AI in Cybersecurity: Enhancing Digital Defenses with Anomaly Detection and LSTM-Based Threat Prediction
Keywords:
Cybersecurity, Anomaly Detection, Long Short-term Memory - Variational Auto-Encoder, Threat Prediction, AI-Based Security, Network Traffic AnalysisAbstract
These are the key future areas of research in Applied Artificial Intelligence. In this era of increasingly fast, internet-connected systems, traditional defenses have become inefficient at detecting and responding to various types of sophisticated threats such as malware, phishing, ransomware, distributed denial of service attacks, and more. Such conventional security approaches are not effective with zero-day attacks or new-age evolving attacks. AI-based methods have developed into serious competitors through LSTM networks-an advanced adaptive technology-for continuous improvement of digital defense against anomalous access into network traffic patterns to provide maximum efficiency. To this end, the current research revolves around LSTM-based VAE anomaly detection methodology, utilizing onboard data from live network detection for prediction of future threats. The methodology involves necessary steps that include data pre-processing of Z-score normalization, encoding network traffic into latent representations and then reconstructing the pattern to realize anomaly detection through reconstruction loss and KL divergence. LSTM-based classifier with SoftMax activation is used for threat classification. Testing results indicated improvement in detection accuracy over 90% after 20 epochs of training. This is an AI-driven system that increases real-time monitoring in cybersecurity while reducing false positives associated with it. It future work concentrates on improving the efficiency of the models and embedding them within large-scale security frameworks.
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References
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