• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.101-109
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• 2019

In addition to enabling access, database accounts play a protective role by defending the database from external attacks. However, because only a single account is used in the database, the account becomes the subject of vulnerability attacks. This common practice is due to the lack of database support, large numbers of users, and row-based database permissions. Therefore if the logic of the application is wrong or vulnerable, there is a risk of exposing the entire database. In this paper, we propose a Least-Privilege Account Separation Model (LPASM) that serves as an information guardian to protect the database from attacks. We separate database accounts depending on the role of application services. This model can protect the database from malicious attacks and prevent damage caused by privilege escalation by an attacker. We classify the account control policies into four categories and propose detailed roles and operating plans for each account.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.239-247
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• 2019

Recently, successful attacks on cyber-physical systems have been reported. As existing network security solutions are limited in preventing the system from malicious attacks, appropriate countermeasures are required from the perspective of the control. In this paper, the cyber and physical attacks are interpreted in terms of actuator and sensor attacks. Based on the interpretation, we suggest a strategy for designing Kalman filters to secure the resilience and safety of the system. Such a strategy is implemented in details to be applied for the lateral control of autonomous driving vehicle. A set of simulation results verify the performance of the proposed Kalman filters.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.387-388
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• 2019

As malicious apps vary significantly across Android malware, it is challenging to prevent that the end-users download apps from unsecured app markets. In this paper, we propose an approach to classify the malicious methods based on permissions using Long Short Term Memory (LSTM) that is used to embed the semantics among Intent and permissions. Then the malicious method that is an unsecured method will be removed and re-uploaded to official market. This approach may induce that the end-users download apps from official market in order to reduce the risk of attacks.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.168-174
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• 2021

The Cognitive radio (CR) is evolving technology for managing the spectrum bandwidth in wireless network. The security plays a vital role in wireless network where the secondary users are trying to access the primary user's bandwidth. During the allocation the any malicious user either he pretends to be primary user or secondary user to access the vital information's such as credentials, hacking the key, network jam, user overlapping etc. This research paper discusses on various types of attack and to prevent the attack in cognitive radio network. In this research, secondary users are identified by the primary user to access the primary network by the secondary users. The secondary users are given authorization to access the primary network. If any secondary user fails to provide the authorization, then that user will be treated as the malicious user. In this paper two approaches are suggested one by applying elliptic curve cryptography and the other method by using priority-based service access.

• Convergence Security Journal
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• v.23 no.5
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• pp.65-72
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• 2023

This paper proposes a method for detecting malicious domains considering human habitual characteristics by building a Deep Learning model based on LSTM (Long Short-Term Memory). DGA (Domain Generation Algorithm) malicious domains exploit human habitual errors, resulting in severe security threats. The objective is to swiftly and accurately respond to changes in malicious domains and their evasion techniques through typosquatting to minimize security threats. The LSTM-based Deep Learning model automatically analyzes and categorizes generated domains as malicious or benign based on malware-specific features. As a result of evaluating the model's performance based on ROC curve and AUC accuracy, it demonstrated 99.21% superior detection accuracy. Not only can this model detect malicious domains in real-time, but it also holds potential applications across various cyber security domains. This paper proposes and explores a novel approach aimed at safeguarding users and fostering a secure cyber environment against cyber attacks.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.487-491
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• 2008

This paper proposes the Byzantine fault tolerant clock synchronization scheme for wireless sensor networks to cope with the clock synchronization disturbance attack of malicious nodes. In the proposed scheme, a node which is requiring clock synchronization receives 3m+1 clock synchronization messages not only from its parent nodes but also from its sibling nodes in order to tolerate malicious attacks even if up to m malicious nodes exist among them. The results show that the proposed scheme is 7 times more resilient to the clock synchronization disturbance attack of malicious nodes than existing schemes in terms of synchronization accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.431-438
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• 2019

Fileless malware uses memory injection attacks to hide traces of payloads to perform malicious works. During the memory injection attack, an attack named "process hollowing" is a method of creating paused benign process like system processes. And then injecting a malicious payload into the benign process allows malicious behavior by pretending to be a normal process. In this paper, we propose a method to detect the memory injection regardless of whether or not the malicious action is actually performed when a process hollowing attack occurs. The replication process having same execution condition as the process of suspending the memory injection is executed, the data set belonging to each process virtual memory area is compared using the fuzzy hash, and the similarity is calculated.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.623-632
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• 2005

In ad-hoc network, there is no fixed infrastructure such as base stations or mobile switching centers. The security of ad-hoc network is more vulnerable than traditional networks because of the basic characteristics of ad-hoc network, and current muting protocols for ad-hoc networks allow many different types of attacks by malicious nodes. Malicious nodes can disrupt the correct functioning of a routing protocol by modifying routing information, by fabricating false routing information and by impersonating other nodes. We propose a routing suity mechanism based on one-time digital signature. In our proposal, we use one-time digital signatures based on one-way hash functions in order to limit or prevent attacks of malicious nodes. For the purpose of generating and keeping a large number of public key sets, we derive multiple sets of the keys from hash chains by repeated hashing of the public key elements in the first set. After that, each node publishes its own public keys, broadcasts routing message including one-time digital signature during route discovery and route setup. This mechanism provides authentication and message integrity and prevents attacks from malicious nodes. Simulation results indicate that our mechanism increases the routing overhead in a highly mobile environment, but provides great security in the route discovery process and increases the network efficiency.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.35-45
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• 2009

In an open environment such as Home Network, ZigBee Cluster comprising a plurality of Ato-cells is required to provide intense security over the movement of collected, measured data. Against this setting, various security issues are currently under discussion concerning master key control policies, Access Control List (ACL), and device sources, which all involve authentication between ZigBee devices. A variety of authentication methods including Hash Chain Method, token-key method, and public key infrastructure, have been previously studied, and some of them have been reflected in standard methods. In this context, this paper aims to explore whether a new method for searching for neighboring devices in order to detect device replications and Sybil attacks can be applied and extended to the field of security. The neighbor detection applied method is a method of authentication in which ACL information of new devices and that of neighbor devices are included and compared, using information on peripheral devices. Accordingly, this new method is designed to implement detection of malicious device attacks such as Sybil attacks and device replications as well as prevention of hacking. In addition, in reference to ITU-T SG17 and ZigBee Pro, the home network equipment, configured to classify the labels and rules into four categories including user's access rights, time, date, and day, is implemented. In closing, the results demonstrates that the proposed method performs significantly well compared to other existing methods in detecting malicious devices in terms of success rate and time taken.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1529-1547
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• 2015

Establishment of trust is important in wireless sensor networks for security enhancement and successful collaboration. Basically, a node establishes trust with other nodes by estimating a trust value based on monitored behavior of the other nodes. Since a malicious/misbehaving node might launch different attack strategies and might demonstrate random misbehavior, a trust estimation method should be robust against such attacks and misbehavior. Otherwise, the operation of trust establishment will be meaningless, and performance of an application that runs on top of trust establishment will degrade. In this paper, we propose a robust and novel trust estimation method. Unlike traditional trust estimation methods, we consider not only the weight of misbehavior but also the frequency of misbehavior. The frequency-of-misbehavior component explicitly demonstrates how frequently a node misbehaves during a certain observed time period, and it tracks the behavior of nodes more efficiently, which is a main factor in deriving an accurate trust value. In addition, the weight of misbehavior is comprehensively measured to mitigate the effect of an on-off attack. Frequency and weight of misbehavior are comprehensively combined to obtain the trust value. Evaluation results show that the proposed method outperforms other trust estimation methods under different attacks and types of misbehavior.