4,948 research outputs found

    MystifY : A Proactive Moving-Target Defense for a Resilient SDN Controller in Software Defined CPS

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    The recent devastating mission Cyber–Physical System (CPS) attacks, failures, and the desperate need to scale and to dynamically adapt to changes, revolutionized traditional CPS to what we name as Software Defined CPS (SD-CPS). SD-CPS embraces the concept of Software Defined (SD) everything where CPS infrastructure is more elastic, dynamically adaptable and online-programmable. However, in SD-CPS, the threat became more immanent, as the long-been physically-protected assets are now programmatically accessible to cyber attackers. In SD-CPSs, a network failure hinders the entire functionality of the system. In this paper, we present MystifY, a spatiotemporal runtime diversification for Moving-Target Defense (MTD) to secure the SD-CPS infrastructure. In this paper, we relied on Smart Grid networks as crucial SD-CPS application to evaluate our presented solution. MystifY’s MTD relies on a set of pillars to ensure the SDN controller resiliency against failures and attacks. The 1st pillar is a grid-aware algorithm that optimally allocates the most suitable controller–deployment location in large-scale grids. The 2nd pillar is a special diversifier that dynamically relocates the controller between heterogeneously configured hosts to avoid host-based attacks. The 3rd pillar is a temporal diversifier that dynamically detours controller–workload between multiple controllers to enhance their reliability and to detect and avoid controller intrusions. Our experimental results showed the efficiency and effectiveness of the presented approach

    Stealthy MTD against unsupervised learning-based blind FDI Attacks in power systems

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    This paper examines how moving target defenses (MTD) implemented in power systems can be countered by unsupervised learning-based false data injection (FDI) attack and how MTD can be combined with physical watermarking to enhance the system resilience. A novel intelligent attack, which incorporates dimensionality reduction and density-based spatial clustering, is developed and shown to be effective in maintaining stealth in the presence of traditional MTD strategies. In resisting this new type of attack, a novel implementation of MTD combining with physical watermarking is proposed by adding Gaussian watermark into physical plant parameters to drive detection of traditional and intelligent FDI attacks, while remaining hidden to the attackers and limiting the impact on system operation and stability

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

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    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

    Operational moving target defences for improved power system cyber-physical security

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    In this work, we examine how Moving Target Defences (MTDs) can be enhanced to circumvent intelligent false data injection (FDI) attacks against power systems. Initially, we show how, by implementing state-of-the-art topology learning techniques, we can commit full-knowledge-equivalent FDI attacks against static power systems with no prior system knowledge. We go on to explore how naive applications of topology change, as MTDs, can be countered by unsupervised learning-based FDI attacks and how MTDs can be combined with physical watermarking to enhance system resilience. A novel intelligent attack, which incorporates dimensionality reduction and density-based spatial clustering, is developed and shown to be effective in maintaining stealth in the presence of traditional MTD strategies. In resisting this new type of attack, a novel implementation of MTD is suggested. The implementation uses physical watermarking to drive detection of traditional and intelligent FDI attacks while remaining hidden to the attackers. Following this, we outline a cyber-physical authentication strategy for use against FDI attacks. An event-triggered MTD protocol is proposed at the physical layer to complement cyber-side enhancements. This protocol applies a distributed anomaly detection scheme based on Holt-Winters seasonal forecasting in combination with MTD implemented via inductance perturbation. To conclude, we developed a cyber-physical risk assessment framework for FDI attacks. Our assessment criteria combines a weighted graph model of the networks cyber vulnerabilities with a centralised residual-based assessment of the physical system with respect to MTD. This combined approach provides a cyber-physical assessment of FDI attacks which incorporates both the likelihood of intrusion and the prospect of an attacker making stealthy change once intruded.Open Acces

    Digital Twins for Moving Target Defense Validation in AC Microgrids

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    Cyber-physical microgrids are vulnerable to stealth attacks that can degrade their stability and operability by performing low-magnitude manipulations in a coordinated manner. This paper formulates the interactions between CSAs and microgrid defenders as a non-cooperative, zero-sum game. Additionally, it presents a hybrid Moving Target Defense (MTD) strategy for distributed microgrids that can dynamically alter local control gains to achieve resiliency against Coordinated Stealth Attacks (CSAs). The proposed strategy reduces the success probability of attack(s) by making system dynamics less predictable. The framework also identifies and removes malicious injections by modifying secondary control weights assigned to them. The manipulated signals are reconstructed using an Artificial Neural Network (ANN)-based Digital Twin (DT) to preserve stability. To guarantee additional immunity against instability arising from gain alterations, MTD decisions are also validated (via utility and best response computations) using the DT before actual implementation. The DT is also used to find the minimum perturbation that defenders must achieve to invalidate an attacker's knowledge effectively.Comment: IEEE Energy Conversion Congress and Expo (ECCE) 202

    Smart Grid Relay Protection and Network Resource Management for Real-Time Communications.

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    Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017
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