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How effective are traditional IDS and IPS approaches in detecting DDoS attacks on the USA smart grid?

Denial-of-service attack

Computer science

State (computer science)

Cyber-physical system

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Traditional IDS and IPS approaches have limitations in effectively detecting DDoS attacks on the USA smart grid. The smart grid faces various cyber threats, including DDoS attacks, which can disrupt normal operations and compromise security. While general cryptographic techniques and firewalls can detect known attacks, they may struggle with real-time or unknown threats. To enhance detection capabilities, research suggests using reconfigurable Kalman filters and machine learning-based Network Intrusion Detection Systems (NIDS). The proposed models, such as convolutional neural networks and densely connected neural networks, show promise in improving the detection and classification of DDoS attacks on the smart grid, outperforming traditional methods in terms of accuracy and efficiency.

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Proceedings Article•DOI Decentralized Smart Grid System:A Survey On Machine Learning-Based Intrusion Detection Approaches Makhmoor Fiza Murk, Noman Zahid, Ali Hassan Sodhro, Bilal Zahid - Show less +3 more 01 Sep 2022	Not addressed in the paper.
DOI Attack Detection based on Alternating Direction Multiplier Method for Distributed State Estimation in Smart Grid Lingling Yuan, Song Deng, Xiangpeng Xie - Show less +2 more 25 Nov 2022	Not addressed in the paper.
Proceedings Article•DOI Densely Connected Neural Networks for Detecting Denial of Service Attacks on Smart Grid Network Tala Talaei Khoei, Naima Kaabouch - Show less +1 more 26 Oct 2022 1 Citations	Not addressed in the paper.
Open access•Journal Article•DOI Efficient detection of faults and false data injection attacks in smart grid using a reconfigurable Kalman filter Prakyath Dayananda, Mallikarjunaswamy Srikantaswamy, Sharmila Nagaraju, Rekha Velluri, Doddananjedevaru Mahesh Kumar - Show less +4 more 01 Dec 2022-International Journal of Power Electronics and Drive Systems 3 Citations	Not addressed in the paper.
Proceedings Article•DOI Attack Detection based on Alternating Direction Multiplier Method for Distributed State Estimation in Smart Grid 25 Nov 2022	Not addressed in the paper.

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How does the proposed approach compare to other DDoS detection techniques?4 answersThe proposed approach for DDoS detection in SDN controllers utilizes deep learning algorithms, specifically Recurrent Neural Networks (RNNs). It involves three stages: data preprocessing, cross-feature selection, and detection using RNNs. The approach achieves an average detection accuracy of 94.186%, average precision of 92.146%, average false positive rate of 8.114%, and average F1-measure of 94.276%. Another proposed approach uses an ensemble technique, combining bagging and boosting models, and shows better accuracy than existing models in the literature. Additionally, a lightweight detection framework based on Convolutional Neural Network (CNN) is proposed, which effectively detects both benign and malicious flows with high performance and low overhead. The proposed methodology in a cloud-based context uses feature extraction and classifiers like Decision Tree, XGBoost, and Random Forest to effectively detect DDoS attacks.

What are the current limitations of DDoS attack detection techniques?5 answersCurrent limitations of DDoS attack detection techniques include issues related to network overload or delay in detection when collecting monitoring data from networking devices. Another limitation is the lack of support for advanced network monitoring functionalities in the widely-adopted data plane programming language, P4. Additionally, the substantial modifications in the regular pattern and traffic rates of DDoS attacks pose a challenge for their identification using various models. Overfitting and computational time can also be limitations in DDoS attack detection techniques.

What are the recent approaches for intrusion detection in iot?5 answersRecent approaches for intrusion detection in IoT include the use of Artificial Intelligence (AI) solutions such as AI-based Intrusion Detection Systems (IDS) implemented in a decentralized manner to address data privacy and scalability concerns. Federated Learning (FL) has gained interest for collaborative learning while preserving data confidentiality and locality. Graph Neural Networks (GNNs) have also been proposed for Network Intrusion Detection Systems (NIDS) to capture complex relations in IoT traffic and model the underlying topology. Additionally, Convolutional Neural Networks (CNNs) have been employed for IoT intrusion detection, achieving high effectiveness in detecting attacks. These approaches demonstrate improvements in performance metrics such as accuracy, precision, recall, and F1-score, while considering the limited resources and characteristics of IoT devices.

What are the most effective intrusion detection approaches for IOT attacks?5 answersMachine learning-based intrusion detection approaches have shown to be effective for IoT attacks. In particular, Convolutional Neural Networks (CNN) have been used to detect IoT intrusion attacks with a high level of accuracy. Another approach that has been successful is the stacking-ensemble model, which has achieved high Matthews correlation coefficient (MCC) scores in both binary and multiclass classification. Additionally, a novel ensemble IDS approach using Random Forest (RF) for dimensionality reduction and ensemble learning has outperformed other approaches in terms of accuracy and other performance criteria. These findings suggest that machine learning techniques, such as CNN, stacking-ensemble models, and RF-based ensemble IDS, are effective for detecting and identifying IoT attacks.

What are some of the latest developments in cybersecurity for the smart grid?5 answersRecent developments in cybersecurity for the smart grid include the evaluation of cybersecurity methods for detecting and identifying False Data Injection (FDI) attacks, as well as the comparison and discussion of recently proposed cyber-attack detection and identification methods. There is a growing recognition of the inadequate level of security measures in the smart grid, leading to a greater threat landscape. State-of-the-art developments in cybersecurity for smart grids have been reviewed, highlighting the need for future research and collaboration to enhance smart grid cybersecurity. The introduction of smart grids has increased the attack surface of the energy grid, making it susceptible to cyberattacks. Advancements in AI and blockchain technologies are being explored as potential solutions to alleviate cybersecurity challenges. Cybersecurity issues in smart grids have been identified, and methodological approaches to protect against cyber security attacks have been proposed.

How can the security of smart grid networks be improved against cyber-attacks?2 answersTo improve the security of smart grid networks against cyber-attacks, several measures can be taken. Firstly, it is important to address the vulnerabilities in the communication network, smart devices, and sensors that make up the grid. Secondly, a comprehensive analysis and classification of attacks can help in understanding the potential risks and developing effective countermeasures. Real-time health monitoring of the grid and its components can also aid in detecting and mitigating cyber-attacks. Additionally, the use of security solutions and methods such as bi-level optimization and fractional programming can help in modeling and understanding the adversarial game between attackers and operators, and generating optimal attack strategies. By implementing these security measures, the smart grid networks can be better protected against cyber-attacks.

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