• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.125-137
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• 2012

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. For such reason, lots of intrusion detection system has been developed. Intrusion detection system has abilities to detect abnormal behavior and unknown intrusions also it can detect intrusions by using patterns studied from various penetration methods. Various algorithms are studying now such as the statistical method for detecting abnormal behavior, extracting abnormal behavior, and developing patterns that can be expected. Etc. This study using clustering of data mining and association rule analyzes detecting areas based on two models and helps design detection system which detecting abnormal behavior, unknown attack, misuse attack in a large network.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.45-52
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• 2021

Although many studies have been conducted to identify unknown attacks in cyber security intrusion detection systems, studies based on outliers are attracting attention. Accordingly, we identify outliers by defining categories for unknown attacks. The unknown attacks were investigated in two categories: first, there are factors that generate variant attacks, and second, studies that classify them into new types. We have conducted outlier studies that can identify similar data, such as variants, in the category of studies that generate variant attacks. The big problem of identifying anomalies in the intrusion detection system is that normal and aggressive behavior share the same space. For this, we applied a technique that can be divided into clear types for normal and attack by discrete wavelet transformation and detected anomalies. As a result, we confirmed that the outliers can be identified through One-Class SVM in the data reconstructed by discrete wavelet transform.

• Bulletin of the Korean Mathematical Society
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• v.51 no.5
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• pp.1347-1356
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• 2014

In this paper, we analyze the security of the KMOV public key cryptosystem. KMOV is based on elliptic curves over the ring $\mathbb{Z}_n$ where n = pq is the product of two large unknown primes of equal bit-size. We consider KMOV with a public key (n, e) where the exponent e satisfies an equation ex-(p+1)(q+1)y = z, with unknown parameters x, y, z. Using Diophantine approximations and lattice reduction techniques, we show that KMOV is insecure when x, y, z are suitably small.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4224-4243
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• 2021

Cognitive radio (CR) is a feasible intelligent technology and can be used as an effective solution to spectrum scarcity and underutilization. As the key function of CR, cooperative spectrum sensing (CSS) is able to effectively prevent the harmful interference with primary users (PUs) and identify the available spectrum resources by exploiting the spatial diversity of multiple secondary users (SUs). However, the open nature of the cognitive radio networks (CRNs) framework makes CSS face many security threats, such as, the malicious user (MU) launches Byzantine attack to undermine CRNs. For this aim, we make an in-depth analysis of the motive and purpose from the MU's perspective in the interweave CR system, aiming to provide the future guideline for defense strategies. First, we formulate a dynamic Byzantine attack model by analyzing Byzantine behaviors in the process of CSS. On the basis of this, we further make an investigation on the condition of making the fusion center (FC) blind when the fusion rule is unknown for the MU. Moreover, the throughput and interference to the primary network are taken into consideration to evaluate the impact of Byzantine attack on the interweave CR system, and then analyze the optimal strategy of Byzantine attack when the fusion rule is known. Finally, theoretical proofs and simulation results verify the correctness and effectiveness of analyses about the impact of Byzantine attack strategy on the throughput and interference.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.279-285
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• 2012

Most national critical key infrastructure, such like electricity, nuclear power plant, and petroleum is run on SCADA (Supervisory Control And Data Acquisition) system as the closed network type. These systems have treated the open protocols like TCP/IP, and the commercial operating system, which due to gradually increasing dependence on IT(Information Technology) is a trend. Recently, concerns have been raised about the possibility of these facilities being attacked by cyber terrorists, hacking, or viruses. In this paper, the method to minimize threats and vulnerabilities is proposed, with the virtual honeynet system architecture and the attack detection algorithm, which can detect the unknown attack patterns of Zero-Day Attack are reviewed.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.171-175
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• 2006

Diffie-Hellman기반 키 합의 프로토콜들은 비교적 고비용의 연산인 지수연산으로 인해, 유선 네트워크 환경에 비해 저전력이고 컴퓨팅 자원이 제한되어 있는 무선 네트워크 환경에서는 비효율적이고 구현하기 어려운 문제가 있다. 이에 Yang등은 대리서버(Proxy Server)를 이용하여 Diffie-Hellman방식을 적용하면서도 단말 무선 네트워크 사용자의 지수연산부담을 감소시키는 효율적인 키 합의 프로토콜(이하 SEKAP)을 제안하였다. 그러나 SEKAP는 재전송공격(Replay Attack), 알려지지 않은 키 공유 공격(Unknown Key Share Attack), 그리고 키 노출로 인한 위장공격(Key Compromised Impersonation Attack) 등에 취약하며 전방향 안전성(Forward Secrecy)을 제공하지 못한다. 본 논문에서는 SEKAP가 위 공격들에 대해 취약함을 보이고, 세션키의 상호인증을 추가한 개선된 프로토콜을 제안한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.107-122
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• 2014

The DDoS attacks and the APT attacks occurred by the zombie computers simultaneously attack target systems at a fixed time, caused social confusion. These attacks require many zombie computers running attacker's commands, and unknown malware that can bypass detecion of the anti-virus products is being executed in those computers. A that time, many methods have been proposed for the detection of unknown malware against the anti-virus products that are detected using the signature. This paper proposes a method of unknown malware detection using digital forensic techniques and describes the results of experiments carried out on various samples of malware and normal files.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4553-4562
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• 2010

With developing networks, information security is going to be important and therefore lots of intrusion detection system has been developed. Intrusion detection system has abilities to detect abnormal behavior and unknown intrusions also it can detect intrusions by using patterns studied from various penetration methods. Various algorithms are studying now such as the statistical method for detecting abnormal behavior, extracting abnormal behavior, and developing patterns that can be expected. Etc. This study using clustering of data mining and association rule analyzes detecting areas based on two models and helps design detection system which detecting abnormal behavior, unknown attack, misuse attack in a large network.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.1
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• pp.3-10
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• 2002

This paper proposes an ID-based entity-aunthentication and authenticated key exchange protocol with ECC via two-pass communications between two parties who airs registered to the trusted third-party KC in advance. The proposed protocol developed by applying ECDSA and Diffie-Hellman key exchange scheme to the ID-based key distribution scheme over ECC proposed by H. Sakazaki, E. Okamoto and M. Mambo(SOM scheme). The security of this protocol is based on the Elliptic Curve Discrete Logarithm Problem(ECDLP) and the Elliptic Curve Diffie-Hellman Problem(ECDHP). It is strong against unknown key share attack and it provides the perfect forward secrecy, which makes up for the weakness in SOM scheme,

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1916-1934
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• 2023

The phishing attack is a malicious emerging threat on the internet where the hackers try to access the user credentials such as login information or Internet banking details through pirated websites. Using that information, they get into the original website and try to modify or steal the information. The problem with traditional defense systems like firewalls is that they can only stop certain types of attacks because they rely on a fixed set of principles to do so. As a result, the model needs a client-side defense mechanism that can learn potential attack vectors to detect and prevent not only the known but also unknown types of assault. Feature selection plays a key role in machine learning by selecting only the required features by eliminating the irrelevant ones from the real-time dataset. The proposed model uses Hyperparameter Optimized Artificial Neural Networks (H-ANN) combined with a Hybrid Firefly and Grey Wolf Optimization algorithm (H-FFGWO) to detect and block phishing websites in Internet of Things(IoT) Applications. In this paper, the H-FFGWO is used for the feature selection from phishing datasets ISCX-URL, Open Phish, UCI machine-learning repository, Mendeley website dataset and Phish tank. The results showed that the proposed model had an accuracy of 98.07%, a recall of 98.04%, a precision of 98.43%, and an F1-Score of 98.24%.