• 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.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.53-62
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• 2020

In the end-user domain of an IoT environment, there are more and more intelligent M2M devices that provide resources to create and share application services. Therefore, it can be very useful to manage trust by transferring the role of the existing centralized service provider to end users in a P2P environment. However, in a decentralized M2M computing environment where end users independently provide or consume services, mutual trust building is the most important factor. This is because malicious users trying to build malfunctioning services can cause security problems in M2M computing environments such as IoT. In this paper, we provide an integrated analysis and approach for trust evaluation of M2M application services, and an optimized trust evaluation model that can guarantee reliability among users of the M2M community.

• ETRI Journal
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• v.37 no.1
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• pp.77-87
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• 2015

In wireless networks, cooperation is necessary for many protocols, such as routing, clock synchronization, and security. It is known that cooperator nodes suffer greatly from problems such as increasing energy consumption. Therefore, rational nodes have no incentive to cooperatively forward traffic for others. A rational node is different from a malicious node. It is a node that makes the best decision in each state (cooperate or non-cooperate). In this paper, game theory is used to analyze the cooperation between nodes. An evolutionary game has been investigated using two nodes, and their strategies have been compared to find the best one. Subsequently, two approaches, one based on a genetic algorithm (GA) and the other on learning automata (LA), are presented to incite nodes for cooperating in a noisy environment. As you will see later, the GA strategy is able to disable the effect of noise by using a big enough chromosome; however, it cannot persuade nodes to cooperate in a noisefree environment. Unlike the GA strategy, the LA strategy shows good results in a noise-free environment because it has good agreement in cooperation-based strategies in both types of environment (noise-free and noisy).

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.35-42
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• 2017

In this paper, we propose to implement an integrated monitoring system using log data to reduce the load of analysis task of information security officer and to detect information leak in advance. To do this, we developed a transmission module between different model DBMS that transmits large amount of log data generated by the individual security system (MSSQL) to the integrated monitoring system (ORACLE), and the transmitted log data is digitized by individual and individual and researches about the continuous inspection and measures against malicious users when the information leakage symptom is detected by using the numerical data.

• Convergence Security Journal
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• v.6 no.1
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• pp.13-23
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• 2006

It is more desirable to execute AV software on server systems rather than on clients for the minimization of damages due to malicious codes. AV software on server system, however, may aggravate the load of server system. In this paper, we propose a new AV software executed on server system without additional loads with a monitor and multi-agent model. The new AV software supports dynamic load balancing that reflects the features of AV engine, and thus it can be executed efficiently on server systems. The results of performance evaluation on the AV software attest to the strong points of the new AV software.

• Convergence Security Journal
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• v.16 no.2
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• pp.63-70
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• 2016

With the development of information and communication technologies like internet, the environment where people are able to access internet at any time and at any place has been established. As a result, cyber threats have been tried through various routes. Of cyber threats, DDoS is on the constant rise. For DDoS prediction modeling, this study drew a DDoS security index prediction formula on the basis of event data by using a statistical technique, and quantified the drawn security index. It is expected that by using the proposed security index and coming up with a countermeasure against DDoS threats, it is possible to minimize damage and thereby the prediction model will become objective and efficient.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1773-1795
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• 2020

Unmanned aerial vehicles (UAVs) network are a very vibrant research area nowadays. They have many military and civil applications. Limited bandwidth, the high mobility and secure communication of micro UAVs represent their three main problems. In this paper, we try to address these problems by means of secure clustering, and a security clustering algorithm based on integrated trust value for UAVs network is proposed. First, an improved the k-means++ algorithm is presented to determine the optimal number of clusters by the network bandwidth parameter, which ensures the optimal use of network bandwidth. Second, we considered variables representing the link expiration time to improve node clustering, and used the integrated trust value to rapidly detect malicious nodes and establish a head list. Node clustering reduce impact of high mobility and head list enhance the security of clustering algorithm. Finally, combined the remaining energy ratio, relative mobility, and the relative degrees of the nodes to select the best cluster head. The results of a simulation showed that the proposed clustering algorithm incurred a smaller computational load and higher network security.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5560-5579
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• 2019

Searchable symmetric encryption (SSE) scheme can perform search on encrypted data directly without revealing the plain data and keywords. At present, many constructive SSE schemes were proposed. However, they cannot really resist the malicious adversary, because it (i.e., the cloud server) may delete some important data. As a result, it is very likely that the returned search results are incorrect. In order to better guarantee the integrity of outsourcing data, and ensure the correction of returned search results at the same time, in this paper, we combine SSE with blockchain (BC), and propose a SSE-on-BC framework model. We then construct two concrete schemes based on the size of the data, which can better provide privacy protection and integrity verification for data. Lastly, we present their security and performance analyses, which show that they are secure and feasible.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2236-2257
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• 2020

Ransomware is malicious software that encrypts the user-related files and data and holds them to ransom. Such attacks have become one of the serious threats to cyberspace. The avoidance techniques that ransomware employs such as obfuscation and/or packing makes it difficult to analyze such programs statically. Although many ransomware detection studies have been conducted, they are limited to a small portion of the attack's characteristics. To this end, this paper proposed a framework for the behavioral-based dynamic analysis of high survivable ransomware (HSR) with integrated valuable feature sets. Term Frequency-Inverse document frequency (TF-IDF) was employed to select the most useful features from the analyzed samples. Support Vector Machine (SVM) and Artificial Neural Network (ANN) were utilized to develop and implement a machine learning-based detection model able to recognize certain behavioral traits of high survivable ransomware attacks. Experimental evaluation indicates that the proposed framework achieved an area under the ROC curve of 0.987 and a few false positive rates 0.007. The experimental results indicate that the proposed framework can detect high survivable ransomware in the early stage accurately.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.19-25
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• 2017

Nowadays, networked computer systems play an increasingly important role in our society and its economy. They have become the targets of a wide array of malicious attacks that invariably turn into actual intrusions. This is the reason computer security has become an essential concern for network administrators. Recently, a number of Detection/Prevention System schemes have been proposed based on various technologies. However, the techniques, which have been applied in many systems, are useful only for the existing patterns of intrusion. Therefore, probe detection has become a major security protection technology to detection potential attacks. Probe detection needs to take into account a variety of factors ant the relationship between the various factors to reduce false negative & positive error. It is necessary to develop new technology of probe detection that can find new pattern of probe. In this paper, we propose an hybrid probe detection using Fuzzy Cognitive Map(FCM) and Self Adaptive Module(SAM) in dynamic environment such as Cloud and IoT. Also, in order to verify the proposed method, experiments about measuring detection rate in dynamic environments and possibility of countermeasure against intrusion were performed. From experimental results, decrease of false detection and the possibilities of countermeasures against intrusions were confirmed.