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 exponential growth of cyber threats and software flaws, automated detection approaches that can recognize malicious code patterns in dynamic environments are required. Signature based and primitive machine learning theory inability to generalize when presented with new or unseen vulnerabilities is reflected in the...
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 growing number of attacks on web applications and the increasing volume of HTTP traffic strengthen the requirements for automatic malware detection systems. 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...
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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