MACHINE LEARNING ALGORITHMS AND THEIR APPLICATIONS IN CLASSIFYING CYBER-ATTACKS ON A SMART GRID NETWORK

2021 ◽  
Author(s):  
Adedayo Aribisala ◽  
Mohammad S. Khan ◽  
Ghaith Husari
Keyword(s):  
Machine Learning ◽  
Smart Grid ◽  
Learning Algorithms ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Grid Network ◽  
Smart Grid Network

scholarly journals Towards Near-Real-Time Intrusion Detection for IoT Devices using Supervised Learning and Apache Spark

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 444 ◽  
Author(s):  
Valerio Morfino ◽  
Salvatore Rampone
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithms ◽  
Hybrid Approach ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Apache Spark ◽  
Identification Accuracy ◽  
Supervised Machine Learning ◽  
Iot Devices

In the fields of Internet of Things (IoT) infrastructures, attack and anomaly detection are rising concerns. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing proportionally. In this paper the performances of several machine learning algorithms in identifying cyber-attacks (namely SYN-DOS attacks) to IoT systems are compared both in terms of application performances, and in training/application times. We use supervised machine learning algorithms included in the MLlib library of Apache Spark, a fast and general engine for big data processing. We show the implementation details and the performance of those algorithms on public datasets using a training set of up to 2 million instances. We adopt a Cloud environment, emphasizing the importance of the scalability and of the elasticity of use. Results show that all the Spark algorithms used result in a very good identification accuracy (>99%). Overall, one of them, Random Forest, achieves an accuracy of 1. We also report a very short training time (23.22 sec for Decision Tree with 2 million rows). The experiments also show a very low application time (0.13 sec for over than 600,000 instances for Random Forest) using Apache Spark in the Cloud. Furthermore, the explicit model generated by Random Forest is very easy-to-implement using high- or low-level programming languages. In light of the results obtained, both in terms of computation times and identification performance, a hybrid approach for the detection of SYN-DOS cyber-attacks on IoT devices is proposed: the application of an explicit Random Forest model, implemented directly on the IoT device, along with a second level analysis (training) performed in the Cloud.

IoT-based SCADA System for Smart Grid Stability Monitoring using Machine Learning Algorithms

2021 ◽  
Author(s):  
Adriana Babilonia Risco ◽  
Renzo Ivan Gonzalez Salinas ◽  
Alhiet Orbegoso Guerrero ◽  
Daniel Leonardo Barrera Esparta
Keyword(s):  
Machine Learning ◽  
Smart Grid ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Stability Monitoring ◽  
Scada System

Survey of Machine Learning Algorithms to Detect Malware in Consumer Internet of Things Devices

2021 ◽  
Vol 30 (04) ◽  
pp. 2150020
Author(s):  
Luke Holbrook ◽  
Miltiadis Alamaniotis
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Internet Of Things ◽  
Deep Neural Network ◽  
Learning Algorithms ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Iot Devices

With the increase of cyber-attacks on millions of Internet of Things (IoT) devices, the poor network security measures on those devices are the main source of the problem. This article aims to study a number of these machine learning algorithms available for their effectiveness in detecting malware in consumer internet of things devices. In particular, the Support Vector Machines (SVM), Random Forest, and Deep Neural Network (DNN) algorithms are utilized for a benchmark with a set of test data and compared as tools in safeguarding the deployment for IoT security. Test results on a set of 4 IoT devices exhibited that all three tested algorithms presented here detect the network anomalies with high accuracy. However, the deep neural network provides the highest coefficient of determination R2, and hence, it is identified as the most precise among the tested algorithms concerning the security of IoT devices based on the data sets we have undertaken.

scholarly journals SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach

2018 ◽  
Vol 10 (8) ◽  
pp. 76 ◽  
Author(s):  
Marcio Teixeira ◽  
Tara Salman ◽  
Maede Zolanvari ◽  
Raj Jain ◽  
Nader Meskin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supervisory Control ◽  
Network Traffic ◽  
Learning Algorithms ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Scada System ◽  
Machine Learning Approach ◽  
Machine Learning Models

This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank’s control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naïve Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments.

scholarly journals A Review on Cyber Security and the Fifth Generation Cyberattacks

2019 ◽  
Vol 12 (2) ◽  
pp. 50-56
Author(s):  
A. Saravanan ◽  
S. Sathya Bama
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Domain Knowledge ◽  
Learning Algorithms ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Digital World ◽  
Digital Era ◽  
Fifth Generation ◽  
Main Challenge

Cyber attacks have become quite common in this internet era. The cybercrimes are getting increased every year and the intensity of damage is also increasing. providing security against cyber-attacks becomes the most significant in this digital world. However, ensuring cyber security is an extremely intricate task as requires domain knowledge about the attacks and capability of analysing the possibility of threats. The main challenge of cybersecurity is the evolving nature of the attacks. This paper presents the significance of cyber security along with the various risks that are in the current digital era. The analysis made for cyber-attacks and their statistics shows the intensity of the attacks. Various cybersecurity threats are presented along with the machine learning algorithms that can be applied to cyber attacks detection. The need for the fifth generation cybersecurity architecture is discussed.

scholarly journals Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6935
Author(s):  
Qasem Abu Al-Haija ◽  
Abdallah A. Smadi ◽  
Mohammed F. Allehyani
Keyword(s):  
Machine Learning ◽  
Smart Grid ◽  
Operating Conditions ◽  
Support Vector ◽  
Identification System ◽  
Network Stability ◽  
Grid Network ◽  
Optimized Model ◽  
Smart Grid Network ◽  
Learning Architectures

The heterogeneous and interoperable nature of the cyber-physical system (CPS) has enabled the smart grid (SG) to operate near the stability limits with an inconsiderable accuracy margin. This has imposed the need for more intelligent, predictive, fast, and accurate algorithms that are able to operate the grid autonomously to avoid cascading failures and/or blackouts. In this paper, a new comprehensive identification system is proposed that employs various machine learning architectures for classifying stability records in smart grid networks. Specifically, seven machine learning architectures are investigated, including optimizable support vector machine (SVM), decision trees classifier (DTC), logistic regression classifier (LRC), naïve Bayes classifier (NBC), linear discriminant classifier (LDC), k-nearest neighbor (kNN), and ensemble boosted classifier (EBC). The developed models are evaluated and contrasted in terms of various performance evaluation metrics such as accuracy, precision, recall, harmonic mean, prediction overhead, and others. Moreover, the system performance was evaluated on a recent and significant dataset for smart grid network stability (SGN_Stab2018), scoring a high identification accuracy (99.90%) with low identification overhead (4.17 μSec) for the optimizable SVM architecture. We also provide an in-depth description of our implementation in conjunction with an extensive experimental evaluation as well as a comparison with state-of-the-art models. The comparison outcomes obtained indicate that the optimized model provides a compact and efficient model that can successfully and accurately predict the voltage stability margin (VSM) considering different operating conditions, employing the fewest possible input features. Eventually, the results revealed the competency and superiority of the proposed optimized model over the other available models. The technique also speeds up the training process by reducing the number of simulations on a detailed power system model around operating points where correct predictions are made.

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