Enhanced Network Intrusion Detection System

Kotecha, Ketan; Verma, Raghav; Rao, Prahalad V.; Prasad, P. B. Arun; Mishra, Vipul Kumar; Badal, Tapas; Jain, Divyansh; Garg, Deepak; Sharma, Shakti

doi:10.3390/s21237835

Cited by 16 publications

(8 citation statements)

References 23 publications

(25 reference statements)

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“…In Figure 9, a brief comparison analysis of the GJODL-CADC technique with existing techniques was made using the UN-SWNB15 dataset [29][30][31][32]. The results described that the ANN and SVM models exhibit poor performance.…”

Section: Resultsmentioning

confidence: 99%

Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems

Maghrabi,

Alzahrani,

Alsalman

et al. 2023

Electronics

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Recently, artificial intelligence (AI) has gained an abundance of attention in cybersecurity for Industry 4.0 and has shown immense benefits in a large number of applications. AI technologies have paved the way for multiscale security and privacy in cybersecurity, namely AI-based malicious intruder protection, AI-based intrusion detection, prediction, and classification, and so on. Moreover, AI-based techniques have a remarkable potential to address the challenges of cybersecurity that Industry 4.0 faces, which is otherwise called the IIoT. This manuscript concentrates on the design of the Golden Jackal Optimization with Deep Learning-based Cyberattack Detection and Classification (GJODL-CADC) method in the IIoT platform. The major objective of the GJODL-CADC system lies in the detection and classification of cyberattacks on the IoT platform. To obtain this, the GJODL-CADC algorithm presents a new GJO-based feature selection approach to improve classification accuracy. Next, the GJODL-CADC method makes use of a hybrid autoencoder-based deep belief network (AE-DBN) approach for cyberattack detection. The effectiveness of the AE-DBN approach can be improved through the design of the pelican optimization algorithm (POA), which in turn improves the detection rate. An extensive set of simulations were accomplished to demonstrate the superior outcomes of the GJODL-CADC technique. An extensive analysis highlighted the promising performance of the GJODL-CADC technique compared to existing techniques.

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Section: Resultsmentioning

confidence: 99%

Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems

Maghrabi,

Alzahrani,

Alsalman

et al. 2023

Electronics

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“…Security Information and Event Management systems have become essential for Cyber SOCs, playing a critical role in safeguarding the IT infrastructure by enhancing cyber threat detection and response, thereby improving operational efficiency and mitigating security incident impacts [50]. Efficient allocation of centralized NIDS sensors through an SIEM system is crucial for optimizing detection coverage and operational efficiency, considering the organization's specific security needs [51]. This strategic approach allows for cohesive management and comprehensive security data analysis, leading to a faster and more effective response to security incidents [52].…”

Section: Cybersecurity Operations Centersmentioning

confidence: 99%

Enhancing the Efficiency of a Cyber SOC Using Biomimetic Algorithms Empowered by Deep Q–Learning

Olivares,

Salinas,

Ravelo

et al. 2024

Preprint

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Given the landscape of intricate and constantly evolving cyber threats, organizations demand refined strategies to deploy a Security Information and Event Management to support the management of a Cyber Security Operations Center. The dynamic nature of cyber threats complicates the efficient allocation of the location of network intrusion detection sensors, a critical component of a robust cybersecurity framework. Our research introduces an approach that integrates the precision of biomimetic optimization algorithms with the adaptability of Deep Q–Learning. By employing different biomimetic algorithms enhanced with deep learning, we aim to refine the deployment of sensors in network infrastructures, balancing the network security imperative against deployment costs. The results of computational tests demonstrate that the improved iterations leveraging Deep Q–Learning have outperformed their native counterparts. These findings underscore the importance of reinforcement learning, specifically through Deep Q–Learning, as a powerful tool to enhance the effectiveness of metaheuristics in addressing optimization challenges.

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“…The DDoS dataset is used for applying the hybrid model. The research [17] was carried out using a novel Arithmetic Optimization Algorithm (AOEDBC-DL) based on the binary LSTM and deep learning approach [46]. The accuracy is 98.16 % for the WSN-DS dataset.…”

Section: Type Style and Fontsmentioning

confidence: 99%

Intrusion Detection System using the Hybrid Model of Classification Algorithm and Rule-Based Algorithm

Kadam,

Garg,

Gayakwad

et al. 2023

IJRITCC

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Intrusion detection system ID is necessary to secure the system from various intrusions. Analysis of the communication to categorize the data as useful or malicious data is crucial. The cyber security employed using intrusion detection systems should not also cause the extra time to perform the categorization. Nowadays machine learning techniques are used to make the identification of malicious data or an intrusion with the help of classification algorithms. The data set used for experimenting is KDD cup 99. The effect of individual classification algorithms can be improvised with the help of hybrid classification models. This model combines classification algorithms with rule-based algorithms. The blend of classification using machine and human intelligence adds an extra layer of security. An algorithm is validated using precision, recall, F-Measure, and Mean age Precision. The accuracy of the algorithm is 92.35 percent. The accuracy of the model is satisfactory even after the results are acquired by combining our rules inwritten by humans with conventional machine learning classification algorithms. Still, there is scope for improving and accurately classifying the attack precisely.

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Enhanced Network Intrusion Detection System

Cited by 16 publications

References 23 publications

Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems

Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems

Enhancing the Efficiency of a Cyber SOC Using Biomimetic Algorithms Empowered by Deep Q–Learning

Intrusion Detection System using the Hybrid Model of Classification Algorithm and Rule-Based Algorithm

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