Cyber Kill Chain-Based Hybrid Intrusion Detection System for Smart Grid

Singh, Vivek Kumar; Manimaran, G.

doi:10.1007/978-3-030-54275-7_22

Cited by 8 publications

(2 citation statements)

References 16 publications

(24 reference statements)

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“…However, this work failed to prove the efficacy and attack detection competency of the suggested model. Singh et al [44] developed a cyber-kill chain-based IDS framework for increasing the security of SCADA networks.…”

Section: Literature Reviewmentioning

confidence: 99%

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

Rabie

Balachandran²,

Shitharth

2022

Electronics

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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.

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Section: Literature Reviewmentioning

confidence: 99%

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

Rabie

Balachandran²,

Shitharth

2022

Electronics

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“…Their model achieved an overall detection accuracy of 99.5% and 99.9% for DDoS and car hacking, respectively. Lastly, in [33], the authors introduced a cyber kill chain-based hybrid IDS framework for a smart grid. They applied the cyber kill chain to identify cyber attacks at different stages of the chain.…”

Section: Intrusion Detectionmentioning

confidence: 99%

Rapid Forecasting of Cyber Events Using Machine Learning-Enabled Features

Ahmed,

Azad,

Asyhari

2024

Information

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In recent years, there has been a notable surge in both the complexity and volume of targeted cyber attacks, largely due to heightened vulnerabilities in widely adopted technologies. The Prediction and detection of early attacks are vital to mitigating potential risks from cyber attacks and network resilience. With the rapid increase of digital data and the increasing complexity of cyber attacks, big data has become a crucial tool for intrusion detection and forecasting. By leveraging the capabilities of unstructured big data, intrusion detection and forecasting systems can become more effective in detecting and preventing cyber attacks and anomalies. While some progress has been made on attack prediction, little attention has been given to forecasting cyber events based on time series and unstructured big data. In this research, we used the CSE-CIC-IDS2018 dataset, a comprehensive dataset containing several attacks on a realistic network. Then we used time-series forecasting techniques to construct time-series models with tuned parameters to assess the effectiveness of these techniques, which include Sequential Minimal Optimisation for regression (SMOreg), linear regression and Long Short-Term Memory (LSTM) to forecast the cyber events. We used machine learning algorithms such as Naive Bayes and random forest to evaluate the performance of the models. The best performance results of 90.4% were achieved with Support Vector Machine (SVM) and random forest. Additionally, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics were used to evaluate forecasted event performance. SMOreg’s forecasted events yielded the lowest MAE, while those from linear regression exhibited the lowest RMSE. This work is anticipated to contribute to effective cyber threat detection, aiming to reduce security breaches within critical infrastructure.

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Cyber-Security of Protection System in Power Grids—Part 1: Vulnerabilities and Counter-Measures

Ameli

Ghafouri

et al. 2023

Power Systems

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Cyber Kill Chain-Based Hybrid Intrusion Detection System for Smart Grid

Cited by 8 publications

References 16 publications

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

Rapid Forecasting of Cyber Events Using Machine Learning-Enabled Features

Cyber-Security of Protection System in Power Grids—Part 1: Vulnerabilities and Counter-Measures

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