Rule-based Intrusion Detection System using Logical Analysis of Data

Kumar, Anjanee; Das, Tanmoy Kanti

doi:10.1109/idciot56793.2023.10053505

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…This technology will soon surpass our anxiety about cyber threats and permeate almost every area of our lives. In Kumar, A et al [13] introduces the CAVIDS approach proposes a different paradigm by leveraging logical analysis of data for real-time intrusion detection in CAVs. Nevertheless, the specific application of logical analysis techniques tailored for CAVs appears to be a novel contribution.…”

Section: Related Workmentioning

confidence: 99%

A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

Mahendran,

Rajendran,

Pandian

et al. 2024

IEEE Access

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Today's Internet of Vehicles (IoV) has soared by leveraging data gathered from transportation systems, yet it grapples with security concerns stemming from network vulnerabilities, exposing it to cyber threats. This study proposes an innovative method to anticipate anomalies and exploit IoV services related to road traffic. Using the Unceasement Conditional Random Field Dynamic Bayesian Network Model (U-CRF-DDBN), this approach predicts the impact of network attacks, strategically managing vulnerable nodes and attackers. Through experimentation and comparisons with existing methods, our model demonstrates its effectiveness in mitigating IoV vulnerabilities. The U-CRF-DDBN strikes a superior balance, outperforming other approaches in intrusion detection for Internet of Vehicles systems. Evaluating its performance on the NSL-KDD dataset reveals a promising average Detection Rate of 93.512% and a low False Acceptance Rate of 0.125% for known attacks, highlighting its robustness. However, with unknown attacks, while the Detection Rate remains at 74.157%, there is an increased FAR of 16.47%, resulting in a slightly lower F1score of 0.822.

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Section: Related Workmentioning

confidence: 99%

A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

Mahendran,

Rajendran,

Pandian

et al. 2024

IEEE Access

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Quantum particle Swarm optimized extreme learning machine for intrusion detection

Qi,

Liu,

Gani

et al. 2024

J Supercomput

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Ensuring a secure online environment hinges on the timely detection of network attacks. Nevertheless, existing detection methods often grapple with the delicate balance between speed and accuracy. In this paper, we introduce a novel intrusion detection algorithm that marries quantum particle swarm optimization with an extreme learning machine (QPSO-ELM). Firstly, we present a feature selection algorithm grounded in partitioned gains to distill vital features from data samples, thereby diminishing feature count to amplify both model training speed and accuracy. Subsequently, we unveil an intrusion detection scheme underpinned by QPSO-ELM, capable of achieving exceptional levels of training and detection speed, all while maintaining high accuracy. Finally, we fine-tune the trained model using the proposed hidden layer node selection algorithm, reducing the detection model size without compromising detection accuracy, thus further elevating its speed. The experiment results indicate that compared to the current baseline, our proposed intrusion detection scheme achieves the best results in terms of accuracy, precision, recall, and detection latency. Furthermore, the ablation experiment results demonstrate the effectiveness of our proposed method in improving both detection speed and detection accuracy.

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Rule-based Intrusion Detection System using Logical Analysis of Data

Cited by 2 publications

References 18 publications

A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

Quantum particle Swarm optimized extreme learning machine for intrusion detection

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