AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
An ensemble-based approach TriFusion-AnomNet is proposed by fusing predictions from isolation forest, autoencoders, and one-class support vector machine (OCSVM) to improve robustness and a feedback loop to refine and improve AD models based on human reviews and system performance.
This paper introduces an innovative ML-based security paradigm that improves the attack detection accuracy by combining adaptive feature extraction techniques with a context-attentive hybrid mechanism and maximizes detection accuracy and computational efficiency.
A machine learning-based framework to tackle issues in traditional systems in traditional systems is introduced by combining large language models (LLMs) and is effective in identifying possible threats as well as filling the semantic gap.
A hybrid deep learning (DL)-based anomaly detection model is presented for IoT cybersecurity that achieves superior performance in terms of accuracy, precision, recall, and F1-score compared to conventional DL techniques.
The majority of assaults in heterogeneous networks are detected by intrusion detection systems (IDS). Cyberattack kinds that seriously harm networks are difficult for conventional IDSs to detect. The majority of existing solutions rely on deep learning models, which have a significant computational and energy overhead...
The proposed real-time anomaly repair model integrates a repair toolset that uses statistical measurements and machine learning techniques to provide multiple replacement options for anomaly repair, making it well-suited for efficiently handling anomalies in real-time data streams.
The Internet of Things (IoT) has become increasingly integrated into our daily lives, offering a wide range of services through the proliferation of connected devices. While this connectivity enhances convenience and functionality, it also introduces significant security challenges, exposing IoT systems to various form...
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