A Deep Multi-architectural Approach for Online Social Network Intrusion Detection System

Boahen, Edward Kwadwo; Frimpong, Samuel Akwasi; Ujakpa, Martin Mabeifam; Sosu, Rexford Nii Ayitey; Larbi-Siaw, Otu; Owusu, Ebenezer; Appati, Justice Kwame; Acheampong, Ernest Nti

doi:10.1109/aic55036.2022.9848865

Cited by 6 publications

(2 citation statements)

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“…The network defense is assured by analyzing the incoming data traffic of the network based on the signs and behavior [1]. The activities of the networks, like dynamic tendency, convolution, heterogeneity, and vagueness, are analyzed to identify the malicious activity of the network continuously [2,3]. Here, the detection of malicious activity or marking of the network intrusion is termed Intrusion detection and utilizes two different criteria in its detection process.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Hybrid CAE-ELM and Enhanced Smote Based Intrusion Detection for Networks

2023

IJIES

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The intrusion detection system (IDS) plays an imperative role in defending the network from attacks. But, the IDS data is imbalanced, making the process complex for detecting the attacks accurately. According to these problems, this study proposes a network intrusion detection system based on an enhanced synthetic minority oversampling technique (SMOTE) and lightweight hybrid CAE (convolutional auto encoder)-ELM (extreme learning machine) model. Initially, the normalization of the original data is performed to avoid the influence of the maximum and minimum values. Secondly, an enhanced SMOTE is employed for solving the issues of the less detection rate of minority-attacks because of less training data. Here, the war strategy optimization (WSO) is incorporated with the SMOTE to design a novel WSO-based SMOTE technique for balancing the dataset. The three major steps utilized in WSO-based SMOTE are WSO based clustering, filtering and over-sampling. Then, the Information gain and fisher score based features extraction is employed for the reduction of computational complexity prior to the intrusion detection. Finally, the lightweight hybrid CAE-ELM model is executed for attack detection. CAEs are one of the most commonly used learning models because of their ability to construct a higher-level feature representation from the input data. Another model used in intrusion detection is the ELM, which provides an acceptable discrimination performance as well as a fast speed of learning. The performance of the proposed NIDS model is tested on the two benchmark datasets and achieved better accuracies of 99.21% and 99.15% on the UNSW-NB15 and CSE-CIC-IDS2018 datasets.

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Section: Introductionmentioning

confidence: 99%

Lightweight Hybrid CAE-ELM and Enhanced Smote Based Intrusion Detection for Networks

2023

IJIES

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“…An Intrusion Detection System (IDS) is a Software or Hardware system, implement to detect and prevent unauthorized access to any computer system or network [7]. IDS is in two types: to be host-based, integrated on a host device and checks process and user activity on the local machine to detect intrusion, or network-based, the most commonly used IDS established over a network and works within a network system in a distributed manner to check traffic flow for intrusions [8,9]. IDS classifies Aegean Wi-Fi Intrusion Dataset (AWID) into the most commonly four types of classes: Normal, Flooding, Injection, and Impersonation according to their behavior on the wireless network.…”

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confidence: 99%

Intrusion Detection System for Wireless Sensor Networks: A Machine Learning Based Approach

Sadia,

Farhan,

Haq

et al. 2024

IEEE Access

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In this era, there are plenty of wireless devices that are being used with the support of WI-FI (Wireless Fidelity) and they need to be maintained and authorized. Wireless Sensor Networks (WSN), a cornerstone of modern wireless technology, offer cost-efficient solutions for diverse monitoring tasks but are simultaneously exposed to a myriad of security threats, including unauthorized access, attacks, and suspicious activities. These vulnerabilities can significantly degrade the performance and reliability of WSNs, making the early detection and mitigation of such threats imperative. Intrusion Detection Systems (IDS) emerge as crucial tools in safeguarding WSNs against these challenges. Numerous studies focus on enhanced Intrusion Detection model accuracy and decrease in loss with higher Detection Rate and lower False Alarm Rate, because of this, eliminating the repetitive feature of the dataset is exhibited. This study introduces a sophisticated Network Intrusion Detection System (NIDS) designed to safeguard Wi-Fi-based WSNs from prevalent cyber threats, such as impersonation, flooding, and injection attacks. At the heart of our approach is a meticulous feature selection process that enhances the dataset's utility by eliminating null values, substituting unknown entries with a placeholder ('NONE'), and refining the feature set to include only the most relevant indicators of potential security breaches. Initially, from a pool of 154 features, a subset of 76 is selected, which is further narrowed down to 13 pivotal features, ensuring a focused and efficient analysis. Employing standard scaler function for feature scaling and preprocessing, this research train proposed a Convolutional Neural Network (CNN) based approach aiming for optimal intrusion detection and prevention across multiclass classifications within WSN environments. The study aims to enhance detection accuracy, reduce loss values, and decrease false alarm rates, comparing it to CNN, Deep Neural Network (DNN) (5), DNN (3), and (Long Short-Term Memory) LSTM networks. The model's performance is evaluated using various metrics, including precision, recall, support, F1 score, and macro-average. The culmination of our research efforts is evidenced by the exceptional performance of the CNN model, achieving an impressive accuracy rate of 97% and a loss metric of 0.14, all while maintaining a minimal False Alarm Rate. This study significantly advances IDS accuracy while simultaneously reducing false alarms, thus fortifying the security posture of WSNs in the face of evolving cyber threats. INDEX TERMSWSN, Wi-Fi, NIDS, WIDS attacks, Security issues, Network threats, Feature engineering, multiclass classification I. INTRODUCTION W IRELESS Sensor Network (WSN) is one of the ultimate sources for communication of wireless base network domain [1]. WSNs integrate with the help of sensor nodes that establish with different topologies such as star, tree, or mesh [2], it senses the data flow and transmission within the wireless network and the dominant serviceabilities ...

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A Comprehensive Review on Deep Learning Techniques on Cyber Attacks on Cyber Physical Systems

Sagar,

Vanmathi

2024

SN COMPUT. SCI.

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A Deep Multi-architectural Approach for Online Social Network Intrusion Detection System

Cited by 6 publications

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Lightweight Hybrid CAE-ELM and Enhanced Smote Based Intrusion Detection for Networks

Lightweight Hybrid CAE-ELM and Enhanced Smote Based Intrusion Detection for Networks

Intrusion Detection System for Wireless Sensor Networks: A Machine Learning Based Approach

A Comprehensive Review on Deep Learning Techniques on Cyber Attacks on Cyber Physical Systems

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