Attack detection and prevention in IoT-SCADA networks using NK-classifier

Justindhas, Y.; Jeyanthi, P.

doi:10.1007/s00500-022-06921-3

Cited by 13 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The original contribution of this work was to ensure the properties of integrity, availability, and confidentiality for protecting the SCADA networks against anomalous activities. Justindhas et al [32] employed an Elephant Herding Optimization (EHO) integrated NK-RNN classification mechanism for improving the security of SCADA systems. In addition, a modified Elliptic Curve Cryptography (ECC) technique was used to prevent the network from attacks.…”

Section: Literature Reviewmentioning

confidence: 99%

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

Rabie

Balachandran²,

Shitharth

2022

Electronics

View full text Add to dashboard Cite

Smart grids are complex cyber-physical systems that incorporate smart devices’ communication capabilities into the grid to enable remote management and the control of power systems. However, this integration reveals numerous SCADA system flaws, which could compromise security goals and pose severe cyber threats to the smart grid. In conventional works, various attack detection methodologies are developed to strengthen the security of smart grid SCADA systems. However, they have several issues with complexity, slow training speed, time consumption, and inaccurate prediction outcomes. The purpose of this work is to develop a novel security framework for protecting smart grid SCADA systems against harmful network vulnerabilities or intrusions. Therefore, the proposed work is motivated to develop an intelligent meta-heuristic-based Artificial Intelligence (AI) mechanism for securing IoT-SCADA systems. The proposed framework includes the stages of dataset normalization, Zaire Ebola Search Optimization (ZESO), and Deep Random Kernel Forest Classification (DRKFC). First, the original benchmarking datasets are normalized based on content characterization and category transformation during preprocessing. After that, the ZESO algorithm is deployed to select the most relevant features for increasing the training speed and accuracy of attack detection. Moreover, the DRKFC technique accurately categorizes the normal and attacking data flows based on the optimized feature set. During the evaluation, the performance of the proposed ZESO-DRKFC method is validated and compared in terms of accuracy, detection rate, f1-score, and false acceptance rate. According to the results, it is observed that the ZESO-DRKFC mechanism outperforms other techniques with high accuracy (99%) by precisely spotting intrusions in the smart grid systems.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

Rabie

Balachandran²,

Shitharth

2022

Electronics

View full text Add to dashboard Cite

show abstract

“…In paper [25], an advanced optimization‐based machine learning model was deployed to increase the security of smart grid SCADA systems against the harmful network intrusions. Justindhas and Jeyanthi [26] deployed an Elephant Herding Optimization (EHO) based classification model for identifying intrusions from the smart grid SCADA systems. In addition to that, a modified Elliptic Curve Cryptography (ECC) mechanism was used to perform Caesar ciphering against the network attacks.…”

Section: Related Workmentioning

confidence: 99%

A security model for smart grid SCADA systems using stochastic neural network

Rabie

Shitharth

Alghazzawi

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

Detection of cyber‐threats in the smart grid Supervisory Control and Data Acquisition (SCADA) is still remains one of the complex and essential processes need to be highly concentrated in present times. Typically, the SCADA is more prone to the security issues due to their environmental problems and vulnerabilities. Therefore, the proposed work intends to design a new detection approach by integrating the optimization and classification models for smart grid SCADA security. In this framework, the min‐max normalization is performed at first for noise removal and attributes arrangement. Here, the correlation estimation mechanism is mainly deployed to reduce the dimensionality of features by choosing the relevant features used for attack prediction. Moreover, the optimal features are selected by using the optimal solution provided by the Holistic Harris Hawks Optimization (H3O). Finally, the Perceptron Stochastic Neural Network (PSNN) is utilized to categorize the normal and attacking data flow in the network with minimal processing time and complexity. By using the combination of proposed H3O‐PSNN technique, the detection accuracy is improved up to 99% for all datasets used in this study, and also other measures such as precision to 99.2%, recall to 99%, f1‐score to 99.2% increased, when compared to the standard techniques.

show abstract

“…Equations 2 and 3 represent the balanced equation for converting imbalanced data into balanced data, and the distance between two data points is calculated using equation (6).…”

Section: ) Smote and Rus Techniquesmentioning

confidence: 99%

“…An effective solution may be to introduce an optimal routing-based MAC protocol. Researchers believe that it will serve as an important medium for secure data transmission between IoT devices [6]. The concept can also be extended to intrusion detection systems (IDS) to detect unauthorized or malicious access [7].…”

Section: Introductionmentioning

confidence: 99%

MAC Protocol Based IoT Network Intrusion Detection Using Improved Efficient Shuffle Bidirectional COOT Channel Attention Network

Nayak

Bhattacharyya

2023

IEEE Access

View full text Add to dashboard Cite

The IoT networks are customized to work under various smart environments, utilizing diverse sensors. However, they are vulnerable to several cyberattacks because of their finite resources and limited computing capabilities. Intruders can easily hack the data transmitted across the end nodes. Therefore, it is crucial to protect the privacy of the IoT network against adversarial attacks. For real-time insights from IoT networks, an appropriate networking protocol is essential. In previous studies, several routing protocols have been formulated for intrusion detection with limited performance. Even the application of advanced machine learning (ML) has shown relatively lower accuracy with an increased error rate. To address these issues, an innovative intrusion detection system (IDS) for IoT networks based on medium access control (MAC) protocols with an improved efficient shuffle bidirectional COOT channel attention network (IEsBCCA-Net) method has been proposed. The suggested system uses the MAC protocol to securely transmit the data with low energy consumption and delay, which provides increased throughput. The collected data is stored in the BoT-IoT dataset, which is imbalanced and balanced using the synthetic minority over-sampling technique (SMOTE) and random under-sampling (RUS) techniques. The data is pre-processed using the reformed histogram equalization method, and the optimal features are selected using the modified honey badger algorithm (MHBA). Finally, the intrusions are classified using the IEsBCCA-Net approach, which provides outstanding accuracy compared to state-of-the-art (SOTA) methods. INDEX TERMSImproved efficient shuffle bidirectional COOT channel attention network, intrusion detection, IoT network, MAC protocol, modified honey badger algorithm. NANAVATH KIRAN SINGH NAYAK was born in Hyderabad, Telangana, India. He received the B.Tech. and M.Tech. degrees in electronics and communication engineering from Jawaharlal

show abstract

Attack detection and prevention in IoT-SCADA networks using NK-classifier

Cited by 13 publications

References 27 publications

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

A security model for smart grid SCADA systems using stochastic neural network

MAC Protocol Based IoT Network Intrusion Detection Using Improved Efficient Shuffle Bidirectional COOT Channel Attention Network

Contact Info

Product

Resources

About