Modelling smart grid IT-OT dependencies for DDoS impact propagation

Acarali, Dilara; Rao, K. Rajesh; Rajarajan, Muttukrishnan; Chema, Doron; Ginzburg, Mark

doi:10.1016/j.cose.2021.102528

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…It is a common practice for an attacker to use signal jamming to conduct a denial-of-service or distributed denial-of-service attack at the physical layer; all they need is a bridge to the communication channels. According to previous reports, jammer assaults on a smart grid may cause anything from delayed delivery of time-critical signals to a total denial of service [99].…”

Section: Common Security Risks In Smart Gridsmentioning

confidence: 99%

Incorporation of Blockchain Technology for Different Smart Grid Applications: Architecture, Prospects, and Challenges

et al. 2023

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Smart grid integrates computer, communication, and sensing technologies into existing power grid networks to achieve significant informatization-related advantages. It will provide communication between neighbors, localized management, bidirectional power transfer, and effective demand response. Smart grids (SG) replace conventional grids by integrating various operational measures, including smart automation appliances, smart meters, and renewable energy sources. Regarding energy management and resolving energy issues, SG is one of the most cutting-edge and potentially game-changing innovations. Even still, its complexity suggests that decentralization may provide significant gains. Because of its increasing digitization and interconnectedness, it is also vulnerable to cyber threats. Blockchain, in this sense, is a potential SG paradigm solution that provides several great benefits. Even though blockchains have been widely discussed to decentralize and strengthen smart grid cybersecurity, they have not yet been researched in depth from an application and architectural standpoint. Blockchain-enabled SG applications are the subject of an in-depth research investigation. Electric vehicles (EVs), home automation, energy management systems, etc., are only a few of the many examples that have prompted the proposal of blockchain designs for their respective use cases. Information communication network security is of paramount importance. However, this evolving system raises cybersecurity issues. This paper aims to guide researchers in the right manner so they may build blockchain-based, secure, distributed SG applications in the future. This article also summarizes cybersecurity threats pertaining to smart grids. It starts with a description of a blockchain followed by the blockchain infrastructure, challenges, and solutions for different smart grid applications. A look back at the tried-and-true methods of securing a power grid is offered, and then it discusses the newer and more complex cybersecurity threats to the smart grid. In addition, models of common cyberattacks are presented, and the methods of defense against them are examined.

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Section: Common Security Risks In Smart Gridsmentioning

confidence: 99%

Incorporation of Blockchain Technology for Different Smart Grid Applications: Architecture, Prospects, and Challenges

et al. 2023

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“…(5) We provide future directions for the advancement of research and the problems hindering the IoT and IoT subsystems in handling DDoS attacks. -Detection of DDoS Attack [7] -Blockchain-based Solutions [3] Background Knowledge Blockchain -Secure Blockchain Model for Botnet Detection [8] (Section 4) IPFS -IoT Data Streaming Using Blockchain and IPFS [9] -Data Security: IoT, Blockchain and IPFS [10] Machine Learning -DDoS Attack Prediction [11] DDoS Power Grids -Modeling Impact of DDoS Attack [12] Detection Techniques Blockchain-based -DDoS Detection for Blockchain Network Layer [13] (Section 7)…”

Section: The Key Contributionsmentioning

confidence: 99%

A Survey of DDOS Attack Detection Techniques for IoT Systems Using BlockChain Technology

Khan

Namin

2022

Electronics

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The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems.

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“…Cyber security in smart grids is a critical problem. It refers to data confidentiality, availability, and integrity in systems or smart devices connected to the Internet [15]. One of the motivations behind providing an intrusion detection system for smart grids is the increasing number of attacks on these networks.…”

Section: Introductionmentioning

confidence: 99%

Detection of cyber-attacks on smart grids using improved VGG19 deep neural network architecture and Aquila optimizer algorithm

Mhmood,

Ergül,

Rahebi

2023

SIViP

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Cyber attacks against Smart Grids (SG) have harmful effects. The first function of a defensive system is to provide an intelligent system to detect intrusions. The nature of attacks against smart grids is very complex, so the intrusion detection system must be able to detect complex attacks. Lack of balancing and optimization of deep learning methods are the main challenges for many intrusion detection systems. This research presents an intelligent intrusion detection system for a smart grid based on Game Theory, Swarm Intelligence, and Deep Learning (DL). First, the proposed method balances the training samples with a conditional DL technique based on Game Theory and CGAN. Secondly, the Aquila Optimizer (AO) algorithm selects features. The third step involves mapping the selected features on the dataset and coding reduced-dimension samples into RGB color images, which are used to train the VGG19 neural network. In the fourth step, the AO algorithm optimally adjusts meta-parameters to reduce the error of the VGG19 neural network. Tests performed on the NSL-KDD dataset show that the proposed method's accuracy, sensitivity, and precision in detecting attacks are 99.82%, 99.69%, and 99.76%, respectively. The CGAN method balances the dataset and increases the accuracy, sensitivity, and precision of the proposed method compared to the GAN method in detecting attacks on the smart grid. Experiments show that the proposed method more accurately detects attacks than deep learning methods such as VGG19, CNN-GRU, CNN-GRU-FL, LSTM, and CNN.

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Modelling smart grid IT-OT dependencies for DDoS impact propagation

Cited by 10 publications

References 27 publications

Incorporation of Blockchain Technology for Different Smart Grid Applications: Architecture, Prospects, and Challenges

Incorporation of Blockchain Technology for Different Smart Grid Applications: Architecture, Prospects, and Challenges

A Survey of DDOS Attack Detection Techniques for IoT Systems Using BlockChain Technology

Detection of cyber-attacks on smart grids using improved VGG19 deep neural network architecture and Aquila optimizer algorithm

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