A Data Preparation Method for Machine-Learning-Based Power System Cyber-Attack Detection

2018 ◽  
Author(s):  
Hongyu Chen ◽  
Jingyu Wang ◽  
Dongyuan Shi
Keyword(s):  
Machine Learning ◽  
Power System ◽  
Preparation Method ◽  
Attack Detection ◽  
Cyber Attack ◽  
Data Preparation

scholarly journals Cyber-Attack Detection in DC Microgrids Based on Deep Machine Learning and Wavelet Singular Values Approach

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1914
Author(s):  
Moslem Dehghani ◽  
Taher Niknam ◽  
Mohammad Ghiasi ◽  
Navid Bayati ◽  
Mehdi Savaghebi
Keyword(s):  
Machine Learning ◽  
Power System ◽  
Cyber Security ◽  
Public Transportation ◽  
Energy Resource ◽  
Singular Values ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Gray Wolf ◽  
Cyber Attack

Nowadays, the role of cyber-physical systems (CPSs) is of paramount importance in power system security since they are more vulnerable to different cyber-attacks. Detection of cyber-attacks on a direct current microgrid (DC-MG) has become a pivotal issue due to the increasing use of them in various electrical engineering applications, from renewable power generations to the distribution of electricity and power system of public transportation and subway electric network. In this study, a novel strategy was provided to diagnose possible false data injection attacks (FDIA) in DC-MGs to enhance the cyber-security of electrical systems. Accordingly, to diagnose cyber-attacks in DC-MG and to identify the FDIA to distributed energy resource (DER) unit, a new procedure of wavelet transform (WT) and singular value decomposition (SVD) based on deep machine learning was proposed. Additionally, this paper presents a developed selective ensemble deep learning (DL) approach using the gray wolf optimization (GWO) algorithm to identify the FDIA in DC-MG. In the first stage, in the paper, to gather sufficient data within the ordinary performance required for the training of the DL network, a DC-MG was operated and controlled with no FDIAs. In the information generation procedure, load changing was considered to have diagnosing datasets for cyber-attack and load variation schemes. The obtained simulation results were compared with the new Shallow model and Hilbert Huang Transform methods, and the results confirmed that the presented approach could more precisely and robustly identify multiple forms of FDIAs with more than 95% precision.

scholarly journals A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 80778-80788 ◽  
Author(s):  
Hadis Karimipour ◽  
Ali Dehghantanha ◽  
Reza M. Parizi ◽  
Kim-Kwang Raymond Choo ◽  
Henry Leung
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Large Scale ◽  
Attack Detection ◽  
Learning System ◽  
Cyber Attack ◽  
Unsupervised Machine Learning

scholarly journals An Efficient Classifier for U2R, R2L, DoS Attack

2020 ◽  
Vol 9 (1) ◽  
pp. 644-647
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Learning Algorithms ◽  
Research Area ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Detection Accuracy ◽  
Cyber Attack ◽  
Detection Systems

The internet has become an irreplaceable communicating and informative tool in the current world. With the ever-growing importance and massive use of the internet today, there has been interesting from researchers to find the perfect Cyber Attack Detection Systems (CADSs) or rather referred to as Intrusion Detection Systems (IDSs) to protect against the vulnerabilities of network security. CADS presently exist in various variants but can be largely categorized into two broad classifications; signature-based detection and anomaly detection CADSs, based on their approaches to recognize attack packets.The signature-based CADS use the well-known signatures or fingerprints of the attack packets to signal the entry across the gateways of secured networks. Signature-based CADS can only recognize threats that use the known signature, new attacks with unknown signatures can, therefore, strike without notice. Alternatively, anomaly-based CADS are enabled to detect any abnormal traffic within the network and report. There are so many ways of identifying anomalies and different machine learning algorithms are introduced to counter such threats. Most systems, however, fall short of complete attack prevention in the real world due system administration and configuration, system complexity and abuse of authorized access. Several scholars and researchers have achieved a significant milestone in the development of CADS owing to the importance of computer and network security. This paper reviews the current trends of CADS analyzing the efficiency or level of detection accuracy of the machine learning algorithms for cyber-attack detection with an aim to point out to the best. CADS is a developing research area that continues to attract several researchers due to its critical objective.

scholarly journals A deep learning‐based classification scheme for cyber‐attack detection in power system

2021 ◽  
Author(s):  
Yucheng Ding ◽  
Kang Ma ◽  
Tianjiao Pu ◽  
Yingxing Wang ◽  
Ran Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power System ◽  
Classification Scheme ◽  
Attack Detection ◽  
Cyber Attack

scholarly journals Experimental Cyber Attack Detection Framework

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1682
Author(s):  
Cătălin Mironeanu ◽  
Alexandru Archip ◽  
Cristian-Mihai Amarandei ◽  
Mitică Craus
Keyword(s):  
Machine Learning ◽  
Attack Detection ◽  
Learning Tools ◽  
Cyber Attack ◽  
Network Access ◽  
Public And Private ◽  
Real Time Traffic ◽  
Attack Pattern ◽  
Attack Patterns ◽  
Novel Concept

Digital security plays an ever-increasing, crucial role in today’s information-based society. The variety of threats and attack patterns has dramatically increased with the advent of digital transformation in our lives. Researchers in both public and private sectors have tried to identify new means to counteract these threats, seeking out-of-the-box ideas and novel approaches. Amongst these, data analytics and artificial intelligence/machine learning tools seem to gain new ground in digital defence. However, such instruments are used mainly offline with the purpose of auditing existing IDS/IDPS solutions. We submit a novel concept for integrating machine learning and analytical tools into a live intrusion detection and prevention solution. This approach is named the Experimental Cyber Attack Detection Framework (ECAD). The purpose of this framework is to facilitate research of on-the-fly security applications. By integrating offline results in real-time traffic analysis, we could determine the type of network access as a legitimate or attack pattern, and discard/drop the latter. The results are promising and show the benefits of such a tool in the early prevention stages of both known and unknown cyber-attack patterns.

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