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

Security protocols suite for machine-to-machine systems

Nowadays, the great diffusion of advanced devices, such as smart-phones, has shown that there is a growing trend to rely on new technologies to generate and/or support progress; the society is clearly ready to trust on next-generation communication systems to face today’s concerns on economic and social fields. The reason for this sociological change is represented by the fact that the technologies have been open to all users, even if the latter do not necessarily have a specific knowledge in this field, and therefore the introduction of new user-friendly applications has now appeared as a business opportunity and a key factor to increase the general cohesion among all citizens. Within the actors of this technological evolution, wireless machine-to-machine (M2M) networks are becoming of great importance. These wireless networks are made up of interconnected low-power devices that are able to provide a great variety of services with little or even no user intervention. Examples of these services can be fleet management, fire detection, utilities consumption (water and energy distribution, etc.) or patients monitoring. However, since any arising technology goes together with its security threats, which have to be faced, further studies are necessary to secure wireless M2M technology. In this context, main threats are those related to attacks to the services availability and to the privacy of both the subscribers’ and the services providers’ data. Taking into account the often limited resources of the M2M devices at the hardware level, ensuring the availability and privacy requirements in the range of M2M applications while minimizing the waste of valuable resources is even more challenging. Based on the above facts, this Ph. D. thesis is aimed at providing efficient security solutions for wireless M2M networks that effectively reduce energy consumption of the network while not affecting the overall security services of the system. With this goal, we first propose a coherent taxonomy of M2M network that allows us to identify which security topics deserve special attention and which entities or specific services are particularly threatened. Second, we define an efficient, secure-data aggregation scheme that is able to increase the network lifetime by optimizing the energy consumption of the devices. Third, we propose a novel physical authenticator or frame checker that minimizes the communication costs in wireless channels and that successfully faces exhaustion attacks. Fourth, we study specific aspects of typical key management schemes to provide a novel protocol which ensures the distribution of secret keys for all the cryptographic methods used in this system. Fifth, we describe the collaboration with the WAVE2M community in order to define a proper frame format actually able to support the necessary security services, including the ones that we have already proposed; WAVE2M was funded to promote the global use of an emerging wireless communication technology for ultra-low and long-range services. And finally sixth, we provide with an accurate analysis of privacy solutions that actually fit M2M-networks services’ requirements. All the analyses along this thesis are corroborated by simulations that confirm significant improvements in terms of efficiency while supporting the necessary security requirements for M2M networks

Defense and traceback mechanisms in opportunistic wireless networks

 In this thesis, we have identified a novel attack in OppNets, a special type of packet dropping attack where the malicious node(s) drops one or more packets (not all the packets) and then injects new fake packets instead. We name this novel attack as the Catabolism attack and propose a novel attack detection and traceback approach against this attack referred to as the Anabolism defence. As part of the Anabolism defence approach we have proposed three techniques: time-based, Merkle tree based and Hash chain based techniques for attack detection and malicious node(s) traceback. We provide mathematical models that show our novel detection and traceback mechanisms to be very effective and detailed simulation results show our defence mechanisms to achieve a very high accuracy and detection rate

Security and Privacy Preservation in Mobile Social Networks

Social networking extending the social circle of people has already become an important integral part of our daily lives. As reported by ComScore, social networking sites such as Facebook and Twitter have reached 82 percent of the world's online population, representing 1.2 billion users around the world. In the meantime, fueled by the dramatic advancements of smartphones and the ubiquitous connections of Bluetooth/WiFi/3G/LTE networks, social networking further becomes available for mobile users and keeps them posted on the up-to-date worldwide news and messages from their friends and families anytime anywhere. The convergence of social networking, advanced smartphones, and stable network infrastructures brings us a pervasive and omnipotent communication platform, named mobile social network (MSN), helping us stay connected better than ever. In the MSN, multiple communication techniques help users to launch a variety of applications in multiple communication domains including single-user domain, two-user domain, user-chain domain, and user-star domain. Within different communication domains, promising mobile applications are fostered. For example, nearby friend search application can be launched in the two-user or user-chain domains to help a user find other physically-close peers who have similar interests and preferences; local service providers disseminate advertising information to nearby users in the user-star domain; and health monitoring enables users to check the physiological signals in the single-user domain. Despite the tremendous benefits brought by the MSN, it still faces many technique challenges among of which security and privacy protections are the most important ones as smartphones are vulnerable to security attacks, users easily neglect their privacy preservation, and mutual trust relationships are difficult to be established in the MSN. In this thesis, we explore the unique characteristics and study typical research issues of the MSN. We conduct our research with a focus on security and privacy preservation while considering human factors. Specifically, we consider the profile matching application in the two-user domain, the cooperative data forwarding in the user-chain domain, the trustworthy service evaluation application in the user-star domain, and the healthcare monitoring application in the single-user domain. The main contributions are, i) considering the human comparison behavior and privacy requirements, we first propose a novel family of comparison-based privacy-preserving profile matching (PPM) protocols. The proposed protocols enable two users to obtain comparison results of attribute values in their profiles, while the attribute values are not disclosed. Taking user anonymity requirement as an evaluation metric, we analyze the anonymity protection of the proposed protocols. From the analysis, we found that the more comparison results are disclosed, the less anonymity protection is achieved by the protocol. Further, we explore the pseudonym strategy and an anonymity enhancing technique where users could be self-aware of the anonymity risk level and take appropriate actions when needed; ii) considering the inherent MSN nature --- opportunistic networking, we propose a cooperative privacy-preserving data forwarding (PDF) protocol to help users forward data to other users. We indicate that privacy and effective data forwarding are two conflicting goals: the cooperative data forwarding could be severely interrupted or even disabled when the privacy preservation of users is applied, because without sharing personal information users become unrecognizable to each other and the social interactions are no longer traceable. We explore the morality model of users from classic social theory, and use game-theoretic approach to obtain the optimal data forwarding strategy. Through simulation results, we show that the proposed cooperative data strategy can achieve both the privacy preservation and the forwarding efficiency; iii) to establish the trust relationship in a distributed MSN is a challenging task. We propose a trustworthy service evaluation (TSE) system, to help users exchange their service reviews toward local vendors. However, vendors and users could be the potential attackers aiming to disrupt the TSE system. We then consider the review attacks, i.e., vendors rejecting and modifying the authentic reviews of users, and the Sybil attacks, i.e., users abusing their pseudonyms to generate fake reviews. To prevent these attacks, we explore the token technique, the aggregate signature, and the secret sharing techniques. Simulation results show the security and the effectiveness of the TSE system can be guaranteed; iv) to improve the efficiency and reliability of communications in the single-user domain, we propose a prediction-based secure and reliable routing framework (PSR). It can be integrated with any specific routing protocol to improve the latter's reliability and prevent data injection attacks during data communication. We show that the regularity of body gesture can be learned and applied by body sensors such that the route with the highest predicted link quality can always be chose for data forwarding. The security analysis and simulation results show that the PSR significantly increases routing efficiency and reliability with or without the data injection attacks

A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks.

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraints, there is a need to develop a routing protocol to fulfill WMSN requirements in terms of delivery reliability, attack resiliency, computational overhead and energy efficiency. This doctoral research therefore aims to advance the state of the art in routing by proposing a lightweight, reliable routing protocol for WMSN. Ensuring a reliable path between the source and the destination requires making trustaware routing decisions to avoid untrustworthy paths. A lightweight and effective Trust Management System (TMS) has been developed to evaluate the trust relationship between the sensor nodes with a view to differentiating between trustworthy nodes and untrustworthy ones. Moreover, a resource-conservative Reinforcement Learning (RL) model has been proposed to reduce the computational overhead, along with two updating methods to speed up the algorithm convergence. The reward function is re-defined as a punishment, combining the proposed trust management system to defend against well-known dropping attacks. Furthermore, with a view to addressing the inborn overestimation problem in Q-learning-based routing protocols, we adopted double Q-learning to overcome the positive bias of using a single estimator. An energy model is integrated with the reward function to enhance the network lifetime and balance energy consumption across the network. The proposed energy model uses only local information to avoid the resource burdens and the security concerns of exchanging energy information. Finally, a realistic trust management testbed has been developed to overcome the limitations of using numerical analysis to evaluate proposed trust management schemes, particularly in the context of WMSN. The proposed testbed has been developed as an additional module to the NS-3 simulator to fulfill usability, generalisability, flexibility, scalability and high-performance requirements

A patient agent controlled customized blockchain based framework for internet of things

Although Blockchain implementations have emerged as revolutionary technologies for various industrial applications including cryptocurrencies, they have not been widely deployed to store data streaming from sensors to remote servers in architectures known as Internet of Things. New Blockchain for the Internet of Things models promise secure solutions for eHealth, smart cities, and other applications. These models pave the way for continuous monitoring of patient’s physiological signs with wearable sensors to augment traditional medical practice without recourse to storing data with a trusted authority. However, existing Blockchain algorithms cannot accommodate the huge volumes, security, and privacy requirements of health data. In this thesis, our first contribution is an End-to-End secure eHealth architecture that introduces an intelligent Patient Centric Agent. The Patient Centric Agent executing on dedicated hardware manages the storage and access of streams of sensors generated health data, into a customized Blockchain and other less secure repositories. As IoT devices cannot host Blockchain technology due to their limited memory, power, and computational resources, the Patient Centric Agent coordinates and communicates with a private customized Blockchain on behalf of the wearable devices. While the adoption of a Patient Centric Agent offers solutions for addressing continuous monitoring of patients’ health, dealing with storage, data privacy and network security issues, the architecture is vulnerable to Denial of Services(DoS) and single point of failure attacks. To address this issue, we advance a second contribution; a decentralised eHealth system in which the Patient Centric Agent is replicated at three levels: Sensing Layer, NEAR Processing Layer and FAR Processing Layer. The functionalities of the Patient Centric Agent are customized to manage the tasks of the three levels. Simulations confirm protection of the architecture against DoS attacks. Few patients require all their health data to be stored in Blockchain repositories but instead need to select an appropriate storage medium for each chunk of data by matching their personal needs and preferences with features of candidate storage mediums. Motivated by this context, we advance third contribution; a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The mapping between health data features and characteristics of each repository is learned using machine learning. The Blockchain’s capacity to make transactions and store records without central oversight enables its application for IoT networks outside health such as underwater IoT networks where the unattended nature of the nodes threatens their security and privacy. However, underwater IoT differs from ground IoT as acoustics signals are the communication media leading to high propagation delays, high error rates exacerbated by turbulent water currents. Our fourth contribution is a customized Blockchain leveraged framework with the model of Patient-Centric Agent renamed as Smart Agent for securely monitoring underwater IoT. Finally, the smart Agent has been investigated in developing an IoT smart home or cities monitoring framework. The key algorithms underpinning to each contribution have been implemented and analysed using simulators.Doctor of Philosoph

Security of Software-defined Wireless Sensor Networks

Wireless Sensor Network (WSN) using Software Defined Networking (SDN) can achieve several advantages such as flexible and centralized network management and efficient routing. This is because SDN is a logically centralized architecture that separates the control plane from the data plane. SDN can provide security solutions, such as routing isolation, while handling the heterogeneity, scalability, and the limited resources of WSNs. However, such centralized architecture brings new challenges due to the single attack point and having non-dedicated channels for the control plane in WSNs. In this thesis, we investigate and propose security solutions for software-defined WSNs considering energy-efficiency and resource-preservation. The details are as follows. First, the functionality of software-defined WSNs can be affected by malicious sensor nodes that perform arbitrary actions such as message dropping or flooding. The malicious nodes can degrade the availability of the network due to in-band communications and the inherent lack of secure channels in software-defined WSNs. Therefore, we design a hierarchical trust management scheme for software-defined WSNs (namely TSW) to detect potential threats inside software-defined WSNs while promoting node cooperation and supporting decision-making in the forwarding process. The TSW scheme evaluates the trustworthiness of involved nodes and enables the detection of malicious behavior at various levels of the software-defined WSN architecture. We develop sensitive trust computational models to detect several malicious attacks. Furthermore, we propose separate trust scores and parameters for control and data traffic, respectively, to enhance the detection performance against attacks directed at the crucial traffic of the control plane. Additionally, we develop an acknowledgment-based trust recording mechanism by exploiting some built-in SDN control messages. To ensure the resilience and honesty of the trust scores, a weighted averaging approach is adopted, and a reliability trust metric is also defined. Through extensive analyses and numerical simulations, we demonstrate that TSW is efficient in detecting malicious nodes that launch several communication and trust management threats such as black-hole, selective forwarding, denial of service, bad and good mouthing, and ON-OFF attacks. Second, network topology obfuscation is generally considered a proactive mechanism for mitigating traffic analysis attacks. The main challenge is to strike a balance among energy consumption, reliable routing, and security levels due to resource constraints in sensor nodes. Furthermore, software-defined WSNs are more vulnerable to traffic analysis attacks due to the uncovered pattern of control traffic between the controller and the nodes. As a result, we propose a new energy-aware network topology obfuscation mechanism, which maximizes the attack costs and is efficient and practical to be deployed. Specifically, first, a route obfuscation method is proposed by utilizing ranking-based route mutation, based on four different critical criteria: route overlapping, energy consumption, link costs, and node reliability. Then, a sink node obfuscation method is introduced by selecting several fake sink nodes that are indistinguishable from actual sink nodes, according to the k-anonymity model. As a result, the most suitable routes and sink nodes can be selected, and a highest obfuscation level can be reached without sacrificing energy efficiency. Finally, extensive simulation results demonstrate that the proposed methods strongly mitigate traffic analysis attacks and achieve effective network topology obfuscation for software-defined WSNs. In addition, the proposed methods reduce the success rate of the attacks while achieving lower energy consumption and longer network lifetime. Last, security networking functions, such as trust management and Intrusion Detection System (IDS), are deployed in WSNs to protect the network from multiple attacks. However, there are many resource and security challenges in deploying these functions. First, they consume tremendous nodes’ energy and computational resources, which are limited in WSNs. Another challenge is preserving the security at a sufficient level in terms of reliability and coverage. Watchdog nodes, as one of the main components in trust management, overhear and monitor other nodes in the network. Accordingly, a secure and energy-aware watchdog placement optimization solution is studied for software-defined WSNs. The solution balances the required energy consumption, computational resource, and security in terms of the honesty of the watchdog nodes. To this end, a multi-population genetic algorithm is proposed for the optimal placement of the watchdog function in the network given the comprehensive aspects of resources and security. Finally, simulation results demonstrate that the proposed solution robustly preserves security levels and achieves energy-efficient deployment. In summary, reactive and proactive security solutions are investigated, designed, and evaluated for software-defined WSNs. The novelty of these proposed solutions is not only efficient and robust security but also their energy awareness, which allows them to be practical on resource-constrained networks. Thus, this thesis is considered a significant advancement toward more trustworthy and dependable software-defined WSNs

A Fog Computing Approach for Cognitive, Reliable and Trusted Distributed Systems

In the Internet of Things era, a big volume of data is generated/gathered every second from billions of connected devices. The current network paradigm, which relies on centralised data centres (a.k.a. Cloud computing), becomes an impractical solution for IoT data storing and processing due to the long distance between the data source (e.g., sensors) and designated data centres. It worth noting that the long distance in this context refers to the physical path and time interval of when data is generated and when it get processed. To explain more, by the time the data reaches a far data centre, the importance of the data can be depreciated. Therefore, the network topologies have evolved to permit data processing and storage at the edge of the network, introducing what so-called fog Computing. The later will obviously lead to improvements in quality of service via processing and responding quickly and efficiently to varieties of data processing requests. Although fog computing is recognized as a promising computing paradigm, it suffers from challenging issues that involve: i) concrete adoption and management of fogs for decentralized data processing. ii) resources allocation in both cloud and fog layers. iii) having a sustainable performance since fog have a limited capacity in comparison with cloud. iv) having a secure and trusted networking environment for fogs to share resources and exchange data securely and efficiently. Hence, the thesis focus is on having a stable performance for fog nodes by enhancing resources management and allocation, along with safety procedures, to aid the IoT-services delivery and cloud computing in the ever growing industry of smart things. The main aspects related to the performance stability of fog computing involves the development of cognitive fog nodes that aim at provide fast and reliable services, efficient resources managements, and trusted networking, and hence ensure the best Quality of Experience, Quality of Service and Quality of Protection to end-users. Therefore the contribution of this thesis in brief is a novel Fog Resource manAgeMEnt Scheme (FRAMES) which has been proposed to crystallise fog distribution and resource management with an appropriate service's loads distribution and allocation based on the Fog-2-Fog coordination. Also, a novel COMputIng Trust manageMENT (COMITMENT) which is a software-based approach that is responsible for providing a secure and trusted environment for fog nodes to share their resources and exchange data packets. Both FRAMES and COMITMENT are encapsulated in the proposed Cognitive Fog (CF) computing which aims at making fog able to not only act on the data but also interpret the gathered data in a way that mimics the process of cognition in the human mind. Hence, FRAMES provide CF with elastic resource managements for load balancing and resolving congestion, while the COMITMENT employ trust and recommendations models to avoid malicious fog nodes in the Fog-2-Fog coordination environment. The proposed algorithms for FRAMES and COMITMENT have outperformed the competitive benchmark algorithms, namely Random Walks Offloading (RWO) and Nearest Fog Offloading (NFO) in the experiments to verify the validity and performance. The experiments were conducted on the performance (in terms of latency), load balancing among fog nodes and fogs trustworthiness along with detecting malicious events and attacks in the Fog-2-Fog environment. The performance of the proposed FRAMES's offloading algorithms has the lowest run-time (i.e., latency) against the benchmark algorithms (RWO and NFO) for processing equal-number of packets. Also, COMITMENT's algorithms were able to detect the collaboration requests whether they are secure, malicious or anonymous. The proposed work shows potential in achieving a sustainable fog networking paradigm and highlights significant benefits of fog computing in the computing ecosystem