An Artificial Neural Network Technique for Prediction of Cyber-Attack using Intrusion Detection System

Jain, Jay Kumar; Waoo, Akhilesh A.

doi:10.55529/jaimlnn.32.33.42

Cited by 10 publications

(3 citation statements)

References 15 publications

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“…The goal of a DoS attack is not to breach the system' security but rather to hinder its functionality and disrupt normal operations. DoS attacks can be executed using various methods, such as flooding the target with traffic, exploiting vulnerabilities in the system, or consuming its resources excessively [20]. Mitigating and preventing DoS attacks require robust security measures, including traffic filtering, resource management, and the use of specialized intrusion detection and prevention systems.…”

Section: Dosmentioning

confidence: 99%

A hybrid deep learning approach for enhanced network intrusion detection

Prabu,

Sudhakar

2024

IJEECS

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The contemporary era places paramount importance on network security and cloud environments, driven by increased data transmission demands, the flexibility of cloud services, and the prevalence of global resources. Addressing the escalating threat of computer malware, the development of efficient intrusion detection systems (IDS) is imperative. This research focuses on the challenges posed by imbalanced datasets and the necessity for unsupervised learning to enhance network security. The proposed hybrid deep learning method utilizes raw data from the CSE-CIC-IDS-2018 dataset, integrating imbalanced and unsupervised learning techniques. After preprocessing and normalization, feature extraction through principal component analysis (PCA) reduces dimensionality from seventy-eight fields to ten essential features. Clustering, employing the density-based spatial clustering of applications with noise (DBSCAN) algorithm optimized with particle swarm optimization (PSO), is applied to the extracted features, distinguishing between attack and non-attack packets. Addressing dataset imbalances, imbalanced learning techniques are employed, and unsupervised learning is exemplified through the AutoEncoder (AE) algorithm. The attack cluster’s data is input into AE, a deep learning-based approach, yielding outputs for attack classification. The proposed technique (PCA+DBSCANPSO+AE) achieves an impressive 99.19% accuracy in intrusion detection, surpassing contemporary methodologies and five existing techniques. This research not only enhances accuracy but also addresses imbalanced learning challenges, utilizing the power of unsupervised learning for robust network security.

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Section: Dosmentioning

confidence: 99%

A hybrid deep learning approach for enhanced network intrusion detection

Prabu,

Sudhakar

2024

IJEECS

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“…There must be more research into cyber refuge and the prevention of cyber attacks, such as the deployment of intrusion detection as a preventative measure, as indicated by Jain et al [18]. Most people today utilize at least one internet service.…”

Section: Related Workmentioning

confidence: 99%

Enhanced SVM Model with Orthogonal Learning Chaotic Grey Wolf Optimization for Cybersecurity Intrusion Detection in Agriculture 4.0

Shaik,

Thumboor,

Veluru

et al. 2023

IJSSE

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Smart agriculture, also known as Agriculture 4.0, integrates cutting-edge technology with conventional farming practices through the agricultural Internet of Things (IoT). Despite its numerous advantages, Agriculture 4.0 introduces additional cybersecurity risks due to the widespread deployment of IoT-based devices. One significant threat is Distributed Denial of Service (DDoS) attacks, which can compromise the availability and integrity of agricultural systems. This paper proposes an Enhanced Multiclass Support Vector Machine (EMSVM) model for detecting DDoS attacks in Agriculture 4.0. To improve classification accuracy, the EMSVM model incorporates a novel optimization method called Orthogonal Learning Chaotic Grey Wolf Optimization (OLCGWO) for parameter selection. The performance of the proposed methodology is evaluated using two realworld traffic datasets, CIC-DDoS2019 and TON_IoT, which contain various DDoS attack scenarios. The results demonstrate the effectiveness of the EMSVM model in both binary and multiclass classification contexts.

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“…In recent years, there has been growing interest in leveraging artificial intelligence (AI) techniques to enhance the detection capabilities of HIDSs by analyzing large volumes of data, identifying patterns, and detecting anomalies that may indicate malicious activities [15] and for system-call-pattern recognition [7]. One approach in the AI direction is the use of autoencoders, a type of neural network, for intrusion detection [16].…”

Section: Introductionmentioning

confidence: 99%

Risk-Based System-Call Sequence Grouping Method for Malware Intrusion Detection

Vyšniūnas,

Čeponis,

Goranin

et al. 2024

Electronics

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Malware intrusion is a serious threat to cybersecurity; that is why new and innovative methods are constantly being developed to detect and prevent it. This research focuses on malware intrusion detection through the usage of system calls and machine learning. An effective and clearly described system-call grouping method could increase the various metrics of machine learning methods, thereby improving the malware detection rate in host-based intrusion-detection systems. In this article, a risk-based system-call sequence grouping method is proposed that assigns riskiness values from low to high based on function risk value. The application of the newly proposed grouping method improved classification accuracy by 23.4% and 7.6% with the SVM and DT methods, respectively, compared to previous results obtained on the same methods and data. The results suggest the use of lightweight machine learning methods for malware attack can ensure detection accuracy comparable to deep learning methods.

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An Artificial Neural Network Technique for Prediction of Cyber-Attack using Intrusion Detection System

Cited by 10 publications

References 15 publications

A hybrid deep learning approach for enhanced network intrusion detection

A hybrid deep learning approach for enhanced network intrusion detection

Enhanced SVM Model with Orthogonal Learning Chaotic Grey Wolf Optimization for Cybersecurity Intrusion Detection in Agriculture 4.0

Risk-Based System-Call Sequence Grouping Method for Malware Intrusion Detection

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