• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.83-90
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• 2009

Since wireless sensor networks are deployed in open environments, an attacker can physically capture some sensor nodes. Using information of compromised nodes, an attacker can launch false data injection attacks that report nonexistent events. False data can cause false alarms and draining the limited energy resources of the forwarding nodes. In order to detect and discard such false data during the forwarding process, various security solutions have been proposed. But since they are prevention-based solutions that involve additional operations, they would be energy-inefficient if the corresponding attacks are not launched. In this paper, we propose a detection method that can detect false data injection attacks without extra overheads. The proposed method is designed based on the signature of false data injection attacks that has been derived through simulation. The proposed method detects the attacks based on the number of reporting nodes, the correctness of the reports, and the variation in the number of the nodes for each event. We show the proposed method can detect a large portion of attacks through simulation.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.19-28
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• 2017

False data injection attacks have recently been introduced as one of important issues related to cyber-attacks on electric power grids. These attacks aim to compromise the readings of multiple power meters in order to mislead the operation and control centers. Recent studies have shown that if a malicious attacker has complete knowledge of the power grid topology and branch admittances, s/he can adjust the false data injection attack such that the attack remains undetected and successfully passes the bad data detection tests that are used in power system state estimation. In this paper, we investigate that a practical false data injection attack is essentially a cyber-attack with uncertain information due to the attackers lack of knowledge with respect to the power grid parameters because the attacker has limited physical access to electric facilities and limited resources to compromise meters. We mathematically formulated a method of identifying the most vulnerable locations to false data injection attack. Furthermore, we suggest minimum topology changes or phasor measurement units (PMUs) installation in the given power grids for mitigating such attacks and indicate a new security metrics that can compare different power grid topologies. The proposed metrics for performance is verified in standard IEEE 30-bus system. We show that the robustness of grids can be improved dramatically with minimum topology changes and low cost.

• Journal of the Korea Society for Simulation
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• v.17 no.3
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• pp.27-34
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• 2008

Wireless sensor networks are comprised of sensor nodes with resource-constrained hardware. Nodes in the sensor network without adequate protection may be compromised by adversaries. Such compromised nodes are vulnerable to the attacks like false reports injection attacks and false data injection attacks on legitimate reports. In false report injection attacks, an adversary injects false report into the network with the goal of deceiving the sink or the depletion of the finite amount of energy in a battery powered network. In false data injection attacks on legitimate reports, the attacker may inject a false data for every legitimate report. To address such attacks, the probabilistic voting-based filtering scheme (PVFS) has been proposed by Li and Wu. However, each cluster head in PVFS needs additional transmission device. Therefore, this paper proposes a fuzzy logic-based false report detection method (FRD) to mitigate the threat of these attacks. FRD employs the statistical en-route filtering scheme as a basis and improves upon it. We demonstrate that FRD is efficient with respect to the security it provides, and allows a tradeoff between security and energy consumption, as shown in the simulation.

• Journal of IKEEE
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• v.26 no.3
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• pp.389-395
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• 2022

With the evolution of technology, cyber physical systems (CPSs) are being upgraded, and new types of cyber attacks are being discovered accordingly. There are many forms of cyber attack, and all cyber attacks are made to manipulate the target systems. A representative system among cyber physical systems is a cyber physical power system (CPPS), that is, a smart grid. Smart grid is a new type of power system that provides reliable, safe, and efficient energy transmission and distribution. In this paper, specific types of cyber attacks well known as false data injection attacks targeting state estimation and energy distribution of smart grid, and protection strategies for defense of these attacks and dynamic monitoring for detection are described.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4183-4204
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• 2018

The existing en-route filtering schemes only consider some simple false data injection attacks, which results in lower safety performance. In this paper, we propose an efficient geographical information-based en-route filtering scheme (EGEFS), in which each forwarding node verifies not only the message authentication codes (MACs), but also the report identifier and the legitimacy and authenticity of locations carried in a data report. Thus, EGEFS can defend against not only the simple false data injection attacks and the replay attack, but also the collusion attack with forged locations proposed in this paper. In addition, we propose a new method for electing the center-of-stimulus (CoS) node, which can ensure that only one detecting node will be elected as the CoS node to generate one data report for an event. The simulation results show that, compared to the existing en-route filtering schemes, EGEFS has higher safety performance, because it can resist more types of false data injection attacks, and it also has higher filtering efficiency and lower energy expenditure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.159-162
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• 2008

Generally, sensor nodes can be easily compromised and seized by an adversary because sensor nodes are hostile environments after dissemination. An adversary may be various security attacks into the networks using compromised node. False data injection attack using compromised node, it may not only cause false alarms, but also the depletion of the severe amount of energy waste. Dynamic en-route scheme for Filtering False Data Injection (DEF) can detect and drop such forged report during the forwarding process. In this scheme, each forwarding nodes verify reports using a regular probability. In this paper, we propose verification probability adjustment scheme of forwarding nodes though a fuzzy rule-base system for the Dynamic en-route filtering scheme for Filtering False Data Injection in sensor networks. Verification probability determination of forwarding nodes use false traffic rate and distance form source to base station.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.121-129
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• 2011

In wireless sensor networks, a compromised sensor node can inject false data during data aggregation. Existing solutions of securing data aggregation require high communication cost in securing data aggregation. In this paper, we propose a monitoring-based secure data aggregation protocol that minimizes communication cost of identifying the location of false data injection attacks. The main idea is that when monitoring nodes find an injected false data, their reporting messages along with Message Authentication Codes (MACs) are summarized in a single message before sending it to the Base Station (BS). Then the BS identifies the attacking node. The simulation shows that energy consumption of the proposed protocol with short and normal concatenations of MACs are 45% and 36% lower than that of an existing protocol, respectively.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.21-29
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• 2011

Wireless sensor nodes operate in open environments. The deployed sensor nodes are very vulnerable to physical attacks from outside. Attackers compromise some sensor nodes. The compromised nodes by attackers can lead to false data injection into sensor networks. These attacks deplete the limited energy of sensor nodes. Ye et al. proposed the Statistical En-Route Filtering (SEF) as a countermeasure of the attacks. The sensor node in SEF examines the event reports based on certain uniform probability. Thus, the same energies are consumed in both legitimate reports and false reports. In this paper, we propose a method that each node controls the probability of attempts to verify a report to reduce energy consumption of sensor nodes. The probability is determined in consideration of the remaining energy of the node, the number of hops from the node to SINK node, the ratio of false reports. the proposed method can have security which is similar with SEF and consumes lower energy than SEF.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.4
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• pp.41-49
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• 2008

Sensor networks are often deployed in unattended environments, thus leaving these networks vulnerable to false data injection attacks in which an adversary injects forged reports into the network through compromised nodes. Such attacks by compromised sensors can cause not only false alarms but also the depletion of the finite amount of energy in a battery powered network. In order to reduce damage from these attacks, several security solutions have been proposed. Researchers have also proposed some techniques to increase the energy-efficiency of such security solutions. In this paper, we propose a CH(Cluster Header) selection algorithm to choose low power delivery method in sensor networks. The CNP(Contract Net Protocol), which is an approach to solve distribution problems, is applied to choose CHs for event sensing. As a result of employing CNP, the proposed method can prevent dropping of sensing reports with an insufficient number of message authentication codes during the forwarding process, and is efficient in terms of energy saving.