Cyberthreats, Attacks and Intrusion Detection in Supervisory Control and Data Acquisition Networks

Supervisory Control and Data Acquisition (SCADA) systems are computer-based process control systems that interconnect and monitor remote physical processes. There have been many real world documented incidents and cyber-attacks affecting SCADA systems, which clearly illustrate critical infrastructure vulnerabilities. These reported incidents demonstrate that cyber-attacks against SCADA systems might produce a variety of financial damage and harmful events to humans and their environment. This dissertation documents four contributions towards increased security for SCADA systems. First, a set of cyber-attacks was developed. Second, each attack was executed against two fully functional SCADA systems in a laboratory environment; a gas pipeline and a water storage tank. Third, signature based intrusion detection system rules were developed and tested which can be used to generate alerts when the aforementioned attacks are executed against a SCADA system. Fourth, a set of features was developed for a decision tree based anomaly based intrusion detection system. The features were tested using the datasets developed for this work. This dissertation documents cyber-attacks on both serial based and Ethernet based SCADA networks. Four categories of attacks against SCADA systems are discussed: reconnaissance, malicious response injection, malicious command injection and denial of service. In order to evaluate performance of data mining and machine learning algorithms for intrusion detection systems in SCADA systems, a network dataset to be used for benchmarking intrusion detection systemswas generated. This network dataset includes different classes of attacks that simulate different attack scenarios on process control systems. This dissertation describes four SCADA network intrusion detection datasets; a full and abbreviated dataset for both the gas pipeline and water storage tank systems. Each feature in the dataset is captured from network flow records. This dataset groups two different categories of features that can be used as input to an intrusion detection system. First, network traffic features describe the communication patterns in a SCADA system. This research developed both signature based IDS and anomaly based IDS for the gas pipeline and water storage tank serial based SCADA systems. The performance of both types of IDS were evaluates by measuring detection rate and the prevalence of false positives

A Comprehensive Survey on the Cyber-Security of Smart Grids: Cyber-Attacks, Detection, Countermeasure Techniques, and Future Directions

One of the significant challenges that smart grid networks face is cyber-security. Several studies have been conducted to highlight those security challenges. However, the majority of these surveys classify attacks based on the security requirements, confidentiality, integrity, and availability, without taking into consideration the accountability requirement. In addition, some of these surveys focused on the Transmission Control Protocol/Internet Protocol (TCP/IP) model, which does not differentiate between the application, session, and presentation and the data link and physical layers of the Open System Interconnection (OSI) model. In this survey paper, we provide a classification of attacks based on the OSI model and discuss in more detail the cyber-attacks that can target the different layers of smart grid networks communication. We also propose new classifications for the detection and countermeasure techniques and describe existing techniques under each category. Finally, we discuss challenges and future research directions

Performance Analysis Of Data-Driven Algorithms In Detecting Intrusions On Smart Grid

The traditional power grid is no longer a practical solution for power delivery due to several shortcomings, including chronic blackouts, energy storage issues, high cost of assets, and high carbon emissions. Therefore, there is a serious need for better, cheaper, and cleaner power grid technology that addresses the limitations of traditional power grids. A smart grid is a holistic solution to these issues that consists of a variety of operations and energy measures. This technology can deliver energy to end-users through a two-way flow of communication. It is expected to generate reliable, efficient, and clean power by integrating multiple technologies. It promises reliability, improved functionality, and economical means of power transmission and distribution. This technology also decreases greenhouse emissions by transferring clean, affordable, and efficient energy to users. Smart grid provides several benefits, such as increasing grid resilience, self-healing, and improving system performance. Despite these benefits, this network has been the target of a number of cyber-attacks that violate the availability, integrity, confidentiality, and accountability of the network. For instance, in 2021, a cyber-attack targeted a U.S. power system that shut down the power grid, leaving approximately 100,000 people without power. Another threat on U.S. Smart Grids happened in March 2018 which targeted multiple nuclear power plants and water equipment. These instances represent the obvious reasons why a high level of security approaches is needed in Smart Grids to detect and mitigate sophisticated cyber-attacks. For this purpose, the US National Electric Sector Cybersecurity Organization and the Department of Energy have joined their efforts with other federal agencies, including the Cybersecurity for Energy Delivery Systems and the Federal Energy Regulatory Commission, to investigate the security risks of smart grid networks. Their investigation shows that smart grid requires reliable solutions to defend and prevent cyber-attacks and vulnerability issues. This investigation also shows that with the emerging technologies, including 5G and 6G, smart grid may become more vulnerable to multistage cyber-attacks. A number of studies have been done to identify, detect, and investigate the vulnerabilities of smart grid networks. However, the existing techniques have fundamental limitations, such as low detection rates, high rates of false positives, high rates of misdetection, data poisoning, data quality and processing, lack of scalability, and issues regarding handling huge volumes of data. Therefore, these techniques cannot ensure safe, efficient, and dependable communication for smart grid networks. Therefore, the goal of this dissertation is to investigate the efficiency of machine learning in detecting cyber-attacks on smart grids. The proposed methods are based on supervised, unsupervised machine and deep learning, reinforcement learning, and online learning models. These models have to be trained, tested, and validated, using a reliable dataset. In this dissertation, CICDDoS 2019 was used to train, test, and validate the efficiency of the proposed models. The results show that, for supervised machine learning models, the ensemble models outperform other traditional models. Among the deep learning models, densely neural network family provides satisfactory results for detecting and classifying intrusions on smart grid. Among unsupervised models, variational auto-encoder, provides the highest performance compared to the other unsupervised models. In reinforcement learning, the proposed Capsule Q-learning provides higher detection and lower misdetection rates, compared to the other model in literature. In online learning, the Online Sequential Euclidean Distance Routing Capsule Network model provides significantly better results in detecting intrusion attacks on smart grid, compared to the other deep online models

A critical review of intrusion detection systems in the internet of things : techniques, deployment strategy, validation strategy, attacks, public datasets and challenges

The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack on the end nodes. To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation strategy, and deployment strategy. This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS. We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT. It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure. These purposes help IoT security researchers by uniting, contrasting, and compiling scattered research efforts. Consequently, we provide a unique IoT IDS taxonomy, which sheds light on IoT IDS techniques, their advantages and disadvantages, IoT attacks that exploit IoT communication systems, corresponding advanced IDS and detection capabilities to detect IoT attacks. © 2021, The Author(s)

IMAT: A Lightweight IoT Network Intrusion Detection System based on Machine Learning techniques

Internet of Things (IoT) is one of the fast-expanding technologies nowadays, and promises to be revolutionary for the near future. IoT systems are in fact an incredible convenience due to centralized and computerized control of any electronic device. This technology allows various physical devices, home applications, vehicles, appliances, etc., to be interconnected and exposed to the Internet. On the other hand, it entails the fundamental need to protect the network from adversarial and unwanted alterations. To prevent such threats it is necessary to appeal to Intrusion Detection Systems (IDS), which can be used in information environments to monitor identified threats or anomalies. The most recent and efficient IDS applications involve the use of Machine Learning (ML) techniques which can automatically detect and prevent malicious attacks, such as distributed denial-of-service (DDoS), which represents a recurring thread to IoT networks in the last years. The work presented on this thesis comes with double purpose: build and test different light Machine Learning models which achieve great performance by running on resource-constrained devices; and at the same time we present a novel Network-based Intrusion Detection System based on the latter devices which can automatically detect IoT attack traffic. Our proposed system consists on deploying small low-powered devices to each component of an IoT environment where each device performs Machine Learning based Intrusion Detection at network level. In this work we describe and train different light-ML models which are tested on Raspberry Pis and FPGAs boards. The performance of such classifiers detecting benign and malicious traffic is presented and compared by response time, accuracy, precision, recall, f1-score and ROC-AUC metrics. The aim of this work is to test these machine learning models on recent datasets with the purpose of finding the most performing ones which can be used for intrusion-defense over IoT environments characterized by high flexibility, easy-installation and efficiency. The obtained results are above 0.99\% of accuracy for different models and they indicate that the proposed system can bring a remarkable layer of security. We show how Machine Learning applied to small low-cost devices is an efficient and versatile combination characterized by a bright future ahead.Internet of Things (IoT) is one of the fast-expanding technologies nowadays, and promises to be revolutionary for the near future. IoT systems are in fact an incredible convenience due to centralized and computerized control of any electronic device. This technology allows various physical devices, home applications, vehicles, appliances, etc., to be interconnected and exposed to the Internet. On the other hand, it entails the fundamental need to protect the network from adversarial and unwanted alterations. To prevent such threats it is necessary to appeal to Intrusion Detection Systems (IDS), which can be used in information environments to monitor identified threats or anomalies. The most recent and efficient IDS applications involve the use of Machine Learning (ML) techniques which can automatically detect and prevent malicious attacks, such as distributed denial-of-service (DDoS), which represents a recurring thread to IoT networks in the last years. The work presented on this thesis comes with double purpose: build and test different light Machine Learning models which achieve great performance by running on resource-constrained devices; and at the same time we present a novel Network-based Intrusion Detection System based on the latter devices which can automatically detect IoT attack traffic. Our proposed system consists on deploying small low-powered devices to each component of an IoT environment where each device performs Machine Learning based Intrusion Detection at network level. In this work we describe and train different light-ML models which are tested on Raspberry Pis and FPGAs boards. The performance of such classifiers detecting benign and malicious traffic is presented and compared by response time, accuracy, precision, recall, f1-score and ROC-AUC metrics. The aim of this work is to test these machine learning models on recent datasets with the purpose of finding the most performing ones which can be used for intrusion-defense over IoT environments characterized by high flexibility, easy-installation and efficiency. The obtained results are above 0.99\% of accuracy for different models and they indicate that the proposed system can bring a remarkable layer of security. We show how Machine Learning applied to small low-cost devices is an efficient and versatile combination characterized by a bright future ahead

ANOMALY NETWORK INTRUSION DETECTION SYSTEM BASED ON DISTRIBUTED TIME-DELAY NEURAL NETWORK (DTDNN)

In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS, U2R, R2L and Probe attack) detected by dynamic neural network. The results indicate that dynamic neural nets (Distributed Time-Delay Artificial Neural Network) can achieve a high detection rate, where the overall accuracy classification rate average is equal to 97.24%

A Survey on Attacks and Preservation Analysis of IDS in Vanet

Vehicular Ad-hoc Networks (VANETs) are the extremely famous enabling network expertise for Smart Transportation Systems. VANETs serve numerous pioneering impressive operations and prospects although transportation preservation and facilitation functions are their basic drivers. Numerous preservation allied VANETs functions are immediate and task imperative, which would entail meticulous assurance of preservation and authenticity. Yet non preservation associated multimedia operations, which would assist an imperative task in the future, would entail preservation assistance. Short of such preservation and secrecy in VANETs is one of the fundamental barriers to the extensive extended implementations of it. An anxious and untrustworthy VANET could be more hazardous than the structure without VANET assistance. So it is imperative to build specific that “life-critical preservation” data is protected adequate to rely on. Securing the VANETs including proper shield of the secrecy drivers or vehicle possessors is an extremely challenging assignment. In this research paper we review the assaults, equivalent preservation entails and objections in VANETs. We as well present the enormously admired common preservation guidelines which are based on avoidance as well recognition methods. Many VANETs operations entail system wide preservation support rather than individual layer from the VANETs’ protocol heap. This paper will also appraise the existing researches in the perception of holistic method of protection. Finally, we serve some potential future trends to attain system-wide preservation with secrecy pleasant preservation in VANETs. Keywords: VANET (Vehicular Ad-hoc Network), Routing algorithm, Vehicle preservation, IDS, attack, Secrec

An Energy Aware and Secure MAC Protocol for Tackling Denial of Sleep Attacks in Wireless Sensor Networks

Wireless sensor networks which form part of the core for the Internet of Things consist of resource constrained sensors that are usually powered by batteries. Therefore, careful energy awareness is essential when working with these devices. Indeed,the introduction of security techniques such as authentication and encryption, to ensure confidentiality and integrity of data, can place higher energy load on the sensors. However, the absence of security protection c ould give room for energy drain attacks such as denial of sleep attacks which have a higher negative impact on the life span ( of the sensors than the presence of security features. This thesis, therefore, focuses on tackling denial of sleep attacks from two perspectives A security perspective and an energy efficiency perspective. The security perspective involves evaluating and ranking a number of security based techniques to curbing denial of sleep attacks. The energy efficiency perspective, on the other hand, involves exploring duty cycling and simulating three Media Access Control ( protocols Sensor MAC, Timeout MAC andTunableMAC under different network sizes and measuring different parameters such as the Received Signal Strength RSSI) and Link Quality Indicator ( Transmit power, throughput and energy efficiency Duty cycling happens to be one of the major techniques for conserving energy in wireless sensor networks and this research aims to answer questions with regards to the effect of duty cycles on the energy efficiency as well as the throughput of three duty cycle protocols Sensor MAC ( Timeout MAC ( and TunableMAC in addition to creating a novel MAC protocol that is also more resilient to denial of sleep a ttacks than existing protocols. The main contributions to knowledge from this thesis are the developed framework used for evaluation of existing denial of sleep attack solutions and the algorithms which fuel the other contribution to knowledge a newly developed protocol tested on the Castalia Simulator on the OMNET++ platform. The new protocol has been compared with existing protocols and has been found to have significant improvement in energy efficiency and also better resilience to denial of sleep at tacks Part of this research has been published Two conference publications in IEEE Explore and one workshop paper

A Survey of Security in UAVs and FANETs: Issues, Threats, Analysis of Attacks, and Solutions

Thanks to the rapidly developing technology, unmanned aerial vehicles (UAVs) are able to complete a number of tasks in cooperation with each other without need for human intervention. In recent years, UAVs, which are widely utilized in military missions, have begun to be deployed in civilian applications and mostly for commercial purposes. With their growing numbers and range of applications, UAVs are becoming more and more popular; on the other hand, they are also the target of various threats which can exploit various vulnerabilities of UAV systems in order to cause destructive effects. It is therefore critical that security is ensured for UAVs and the networks that provide communication between UAVs. In this survey, we aimed to present a comprehensive detailed approach to security by classifying possible attacks against UAVs and flying ad hoc networks (FANETs). We classified the security threats into four major categories that make up the basic structure of UAVs; hardware attacks, software attacks, sensor attacks, and communication attacks. In addition, countermeasures against these attacks are presented in separate groups as prevention and detection. In particular, we focus on the security of FANETs, which face significant security challenges due to their characteristics and are also vulnerable to insider attacks. Therefore, this survey presents a review of the security fundamentals for FANETs, and also four different routing attacks against FANETs are simulated with realistic parameters and then analyzed. Finally, limitations and open issues are also discussed to direct future wor