Abstract
False data injection (FDI) attacks are a major security threat to smart grid (SG) communication systems. In FDI attacks, the attacker has the ability of modifying the measurements transmitted by smart grid entities such as smart meters, buses, etc. Many solutions have been proposed to mitigate FDI attacks in the SG. However, most of these solutions rely on centralized state estimation (SE), which is computationally expensive. To engulf this problem in FDI attack detection and to improve security of SGs, this paper proposes novel two-tier secure smart grid (T2S2G) architecture with distributed SE. In T2S2G, measurement collection and SE are involved in first tier while compromised meter detection is involved in second tier. Initially the overall SG system is divided into four sections by the weighted quad tree (WQT) method. Each section is provided with an aggregator, which is responsible to perform SE. Measurements from power grids are collected by remote terminal units (RTUs) and encrypted using a parallel enhanced elliptic curve cryptography (PEECC) algorithm. Then the measurements are transmitted to the corresponding aggregator. Upon collected measurements, aggregator estimates state using the amended particle swarm optimization (APSO) algorithm in a distributed manner. To verify authenticity of aggregators, each aggregator is authenticated by a logical schedule based authentication (LSA) scheme at the control server (CS). In the CS, fuzzy with a neural network (FNN) algorithm is employed for measurements classification. Our proposed T2S2G shows promising results in the following performance metrics: Estimation error, number of protected measurements, detection probability, successful detection rate, and detection delay.
Highlights
In recent times, smart grids (SGs) have become emerging technology in the electricity market since it offers remote monitoring of distributed energy generation [1,2,3]
The cumulative sum (CUSUM) method was modified as the adaptive CUSUM method in order to improve delay and accuracy. This detection model was recursive in nature and each recursion was comprised with the following tests: (i) Unknown variable solver based on the Rao test, and (ii) multithread CUSUM test
We proposed the parallel enhanced elliptic curve cryptography (PEECC) algorithm based encryption scheme, which is similar in terms of performance to [43]
Summary
Smart grids (SGs) have become emerging technology in the electricity market since it offers remote monitoring of distributed energy generation [1,2,3]. The effect of FDI attack is evaluated by bi-level modeling method [9] This evaluation shows that securing the minimal set of sensors (or RTUs) is sufficient to secure an entire SG system since the minimal set of sensors are required to launch the attack. Machine learning approaches such as perceptron, K-nearest neighbor approach, support vector machine (SVM) algorithm, sparse logistic regression method, ensemble learning method, and multiple kernel learning method are adapted for false data detection [10]. All measurements are secured by RTUs using the parallel enhanced elliptic curve cryptography (PEECC) algorithm, which ensures high level data security with minimum time consumption.
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