efficiency, accuracy, and false-negative rate) for identifying vulnerabilities compared to conventional methods, and shows much greater flexibility and reliability when analyzing large code bases and different categories of vulnerabilities.
The results show that the ensemble techniques are a practical approach to boost the precision of LLMs in the detection of vulnerabilities and suggest that ensemble methods offer great potential in the advancement of software security analysis.
The results indicate that the hybrid RNN–BiLSTM architecture can be used to efficiently perform proactive vulnerability detection and is also applicable to enable real-time cybersecurity monitoring and the use of automatically possible counter efforts in contemporary software systems.
Malware is a serious threat in the cybersecurity area because of its dynamic nature, the variety of malware families, stealth, propagation and the capability of evading traditional security products. Therefore, proper malware detection and classification are crucial for detecting malicious software and for securing com...
Malware family classification is essential for understanding malicious software behaviour and supporting cybersecurity analysis. Existing machine learning approaches have demonstrated promising classification performance; however, many rely on high-dimensional feature sets that increase computational complexity and inc...
The aim of the research is to develop a technique for detecting attacks on web applications based on a cascade approach, which allows combining a quick initial analysis of incoming requests with a more detailed classification of potentially dangerous traffic.
This article proposes an advanced method for network intrusion detection using a combination of recurrent neural networks (RNNs), specifically long short-term memory (LSTM), gated recurrent units (GRU), and bidirectional long short-term memory (BiLSTM) models, enhanced by synthetic minority oversampling technique (SMOT...
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