An Enhanced Hybrid Intrusion Detection Based on Crow Search Analysis Optimizations and Artificial Neural Network

Gupta, Chetan; Kumar, Amit; Jain, Neelesh Kumar

doi:10.1007/s11277-024-10880-3

Wireless Pers Commun

2024

DOI: 10.1007/s11277-024-10880-3

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An Enhanced Hybrid Intrusion Detection Based on Crow Search Analysis Optimizations and Artificial Neural Network

Chetan Gupta,

Amit Kumar,

Neelesh Kumar Jain

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2024

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Cited by 3 publications

References 35 publications

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Hybrid Deep Learning Approach Based on LSTM and CNN for Malware Detection

Thakur,

Kansal,

Rishiwal

2024

Wireless Pers Commun

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Hybrid Deep Learning Approach Based on LSTM and CNN for Malware Detection

Thakur,

Kansal,

Rishiwal

2024

Wireless Pers Commun

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Review of Intrusion Detection System for Prediction of Cyber Attacks using AI Techniques

Divya Yadav,

Prof. Chetan Gupta,

Dr. Ritu Shrivastava

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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The ever-evolving threat landscape of cyber-attacks necessitates continuous advancements in intrusion detection systems (IDS). This paper delves into the application of Artificial Intelligence (AI) techniques to enhance the predictive capabilities of IDS. We explore the limitations of traditional signature-based and anomaly-based IDS approaches and highlight the potential of AI methods like machine learning (ML) and deep learning (DL) for identifying and predicting novel and sophisticated cyber-attacks. By integrating AI into IDS, organizations can bolster their cyber security posture, proactively mitigate threats, and safeguard their critical infrastructure.

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Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions

BENCHAMA,

ZEBBARA

2023

Data and Metadata

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This paper introduces an innovative intrusion detection system that harnesses Generative Adversarial Networks (GANs), Multi-Scale Convolutional Neural Networks (MSCNNs), and Bidirectional Long Short-Term Memory (BiLSTM) networks, supplemented by Local Interpretable Model-Agnostic Explanations (LIME) for interpretability. Employing a GAN, the system generates realistic network traffic data, encompassing both normal and attack patterns. This synthesized data is then fed into an MSCNN-BiLSTM architecture for intrusion detection. The MSCNN layer extracts features from the network traffic data at different scales, while the BiLSTM layer captures temporal dependencies within the traffic sequences. Integration of LIME allows for explaining the model's decisions. Evaluation on the Hogzilla dataset, a standard benchmark, showcases an impressive accuracy of 99.16% for multi-class classification and 99.10% for binary classification, while ensuring interpretability through LIME. This fusion of deep learning and interpretability presents a promising avenue for enhancing intrusion detection systems by improving transparency and decision support in network security.

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An Enhanced Hybrid Intrusion Detection Based on Crow Search Analysis Optimizations and Artificial Neural Network

Cited by 3 publications

References 35 publications

Hybrid Deep Learning Approach Based on LSTM and CNN for Malware Detection

Hybrid Deep Learning Approach Based on LSTM and CNN for Malware Detection

Review of Intrusion Detection System for Prediction of Cyber Attacks using AI Techniques

Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions

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