This paper proposes an improved CNN-LSTM model for IoT intrusion detection, achieving high accuracy by combining spatial and temporal feature learning from network traffic.

This paper proposes and evaluates two lightweight deep learning-based intelligent Intrusion Detection Systems (CNN and LSTM) to enhance the security of large-scale IoT networks, achieving high classif…

The paper proposes a Cognitive Threat Intelligence and Explainable Federated Security Analytics framework to enable privacy-preserving and scalable cyber threat detection across distributed infrastruc…

The paper introduces an end-to-end framework that not only detects network intrusions using deep learning but also generates actionable, citation-grounded mitigation reports using a Retrieval-Augmente…

This paper proposes and evaluates the KAN-LSTM model, demonstrating that Kolmogorov-Arnold Networks (KANs) significantly outperform traditional deep learning models for accurate and parameter-efficien…

This paper proposes an Explainable AI (XAI)-driven framework using XGBoost and SHAP to enhance cyber risk analytics and model reliability for intelligent governance of U.S. critical infrastructure.

This paper evaluates unsupervised temporal learning models, specifically recurrent autoencoders, for real-time anomaly detection in vulnerable IEC-61850 GOOSE networks, demonstrating that the GRU mode…

A hybrid deep learning model combining ResNet-1D, BiGRU, and Multi-Head Attention achieves high accuracy and low latency for robust cyberattack detection in Industrial IoT environments.

The paper proposes an explainable attention-based LSTM framework to achieve early and reliable detection of advanced, AI-assisted ransomware by analyzing file system behavioral sequences.

This paper proposes a comprehensive framework for network intrusion detection using unified multi-modal datasets and evaluates advanced adversarial learning methods for generating high-fidelity synthe…

This paper enhances anomaly detection and threat intelligence in Zero Trust IoT environments by applying and comparing various machine learning classifiers, notably using SMOTE to improve accuracy on…

The paper proposes an SE ViT-BiLSTM hybrid model for enhanced intrusion detection in IIoT and IoMT environments, achieving superior performance on real-world datasets, especially after data balancing.

SentinelSphere is an AI platform that integrates advanced deep learning for real-time threat detection with an LLM-powered training system to holistically address both technical and human-factor cyber…

This paper enhances an existing autonomous online Intrusion Detection System (AOC-IDS) for IoT by addressing class imbalance, pseudo-label reliability, and computational overhead, achieving significan…

The paper proposes a lightweight hybrid MLP framework that uses structural URL features to achieve highly accurate and computationally efficient real-time phishing URL detection, outperforming several…

This paper demonstrates that an off-the-shelf Large Language Model (LLM) can function as a high-performing, explainable, human-in-the-loop layer for detecting cyberattacks in Industrial Control System…

The paper introduces the CAI Dataset, a massive, multi-terabyte corpus of real-world, hands-on cybersecurity LLM trajectories, designed to address the performance bottleneck caused by expert operator…

The paper systematically evaluates 27 Spiking Neural Network (SNN) configurations to determine the optimal combination of neuron model and spike encoding scheme for network intrusion detection, findin…

The paper evaluates 27 different Spiking Neural Network (SNN) configurations to determine the optimal design for network intrusion detection, finding that the LeakyParallel neuron combined with latenc…

The paper proposes a Digital Twin (DT)-driven hybrid system that combines deterministic heuristics and constrained Large Language Model (LLM) reasoning to achieve highly accurate and interpretable rea…