IDS Detection Based on Optimization Based on WI-CS and GNN Algorithm in SCADA Network

Shitharth, S.; Satheesh, N.; Balachandran, Praveen Kumar; Sangeetha, K.

doi:10.1007/978-981-16-0386-0_14

Cited by 19 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other words, all inclusive experimentation and trustworthy results need joint consideration. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] emphases only on basic attacks where data is processed with low security features. Even many methods does not incorporate learning techniques for avoiding attacks from external users.…”

Section: Research Gap and Motivationmentioning

confidence: 99%

“…The identification rate is increased utilizing CNNs which are trained and tested in stages known as the training and testing process. According to Shitharth et al (2020) [11], a novel DDoS detection method that leverages machine learning-based classifiers is proposed in a cloud environment. As input to the classifier, people have gathered and categorized characteristics that they believe to be helpful.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Construal Attacks on Wireless Data Storage Applications and Unraveling Using Machine Learning Algorithm

Kshirsagar

Manoharan²,

Alterazi

et al. 2022

Journal of Sensors

Self Cite

View full text Add to dashboard Cite

Cloud services are a popular concept used to describe how internet-based services are delivered and maintained. The computer technology environment is being restructured with respect to information preservation. Data protection is of critical importance when storing huge volumes of information. In today’s cyber world, an intrusion is a significant security problem. Services, information, and services are all vulnerable to attack in the cloud due to its distributed structure of the cloud. Inappropriate behavior in the connection and in the host is detected using intrusion detection systems (IDS) in the cloud. DDoS attacks are difficult to protect against since they produce massive volumes of harmful information on the network. This assault forces the cloud services to become unavailable to target consumers, which depletes computer resources and leaves the provider exposed to massive financial and reputational losses. Cyber-analyst data mining techniques may assist in intrusion detection. Machine learning techniques are used to create many strategies. Attribute selection techniques are also vital in keeping the dataset’s dimensionality low. In this study, one method is provided, and the dataset is taken from the NSL-KDD dataset. In the first strategy, a filtering method called learning vector quantization (LVQ) is used, and in the second strategy, a dimensionality-simplifying method called PCA. The selected attributes from each technique are used for categorization before being tested against a DoS attack. This recent study shows that an LVQ-based SVM performs better than the competition in detecting threats.

show abstract

Section: Research Gap and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Construal Attacks on Wireless Data Storage Applications and Unraveling Using Machine Learning Algorithm

Kshirsagar

Manoharan²,

Alterazi

et al. 2022

Journal of Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Each node has an extensive connection to several other nodes in neighbouring layers. Individual nodes can use the weights and a simple function [22] to determine the output values from the linked nodes. Neural networks can be established for supervised or unsupervised learning.…”

Section: Iirelated Workmentioning

confidence: 99%

An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System

et al. 2022

Self Cite

View full text Add to dashboard Cite

Intrusion detection and classification have gained significant attention recently due to the increased utilization of networks. For this purpose, there are different types of Network Intrusion Detection System (NIDS) approaches developed in the conventional works, which mainly focus on identifying the intrusions from the datasets with the help of classification techniques. Still, it is limited by the significant problems of inefficiency in handling large dimensional datasets, high computational complexity, false detection, and more time consumption for training the models. To solve these problems, this research intends to develop an innovative clustering-based classification methodology to precisely detect intrusions from the different types of IDS datasets. Here, the most recent and extensively used IDS datasets such as NSL-KDD, CICIDS, and Bot-IoT have been employed for detecting intrusions. Data preprocessing has been performed to normalize the dataset to eliminate irrelevant attributes and organize the features. Then, the data separation is applied by forming the clusters by using an intelligent Anticipated Distance-based Clustering (ADC) incorporated with the Density-Based Spatial clustering of applications with noise (DBScan) algorithm. It helps to find the distance and density measures for grouping the attributes into the clusters, which increases the efficiency of classification. Here, the most suitable optimal parameters are selected using the Perpetual Pigeon Galvanized Optimization (PPGO) technique. The extracted features are used for training and testing the dataset samples.Consequently, the Likelihood Naïve Bayes (LNB) classification approach is implemented to accurately predict the classified label as to whether normal or attack. During the evaluation, the performance of the proposed IDS framework is validated and compared using various evaluation metrics. Theresults show that the proposed ADC-DBScan-LNB model outperforms the other techniques with improved performance outcomes. INDEX TERMSNetwork IntrusionDetection System (NIDS), Density-based spatial clustering of applications with noise (DBSCAN), Anticipated Distance-based Clustering (ADC), Data Preprocessing, Likelihood Naïve Bayes (LNB), and IDS Datasets.

show abstract

“…However, if the same amount for different lines is used, then the critical distance of measurement must also be observed in coupling stage periods which is a difficult management process. To manage complexity conditions, both real and reactive measurements are taken for distribution systems using scheduling operation where a greater number of energy resources are introduced [6]. During this scheduling process, the energy in SCADA network and the cost of implementation are reduced, but in case of distribution level measurements, active voltage measurements can only be made which is observed as a major drawback.…”

Section: Literature Surveymentioning

confidence: 99%

Integrating Industrial Appliances for Security Enhancement in Data Point Using SCADA Networks with Learning Algorithm

Khadidos

Manoharan³

et al. 2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

e process of ensuring automatic operation for industrial appliances using both supervision and control techniques is a challenging task.erefore, this article focuses on implementing Supervisory Control and Data Acquisition (SCADA) for controlling all industrial appliances. e design process of implementation case is performed using an analytical framework by examining the primary energy sources at the initial state; thus, a smart network is supported. e designed mathematical model is integrated with a learning technique that allocates resources at proper quantities. Further, the complex manual tuning of individual appliances is avoided in the projected method as the input variables are driven in a direct way at reduced loss state. In addition, the data processing state of individual appliances is carried out using central data controller where all parametric values are stored. In case any errors are observed, then SCADA network fixes the error in an automated way, reducing end-to-end delays in all appliances. To validate the effectiveness of the proposed method, five scenarios are examined and simulated where outcomes prove that SCADA network using learning models provides optimal results on an average of 84 percent as compared to the existing models without learning algorithm.

show abstract

IDS Detection Based on Optimization Based on WI-CS and GNN Algorithm in SCADA Network

Cited by 19 publications

References 28 publications

Construal Attacks on Wireless Data Storage Applications and Unraveling Using Machine Learning Algorithm

Construal Attacks on Wireless Data Storage Applications and Unraveling Using Machine Learning Algorithm

An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System

Integrating Industrial Appliances for Security Enhancement in Data Point Using SCADA Networks with Learning Algorithm

Contact Info

Product

Resources

About