Isolation Forest based Detection for False Data Attacks in Power Systems

Song, Yufei; Yu, Zongchao; Liu, Xuan; Tian, Jing; Mu, Chunlai

doi:10.1109/isgt-asia.2019.8881319

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…The work highlights that the analysis on ON-OFF and data omission attacks with minor modifications to the tier-2 detection level approach can also be considered for future studies. In [69], researchers have developed an isolation forest-based detection method to detect FDIA without a pretraining procedure for detection labels in a power system with an fast detection accuracy rate 96.3% at time 1.944 sec. In [70], the authors have developed a highly randomized tree algorithm to detect FDIA, which jeopardizes power system state estimation by applying FDIA into smart meter measurements.…”

Section: ) Detection Of Devices Through Cyber-attacksmentioning

confidence: 99%

A Comprehensive Review on Cyber-Attacks in Power Systems: Impact Analysis, Detection, and Cyber Security

Tatipatri,

Arun

2024

IEEE Access

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Continuous communication and information technology advancements facilitate the modernization of the conventional energy grid into an integrated platform. Internet-of-Things (IoT) incorporates power systems, particularly smart grid features and the delivery of new services from the utility side to the end user over a two-way communication channel. However, severe security vulnerabilities have been created due to over-dependency on IoT based communication systems. In addition, critical information exchange between any two entities or devices is always an appealing target for cyber-attackers, especially with financial interest motive by damaging integrity, confidentiality and authenticity in a communication channel. Maintaining data security and preserving privacy in between two entities during the transmission or any data distribution are essential. The potential attacks and impacts of those attacks need to be investigated to develop an effective cyber security infrastructure. Thus, considerable researchers focused on detection and mitigation of these vulnerable cyber-attacks using advanced computation tools. This review article thoroughly investigated possible ways to address cyber security challenges such as smart meter security, end-users privacy, electricity theft cyber-attacks using blockchain and cryptography against communication attacks in smart grid. The operational impacts of cyber-attacks on power system security, as well as the economic impact on deregulated energy markets, have been extensively explored. In addition, the robustness of security features and cryptographic methods against various cyber-attacks is investigated to suggest unexplored cyber-attacks for future scope. Specially, the study of real-world cyber security events, case studies, new findings and new scopes in diverse power industries are carried out. More than 135 research articles has been examined for this review article. This paper mainly concentrates on distribution-side cyberattacks with impact analysis, detection and protection techniques.

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Section: ) Detection Of Devices Through Cyber-attacksmentioning

confidence: 99%

A Comprehensive Review on Cyber-Attacks in Power Systems: Impact Analysis, Detection, and Cyber Security

Tatipatri,

Arun

2024

IEEE Access

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“…Experimentation considered 4 real-time datasets taken from UCI source validate that the suggested procedure can efficiently detect anomalies for the considered data. Song et al [47] employed isolation forest method to detect attacks on power systems which are very vulnerable to cyber-attacks. Authors [48] find isolation forest useful in finding anomalous data patterns in sensory data generated by IoT environment.…”

Section: A Deep Learning Based Idsmentioning

confidence: 99%

Intrusion Detection Based on Autoencoder and Isolation Forest in Fog Computing

2020

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Fog Computing has emerged as an extension to cloud computing by providing an efficient infrastructure to support IoT. Fog computing acting as a mediator provides local processing of the endusers' requests and reduced delays in communication between the end-users and the cloud via fog devices. Therefore, the authenticity of incoming network traffic on the fog devices is of immense importance. These devices are vulnerable to malicious attacks. All kinds of information, especially financial and health information travel through these devices. Attackers target these devices by sending malicious data packets. It is imperative to detect these intrusions to provide secure and reliable service to the user. So, an effective Intrusion Detection System (IDS) is essential for the secure functioning of fog without compromising efficiency. In this paper, we propose a method (Auto-IF) for intrusion detection based on deep learning approach using Autoencoder (AE) and Isolation Forest (IF) for the fog environment. This approach targets only binary classification of the incoming packets as fog devices are more concerned about differentiating attack from normal packets in real-time. We validate the proposed method on the benchmark NSL-KDD dataset. Our technique of intrusion detection achieves a high accuracy rate of 95.4% as compared to many other state-of-art intrusion detection methods.

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