The smart grid is an upgraded concept of electricity network with tight coupling among information, control, and bi-directional communication technologies. Along with the silent features of the on-going smart grid, cyber–physical security appears to be a deep concern due to its significant dependence on sensing technologies, complex networks of computers, intelligence, and wide-area communication infrastructures. Moreover, the smart grid is an extensive critical infrastructure and vulnerable to coordinated cyber–physical attacks. As a result, cyber–physical attacks cause significant threats to the confidentiality and integrity of the power data, including power outages, cascading failures, and unnecessary expenditure. In this study, security issues of smart grid, including cyber–physical interdependency, attack varieties, detection methods, requirements, standards, challenges, and future prospects, are taken into consideration for both cyber and physical systems, aiming to provide an extensive understanding of vulnerabilities and solutions for the smart power grid. By revealing the inherent features of cyber–physical security in the smart grid, this survey study is addressed to facilitate future research in these two areas.

Cyber–physical security for on-going smart grid initiatives: a survey

Secure semantic searching provides privacy-preserving information retrieval for arbitrary queries semantically related to keywords. However, current verification mechanisms cannot verify the correctness of search results for semantically related queries in mutual distrust scenarios. Moreover, most secure semantic searching schemes still perform exact matching on ciphertext after query expansion, then directly accumulate the weights of the matched keywords as similarity measurements to rank the documents, resulting in the search results with unsatisfied ranking. In this paper, we propose a secure heuristic semantic searching scheme, in which a privacy-preserving word nonlinear matching (PPWNM) method is developed, and a blockchain-based verification is designed to obtain trustworthy search results with high retrieval accuracy. Specifically, we describe several retrieval heuristics and formulate them as a word nonlinear matching problem, then transform it to the PPWNM problem for calculating the similarity measurement between the query and document so that the highly accurate ranked results can be obtained. Also we design a blockchain-based verification mechanism in which the blockchain nodes utilize the proofs generated during the matching process to verify the correctness of the search results and reach a consensus to ensure the trustworthy results and fair payment between the participants in a mutual distrust model. Our security analysis and experimental results show that the proposed scheme is secure and has higher accuracy compared with the existing other schemes.

A secure heuristic semantic searching scheme with blockchain-based verification

Machine learning is a field of artificial intelligence (AI) that is becoming essential for several critical systems, making it a good target for threat actors. Threat actors exploit different Tactics, Techniques, and Procedures (TTPs) against the confidentiality, integrity, and availability of Machine Learning (ML) systems. During the ML cycle, they exploit adversarial TTPs to poison data and fool ML-based systems. In recent years, multiple security practices have been proposed for traditional systems but they are not enough to cope with the nature of ML-based systems. In this paper, we conduct an empirical study of threats reported against ML-based systems with the aim to understand and characterize the nature of ML threats and identify common mitigation strategies. The study is based on 89 real-world ML attack scenarios from the MITRE's ATLAS database, the AI Incident Database, and the literature; 854 ML repositories from the GitHub search and the Python Packaging Advisory database, selected based on their reputation. Attacks from the AI Incident Database and the literature are used to identify vulnerabilities and new types of threats that were not documented in ATLAS. Results show that convolutional neural networks were one of the most targeted models among the attack scenarios. ML repositories with the largest vulnerability prominence include TensorFlow, OpenCV, and Notebook. In this paper, we also report the most frequent vulnerabilities in the studied ML repositories, the most targeted ML phases and models, the most used TTPs in ML phases and attack scenarios. This information is particularly important for red/blue teams to better conduct attacks/defenses, for practitioners to prevent threats during ML development, and for researchers to develop efficient defense mechanisms.

Threat Assessment in Machine Learning based Systems

Mobile Cloud computing architecture for massively parallelizable geometric computation

Nowadays, an increasing number of applications, services, and devices are shifting towards a less centralized cloud computing model. Cloud providers allow customers to deploy applications across multiple regions of their infrastructures to achieve high availability, be compliant with data privacy laws and regulations, and provide more robustness. Although an application can be deployed in different areas, it still has to behave as a single service that undergoes the same access control policies. However, verifying access control is a challenging task since the desired requirements are often in conflict (i.e., high availability and strict consistency) according to the use-case scenario taken into account. Different solutions have been proposed to distribute access control policies that are used to grant or deny user requests to access data or services. In this direction, Distributed Ledger Technologies (DLTs) seem to be a promising solution to distribute policies across different regions while still enabling their integrity, authenticity, and confidentiality. In this work, we propose an access control framework that uses IOTA, a novel DLT implemented through a directed acyclic graph named Tangle, to distribute policies. We implemented a prototype of our solution and compared it with its corresponding where the Tangle was replaced by a globally distributed NoSQL database. 

Evaluating Tangle Distributed Ledger for Access Control Policy Distribution in Multi-region Cloud Environments

/pdf/general-access-control-guidance-for-cloud-systems-1wf8md8oak.pdf

General Access Control Guidance for Cloud Systems

With the integration of advanced communication technologies, the power grid is expected to greatly enhance efficiency and reliability of future energy delivery. However, since most devices in electric substations are connected via communication networks, the security of these communication networks becomes a critical issue. Real-World incidents, e.g., Stuxnet and the 2015 Ukraine power grid cyber attack, have demonstrated the feasibility of compromising devices in the power grid network to launch further sophisticated attacks. To deal with security attacks of this spirit, this paper aims to hide critical targets from compromised internal nodes and hence protect them from further attacks launched by those compromised nodes. In particular, we consider substation networks and propose to add carefullycontrolled dummy traffic to a substation network to make critical target nodes indistinguishable from other nodes in network traffic patterns. This paper describes the design and evaluation of such a scheme. Evaluations show that the scheme can effectively protect critical nodes with acceptable communication cost.

Hiding Critical Targets in Smart Grid Networks

Nowadays industries are collecting a massive and exponentially growing amount of data that can potentially promote business innovations. However, it is challenging for resourcelimited clients to analyze their data in a cost-effective and timely way as the data volume keeps growing. With cloud computing, one feasible solution is to analyze the massive data by outsourcing them to the cloud. Nonetheless, clients’ data may contain private information that needs to be kept secret. In this paper, we design a secure, efficient, and verifiable outsourcing protocol specifically for geometric programming, which is one of the most fundamental problems in data analysis with many applications. In particular, a secure and efficient transformation scheme is used to encrypt the original geometric programming problem at the client side and protect its privacy before offloading it, and the gradient projection method is employed to solve the encrypted geometric programming problem in the cloud side. Experiments are conducted on both Amazon Elastic Compute Cloud (EC2) and a laptop to evaluate performance of the designed outsourcing protocol, and the results show the feasibility and efficiency of the protocol.

Efficient Privacy-Preserving Outsourcing of Large-Scale Geometric Programming

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General Access Control Guidance for Cloud Systems

Hiding Critical Targets in Smart Grid Networks

Efficient Privacy-Preserving Outsourcing of Large-Scale Geometric Programming