Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm

Zhang, Ling; Hao, Zhang Jia

doi:10.4018/ijswis.307324

Cited by 21 publications

(15 citation statements)

References 28 publications

(30 reference statements)

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“…The former location of that population member is then replaced if the new position raises the value of the goal function. Equation (18).is used to model this update condition…”

Section: Phase 1: Training (Exploration)mentioning

confidence: 99%

“…The NMIFS approach is used to choose the ideal feature combination in order to effectively reduce high‐dimensional feature data. The best features are sent to the MOP‐AQGA classifier for learning and training, and the intrusion detectors are obtained 18,19 . However, the model could not handle the complex data, and hence the deep learning models arise.…”

Section: Introductionmentioning

confidence: 99%

“…The best features are sent to the MOP-AQGA classifier for learning and training, and the intrusion detectors are obtained. 18,19 However, the model could not handle the complex data, and hence the deep learning models arise. The following contributions help this study implement the deep learning-based intrusion detection model:…”

mentioning

confidence: 99%

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Deep ensemble of classifier for intrusion detection in WSN and improved attack mitigation process

Chandan,

Santhi,

Srinivasa Rao

2024

Concurrency and Computation

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SummaryAttacks have a significant negative impact on the wireless sensor network's network operations. They target to weaken the network layer, if they are not completely eradicated, the networks' ability to execute their desired function becomes collapsed. While intended to monitor such unexpected attacks, anomaly‐based intrusion detection systems (AIDS) have a high risk of false positives. This article intends to propose a Deep Ensemble Intrusion detection with a Self‐adaptive Sewing Training‐Based Optimization (DIED‐SASTO) model that includes the following steps: (a) preprocessing, (b) feature extraction, (c) optimal feature selection, and (d) detection. Initially, an improved class imbalance processing will take place to solve the imbalance problem during the preprocessing step. Subsequently, the features are extracted including higher‐order statistical features, improved entropy, and correlation‐based features during the feature extraction step. From the extracted feature set, as the curse of dimensionality becomes a serious challenge, it is significant to choose the optimal features. In the optimal feature selection process, a new Self‐adaptive Sewing Training‐Based Optimization (SASTO) is used. In addition, the intrusion detection will be employed based on the selected optimal features and trained using deep ensemble models like Deep maxout, Deep Belief Network (DBN), and Bidirectional Long short‐term memory (Bi‐LSTM). Once the presence of an attack is detected, it is mandatory to mitigate the attacker node form the network. For Attack Mitigation, improved entropy will be progressed. The effectiveness of the DIED‐SASTO is evaluated over conventional methods in terms of various metrics.

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“…The former location of that population member is then replaced if the new position raises the value of the goal function. Equation (18).is used to model this update condition…”

Section: Phase 1: Training (Exploration)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep ensemble of classifier for intrusion detection in WSN and improved attack mitigation process

Chandan,

Santhi,

Srinivasa Rao

2024

Concurrency and Computation

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“…The existing trust management schemes [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]44,45,51,52 failed to fulfill the most fundamental requirement for industrial WSN (ICN). Finally, after sincerely analyzing existing work, we can say that without considering indirect (feedback or reputation) trust, frequency of misbehavior, current, and past misbehavior, a malicious node might disguise the network to ruin its reputation 38 and remain not detected as well as trustworthy [46][47][48][49][50] . The survival of ICNs is highly dependent on the successful cooperation of tamper-resistant SNs 3 .…”

Section: Literature Reviewmentioning

confidence: 99%

A secure and dependable trust assessment (SDTS) scheme for industrial communication networks

Khan

Singh

Ahmad

et al. 2023

Sci Rep

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Due to tamper-resistant sensor nodes and wireless media, Industrial Wireless Sensor Networks (WSNs) are susceptible to various security threats that severely affect industrial/business applications. The survival of sensor networks is highly dependent on the flourishing collaboration of sensor nodes. Trust management schemes seem to be realistic and promising techniques to improve security as well as cooperation (dependability) among sensor nodes by estimating the trust level (score) of individual sensor nodes. This research paper presents a well-organized and motivating secure, dependable trust assessment (SDTS) scheme for industrial WSNs to cope with unexpected behavior such as an on–off attack, bad-mouthing attack, garnished attack, etc., by employing robust trust evaluation components based on success ratio and node misbehaviour. SDTS incorporates an interesting trust evaluation function in which the trust range can be adjusted in accordance with the application requirement. SDTS include direct communication trust, indirect communication trust, data trust, and misbehavior-based trust to defend the multiple internal attacks. SDTS works according to the behavior of nodes, i.e., whether the sensor nodes are interacting frequently or not. Moreover, abnormal attenuation and dynamic slide lengths are incorporated in the proposed model (SDTS) to deal with various natural calamities and internal attacks. SDTS is compared against three recent state-of-the-art methods and found efficient in terms of ease of trust assessment, false-positive rate (2.5%), false-negative rate (2%), attack detection rate (90%), detection accuracy (91%), average energy consumption (0.40 J), and throughput (108 Kbps) under the load of 500 sensor nodes with 50% malicious nodes. Investigational results exhibit the potency of the proposed scheme.

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“…This communication process can potentially introduce latency and consume bandwidth, particularly in IoT networks with limited resources. Zhang Ling et al 35 36 introduced two approaches, namely NMAIFS MOP‐AQAI and NMIFS MOP‐AQGA, to tackle the challenges related to detection rate and accuracy. The experiments were conducted using the KDD99 and UNSW‐NB15 datasets.…”

Section: Literature Surveymentioning

confidence: 99%

P‐DNN: Parallel DNN based IDS framework for the detection of IoT vulnerabilities

Sharmila¹,

Nagapadma²

2023

Security and Privacy

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SummaryThe rapid growth of the Internet of Things (IoT) in our daily life has recently received attention from hackers in releasing novel attacks. This is because the existing traditional Intrusion Detection System (IDS) uses an alert‐based approach that cannot detect new emerging attacks, making it unfeasible for devices with limited resources. The study presents the deployment of a Parallel Deep Neural Network (P‐DNN) IDS framework to improve the detection rate and resource constraint issues. To validate the framework for detecting the latest IoT vulnerabilities, we generated a proprietary dataset created under the IoT environment. The experimental results reveal that the P‐DNN IDS framework proposed in this study outperformed the K‐nearest Neighbour (KNN), Support Vector Machine (SVM), and Naive Bayes algorithms with respect to performance metrics. In addition, a comparative analysis of the P‐DNN framework with Snort and Suricata IDS results demonstrates a significant reduction in CPU consumption, memory utilization, and processing time. The results of this investigation indicate that the deployment of the P‐DNN framework has the capability to detect vulnerabilities in resource‐constraint IoT environments effectively.

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Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm

Cited by 21 publications

References 28 publications

Deep ensemble of classifier for intrusion detection in WSN and improved attack mitigation process

Deep ensemble of classifier for intrusion detection in WSN and improved attack mitigation process

A secure and dependable trust assessment (SDTS) scheme for industrial communication networks

P‐DNN: Parallel DNN based IDS framework for the detection of IoT vulnerabilities

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