Cyber-physical attacks are the main substantial threats facing the utilization and development of the various smart grid technologies. Among these attacks, false data injection attack represents a main category with its widely varied types and impacts that have been extensively reported recently. In addressing this threat, several detection algorithms have been developed in the last few years. These were either model-based or data-driven algorithms. This paper provides an intensive summary of these algorithms by categorizing them and elaborating on the pros and cons of each category. The paper starts by introducing the various cyber-physical attacks along with the main reported incidents in history. The significance and the impacts of the false data injection attacks are then reported. The concluding remarks present the main criteria that should be considered in developing future detection algorithms for the false data injection attacks.

A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids

Deep learning and big data technologies for IoT security

Supervisory Control and Data Acquisition (SCADA) systems are the underlying monitoring and control components of critical infrastructures, such as power, telecommunication, transportation, pipelines, chemicals and manufacturing plants. Legacy SCADA systems operated on isolated networks, that made them less exposed to Internet threats. However, the increasing connection of SCADA systems to the Internet, as well as corporate networks, introduces severe security issues. Security considerations for SCADA systems are gaining higher attention, as the number of security incidents against these critical infrastructures is increasing. In this survey, we provide an overview of the general SCADA architecture, along with a detailed description of the SCADA communication protocols. Additionally, we discuss certain high-impact security incidents, objectives, and threats. Furthermore, we carry out an extensive review of the security proposals and tactics that aim to secure SCADA systems. We also discuss the state of SCADA system security. Finally, we present the current research trends and future advancements of SCADA security.

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A Survey on SCADA Systems: Secure Protocols, Incidents, Threats and Tactics

Despite the growing popularity of machine learning models in the cyber-security applications (e.g., an intrusion detection system (IDS)), most of these models are perceived as a black-box. The eXplainable Artificial Intelligence (XAI) has become increasingly important to interpret the machine learning models to enhance trust management by allowing human experts to understand the underlying data evidence and causal reasoning. According to IDS, the critical role of trust management is to understand the impact of the malicious data to detect any intrusion in the system. The previous studies focused more on the accuracy of the various classification algorithms for trust in IDS. They do not often provide insights into their behavior and reasoning provided by the sophisticated algorithm. Therefore, in this paper, we have addressed XAI concept to enhance trust management by exploring the decision tree model in the area of IDS. We use simple decision tree algorithms that can be easily read and even resemble a human approach to decision-making by splitting the choice into many small subchoices for IDS. We experimented with this approach by extracting rules in a widely used KDD benchmark dataset. We also compared the accuracy of the decision tree approach with the other state-of-the-art algorithms.

/pdf/explainable-artificial-intelligence-xai-to-enhance-trust-4wssaf3qkz.pdf

Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model

Recently, the huge amounts of data and its incremental increase have changed the importance of information security and data analysis systems for Big Data Intrusion detection system (IDS) is a system that monitors and analyzes data to detect any intrusion in the system or network High volume, variety and high speed of data generated in the network have made the data analysis process to detect attacks by traditional techniques very difficult Big Data techniques are used in IDS to deal with Big Data for accurate and efficient data analysis process This paper introduced Spark-Chi-SVM model for intrusion detection In this model, we have used ChiSqSelector for feature selection, and built an intrusion detection model by using support vector machine (SVM) classifier on Apache Spark Big Data platform We used KDD99 to train and test the model In the experiment, we introduced a comparison between Chi-SVM classifier and Chi-Logistic Regression classifier The results of the experiment showed that Spark-Chi-SVM model has high performance, reduces the training time and is efficient for Big Data

/pdf/intrusion-detection-model-using-machine-learning-algorithm-1tp6hqf2jb.pdf

Intrusion detection model using machine learning algorithm on Big Data environment

Technological advancement enables the need of internet everywhere. The power industry is not an exception in the technological advancement which makes everything smarter. Smart grid is the advanced version of the traditional grid, which makes the system more efficient and self-healing. Synchrophasor is a device used in smart grids to measure the values of electric waves, voltages and current. The phasor measurement unit produces immense volume of current and voltage data that is used to monitor and control the performance of the grid. These data are huge in size and vulnerable to attacks. Intrusion Detection is a common technique for finding the intrusions in the system. In this paper, a big data framework is designed using various machine learning techniques, and intrusions are detected based on the classifications applied on the synchrophasor dataset. In this approach various machine learning techniques like deep neural networks, support vector machines, random forest, decision trees and naive bayes classifications are done for the synchrophasor dataset and the results are compared using metrics of accuracy, recall, false rate, specificity, and prediction time. Feature selection and dimensionality reduction algorithms are used to reduce the prediction time taken by the proposed approach. This paper uses apache spark as a platform which is suitable for the implementation of Intrusion Detection system in smart grids using big data analytics.

A big data framework for intrusion detection in smart grids using apache spark

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A big data framework for intrusion detection in smart grids using apache spark