Fault-Tolerant Secure Data Aggregation Schemes in Smart Grids: Techniques, Design Challenges, and Future Trends

Secure data aggregation is an important process that enables a smart meter to perform efficiently and accurately. However, the fault tolerance and privacy of the user data are the most serious concerns in this process. While the security issues of Smart Grids are extensively studied, these two issues have been ignored so far. Therefore, in this paper, we present a comprehensive survey of fault-tolerant and differential privacy schemes for the Smart Gird. We selected papers from 2010 to 2021 and studied the schemes that are specifically related to fault tolerance and differential privacy. We divided all existing schemes based on the security properties, performance evaluation, and security attacks. We provide a comparative analysis for each scheme based on the cryptographic approach used. One of the drawbacks of existing surveys on the Smart Grid is that they have not discussed fault tolerance and differential privacy as a major area and consider them only as a part of privacy preservation schemes. On the basis of our work, we identified further research areas that can be explored

Privacy Preservation & Security Solutions in Blockchain Network

Blockchain has seen exponential progress over the past few years, and today its usage extends well beyond cryptocurrencies. Its features, including openness, transparency, secure communication, difficult falsification, and multi-consensus, have made it one of the most valuable technology in the world. In most open blockchain platforms, any node can access the data on the blockchain, which leads to a potential risk of personal information leakage. So the issue of blockchain privacy and security is particularly prominent and has become an important research topic in the field of blockchain. This dissertation mainly summarizes my research on blockchain privacy and security protection issues throughout recent years. We first summarize the security and privacy vulnerabilities in the mining pools of traditional bitcoin networks and some possible protection measures. We then propose a new type of attack: coin hopping attack, in the case of multiple blockchains under an IoT environment. This attack is only feasible in blockchain-based IoT scenarios, and can significantly reduce the operational efficiency of the entire blockchain network in the long run. We demonstrate the feasibility of this attack by theoretical analysis of four different attack models and propose two possible solutions. We also propose an innovative hybrid blockchain crowdsourcing platform solution to settle the performance bottlenecks and various challenges caused by privacy, scalability, and verification efficiency problems of current blockchain-based crowdsourcing systems. We offer flexible task-based permission control and a zero-knowledge proof mechanism in the implementation of smart contracts to flexibly obtain different levels of privacy protection. By performing several tests on Ethereum and Hyperledger Fabric, EoS.io blockchains, the performance of the proposed platform consensus under different transaction volumes is verified. At last, we also propose further investigation on the topics of the privacy issues when combining AI with blockchain and propose some defense strategies

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Security and Privacy in Smart Grid

Smart grid utilizes different communication technologies to enhance the reliability and efficiency of the power grid; it allows bi-directional flow of electricity and information, about grid status and customers requirements, among different parties in the grid, i.e., connect generation, distribution, transmission, and consumption subsystems together. Thus, smart grid reduces the power losses and increases the efficiency of electricity generation and distribution. Although smart grid improves the quality of grid's services, it exposes the grid to the cyber security threats that communication networks suffer from in addition to other novel threats because of power grid's nature. For instance, the electricity consumption messages sent from consumers to the utility company via wireless network may be captured, modified, or replayed by adversaries. As a consequent, security and privacy concerns are significant challenges in smart grid. Smart grid upgrade creates three main communication architectures: The first one is the communication between electricity customers and utility companies via various networks; i.e., home area networks (HANs), building area networks (BANs), and neighbour area networks (NANs), we refer to these networks as customer-side networks in our thesis. The second architecture is the communication between EVs and grid to charge/discharge their batteries via vehicle-to-grid (V2G) connection. The last network is the grid's connection with measurements units that spread all over the grid to monitor its status and send periodic reports to the main control center (CC) for state estimation and bad data detection purposes. This thesis addresses the security concerns for the three communication architectures. For customer-side networks, the privacy of consumers is the central concern for these networks; also, the transmitted messages integrity and confidentiality should be guaranteed. While the main security concerns for V2G networks are the privacy of vehicle's owners besides the authenticity of participated parties. In the grid's connection with measurements units, integrity attacks, such as false data injection (FDI) attacks, target the measurements' integrity and consequently mislead the main CC to make the wrong decisions for the grid. The thesis presents two solutions for the security problems in the first architecture; i.e., the customer-side networks. The first proposed solution is security and privacy-preserving scheme in BAN, which is a cluster of HANs. The proposed scheme is based on forecasting the future electricity demand for the whole BAN cluster. Thus, BAN connects to the electricity provider only if the total demand of the cluster is changed. The proposed scheme employs the lattice-based public key NTRU crypto-system to guarantee the confidentiality and authenticity of the exchanged messages and to further reduce the computation and communication load. The security analysis shows that our proposed scheme can achieve the privacy and security requirements. In addition, it efficiently reduces the communication and computation overhead. According to the second solution, it is lightweight privacy-preserving aggregation scheme that permits the smart household appliances to aggregate their readings without involving the connected smart meter. The scheme deploys a lightweight lattice-based homomorphic crypto-system that depends on simple addition and multiplication operations. Therefore, the proposed scheme guarantees the customers' privacy and message integrity with lightweight overhead. In addition, the thesis proposes lightweight secure and privacy-preserving V2G connection scheme, in which the power grid assures the confidentiality and integrity of exchanged information during (dis)charging electricity sessions and overcomes EVs' authentication problem. The proposed scheme guarantees the financial profits of the grid and prevents EVs from acting maliciously. Meanwhile, EVs preserve their private information by generating their own pseudonym identities. In addition, the scheme keeps the accountability for the electricity-exchange trade. Furthermore, the proposed scheme provides these security requirements by lightweight overhead; as it diminishes the number of exchanged messages during (dis)charging sessions. Simulation results demonstrate that the proposed scheme significantly reduces the total communication and computation load for V2G connection especially for EVs. FDI attack, which is one of the severe attacks that threatens the smart grid's efficiency and reliability, inserts fake measurements among the correct ones to mislead CC to make wrong decisions and consequently impact on the grid's performance. In the thesis, we have proposed an FDI attack prevention technique that protects the integrity and availability of the measurements at measurement units and during their transmission to the CC, even with the existence of compromised units. The proposed scheme alleviates the negative impacts of FDI attack on grid's performance. Security analysis and performance evaluation show that our scheme guarantees the integrity and availability of the measurements with lightweight overhead, especially on the restricted-capabilities measurement units. The proposed schemes are promising solutions for the security and privacy problems of the three main communication networks in smart grid. The novelty of these proposed schemes does not only because they are robust and efficient security solutions, but also due to their lightweight communication and computation overhead, which qualify them to be applicable on limited-capability devices in the grid. So, this work is considered important progress toward more reliable and authentic smart grid

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed

A Blockchain-Based Retribution Mechanism for Collaborative Intrusion Detection

Collaborative intrusion detection approach uses the shared detection signature between the collaborative participants to facilitate coordinated defense. In the context of collaborative intrusion detection system (CIDS), however, there is no research focusing on the efficiency of the shared detection signature. The inefficient detection signature costs not only the IDS resource but also the process of the peer-to-peer (P2P) network. In this paper, we therefore propose a blockchain-based retribution mechanism, which aims to incentivize the participants to contribute to verifying the efficiency of the detection signature in terms of certain distributed consensus. We implement a prototype using Ethereum blockchain, which instantiates a token-based retribution mechanism and a smart contract-enabled voting-based distributed consensus. We conduct a number of experiments built on the prototype, and the experimental results demonstrate the effectiveness of the proposed approach

Robustness of Image-Based Malware Analysis

In previous work, “gist descriptor” features extracted from images have been used in malware classification problems and have shown promising results. In this research, we determine whether gist descriptors are robust with respect to malware obfuscation techniques, as compared to Convolutional Neural Networks (CNN) trained directly on malware images. Using the Python Image Library (PIL), we create images from malware executables and from malware that we obfuscate. We conduct experiments to compare classifying these images with a CNN as opposed to extracting the gist descriptor features from these images to use in classification. For the gist descriptors, we consider a variety of classification algorithms including k-nearest neighbors, random forest, support vector machine, and multi-layer perceptron. We find that gist descriptors are more robust than CNNs, with respect to the obfuscation techniques that we consider

Twitter Bots’ Detection with Benford’s Law and Machine Learning

Online Social Networks (OSNs) have grown exponentially in terms of active users and have now become an influential factor in the formation of public opinions. For this reason, the use of bots and botnets for spreading misinformation on OSNs has become a widespread concern. Identifying bots and botnets on Twitter can require complex statistical methods to score a profile based on multiple features. Benford’s Law, or the Law of Anomalous Numbers, states that, in any naturally occurring sequence of numbers, the First Significant Leading Digit (FSLD) frequency follows a particular pattern such that they are unevenly distributed and reducing. This principle can be applied to the first-degree egocentric network of a Twitter profile to assess its conformity to such law and, thus, classify it as a bot profile or normal profile. This paper focuses on leveraging Benford’s Law in combination with various Machine Learning (ML) classifiers to identify bot profiles on Twitter. In addition, a comparison with other statistical methods is produced to confirm our classification results

Impact of Location Spoofing Attacks on Performance Prediction in Mobile Networks

Performance prediction in wireless mobile networks is essential for diverse purposes in network management and operation. Particularly, the position of mobile devices is crucial to estimating the performance in the mobile communication setting. With its importance, this paper investigates mobile communication performance based on the coordinate information of mobile devices. We analyze a recent 5G data collection and examine the feasibility of location-based performance prediction. As location information is key to performance prediction, the basic assumption of making a relevant prediction is the correctness of the coordinate information of devices given. With its criticality, this paper also investigates the impact of position falsification on the ML-based performance predictor, which reveals the significant degradation of the prediction performance under such attacks, suggesting the need for effective defense mechanisms against location spoofing threats