The Experts below are selected from a list of 3996 Experts worldwide ranked by ideXlab platform

Ehab Alshaer - One of the best experts on this subject based on the ideXlab platform.

  • randomization based intrusion detection system for advanced metering infrastructure
    ACM Transactions on Information and System Security, 2015
    Co-Authors: Muhammad Qasim Ali, Ehab Alshaer
    Abstract:

    Smart Grid Deployment initiatives have been witnessed in recent years. Smart Grids provide bidirectional communication between meters and head-end systems through Advanced Metering Infrastructure (AMI). Recent studies highlight the threats targeting AMI. Despite the need for tailored Intrusion Detection Systems (IDSs) for Smart Grids, very limited progress has been made in this area. Unlike traditional networks, Smart Grids have their own unique challenges, such as limited computational power devices and potentially high Deployment cost, that restrict the Deployment options of intrusion detectors. We show that Smart Grids exhibit deterministic and predictable behavior that can be accurately modeled to detect intrusion. However, it can also be leveraged by the attackers to launch evasion attacks. To this end, in this article, we present a robust mutation-based intrusion detection system that makes the behavior unpredictable for the attacker while keeping it deterministic for the system. We model the AMI behavior using event logs collected at Smart collectors, which in turn can be verified using the invariant specifications generated from the AMI behavior and mutable configuration. Event logs are modeled using fourth-order Markov chain and specifications are written in Linear Temporal Logic (LTL). To counter evasion and mimicry attacks, we propose a configuration randomization module. The approach provides robustness against evasion and mimicry attacks; however, we discuss that it still can be evaded to a certain extent. We validate our approach on a real-world dataset of thousands of meters collected at the AMI of a leading utility provider.

  • configuration based ids for advanced metering infrastructure
    Computer and Communications Security, 2013
    Co-Authors: Muhammad Qasim Ali, Ehab Alshaer
    Abstract:

    Smart Grid Deployment initiatives have been witnessed in the past recent years. Smart Grids provide bi-directional communication between meters and headend system through Advanced Metering Infrastructure (AMI). Recent studies highlight the threats targeting AMI. Despite the need of tailored Intrusion Detection Systems (IDS) for the Smart Grid, very limited progress has been made in this area. Unlike traditional networks, Smart Grid has its own unique challenges, such as limited computational power devices and potentially high Deployment cost, that restrict the Deployment options of intrusion detectors. We show that Smart Grid exhibits deterministic and predictable behavior that can be accurately modeled to develop intrusion detection system. In this paper, we show that AMI behavior can be modeled using event logs collected at Smart collectors, which in turn can be verified using the specifications invariant generated from the configurations of the AMI devices. Event logs are modeled using fourth order Markov Chain and specifications are written in Linear Temporal Logic (LTL). The approach provides robustness against evasion and mimicry attacks, however, we discuss that it still can be evaded to a certain extent. We validate our approach on a real-world dataset of thousands of meters collected at the AMI of a leading utility provider.

Muhammad Qasim Ali - One of the best experts on this subject based on the ideXlab platform.

  • randomization based intrusion detection system for advanced metering infrastructure
    ACM Transactions on Information and System Security, 2015
    Co-Authors: Muhammad Qasim Ali, Ehab Alshaer
    Abstract:

    Smart Grid Deployment initiatives have been witnessed in recent years. Smart Grids provide bidirectional communication between meters and head-end systems through Advanced Metering Infrastructure (AMI). Recent studies highlight the threats targeting AMI. Despite the need for tailored Intrusion Detection Systems (IDSs) for Smart Grids, very limited progress has been made in this area. Unlike traditional networks, Smart Grids have their own unique challenges, such as limited computational power devices and potentially high Deployment cost, that restrict the Deployment options of intrusion detectors. We show that Smart Grids exhibit deterministic and predictable behavior that can be accurately modeled to detect intrusion. However, it can also be leveraged by the attackers to launch evasion attacks. To this end, in this article, we present a robust mutation-based intrusion detection system that makes the behavior unpredictable for the attacker while keeping it deterministic for the system. We model the AMI behavior using event logs collected at Smart collectors, which in turn can be verified using the invariant specifications generated from the AMI behavior and mutable configuration. Event logs are modeled using fourth-order Markov chain and specifications are written in Linear Temporal Logic (LTL). To counter evasion and mimicry attacks, we propose a configuration randomization module. The approach provides robustness against evasion and mimicry attacks; however, we discuss that it still can be evaded to a certain extent. We validate our approach on a real-world dataset of thousands of meters collected at the AMI of a leading utility provider.

  • configuration based ids for advanced metering infrastructure
    Computer and Communications Security, 2013
    Co-Authors: Muhammad Qasim Ali, Ehab Alshaer
    Abstract:

    Smart Grid Deployment initiatives have been witnessed in the past recent years. Smart Grids provide bi-directional communication between meters and headend system through Advanced Metering Infrastructure (AMI). Recent studies highlight the threats targeting AMI. Despite the need of tailored Intrusion Detection Systems (IDS) for the Smart Grid, very limited progress has been made in this area. Unlike traditional networks, Smart Grid has its own unique challenges, such as limited computational power devices and potentially high Deployment cost, that restrict the Deployment options of intrusion detectors. We show that Smart Grid exhibits deterministic and predictable behavior that can be accurately modeled to develop intrusion detection system. In this paper, we show that AMI behavior can be modeled using event logs collected at Smart collectors, which in turn can be verified using the specifications invariant generated from the configurations of the AMI devices. Event logs are modeled using fourth order Markov Chain and specifications are written in Linear Temporal Logic (LTL). The approach provides robustness against evasion and mimicry attacks, however, we discuss that it still can be evaded to a certain extent. We validate our approach on a real-world dataset of thousands of meters collected at the AMI of a leading utility provider.

Ya Zhang - One of the best experts on this subject based on the ideXlab platform.

  • permissioned blockchain and edge computing empowered privacy preserving Smart Grid networks
    IEEE Internet of Things Journal, 2019
    Co-Authors: Keke Gai, Liehuang Zhu, Ya Zhang
    Abstract:

    The blooming trend of Smart Grid Deployment is engaged by the evolution of the network technology, as the connected environment offers various alternatives for electrical data collections. Having diverse data sharing/transfer means is deemed an important aspect in enabling intelligent controls/governance in Smart Grid. However, security and privacy concerns also are introduced while flexible communication services are provided, such as energy depletion and infrastructure mapping attacks. This paper proposes a model permissioned blockchain edge model for Smart Grid network (PBEM-SGN) to address the two significant issues in Smart Grid, privacy protections, and energy security, by means of combining blockchain and edge computing techniques. We use group signatures and covert channel authorization techniques to guarantee users’ validity. An optimal security-aware strategy is constructed by Smart contracts running on the blockchain. Our experiments have evaluated the effectiveness of the proposed approach.

D P Labridis - One of the best experts on this subject based on the ideXlab platform.

  • cyber attack impact on critical Smart Grid infrastructures
    IEEE PES Innovative Smart Grid Technologies Conference, 2014
    Co-Authors: Kallisthenis I Sgouras, Athina D Birda, D P Labridis
    Abstract:

    Electrical Distribution Networks face new challenges by the Smart Grid Deployment. The required metering infrastructures add new vulnerabilities that need to be taken into account in order to achieve Smart Grid functionalities without considerable reliability trade-off. In this paper, a qualitative assessment of the cyber attack impact on the Advanced Metering Infrastructure (AMI) is initially attempted. Attack simulations have been conducted on a realistic Grid topology. The simulated network consisted of Smart Meters, routers and utility servers. Finally, the impact of Denial-of-Service and Distributed Denial-of-Service (DoS/DDoS) attacks on distribution system reliability is discussed through a qualitative analysis of reliability indices.

A C De C Lima - One of the best experts on this subject based on the ideXlab platform.

  • demand side management using artificial neural networks in a Smart Grid environment
    Renewable & Sustainable Energy Reviews, 2015
    Co-Authors: Maria N Q Macedo, Joaquim J M Galo, L A L De Almeida, A C De C Lima
    Abstract:

    Abstract Smart Grid Deployment is a global trend, creating endless possibilities for the use of data generated by dynamic networks. The challenge is the transformation of this large volume of data into useful information for the electrical system. An example of this is the application of demand side management (DSM) techniques for the optimisation of power system management in real time. This article discusses the use of DSM in this new environment of electrical system and it presents a simulation that uses data acquired from digital meters, it creates patterns of load curves, uses these patterns load data to train and validate a ANN and uses this ANN to classify new data using these defined patters. The results obtained in this study show that the intelligent network environment facilitates the implementation of DSM and the use of ANN presented a satisfactory performance for the classification of load curves.