• Journal of the Korea Society of Computer and Information
• /
• v.14 no.12
• /
• pp.157-167
• /
• 2009

Recently, the leakage of confidential information and personal information is taking place on the Internet more frequently than ever before. Most of such online security incidents are caused by attacks on vulnerabilities in web applications developed carelessly. It is impossible to detect an attack on a web application with existing firewalls and intrusion detection systems. Besides, the signature-based detection has a limited capability in detecting new threats. Therefore, many researches concerning the method to detect attacks on web applications are employing anomaly-based detection methods that use the web traffic analysis. Much research about anomaly-based detection through the normal web traffic analysis focus on three problems - the method to accurately analyze given web traffic, system performance needed for inspecting application payload of the packet required to detect attack on application layer and the maintenance and costs of lots of network security devices newly installed. The UTM(Unified Threat Management) system, a suggested solution for the problem, had a goal of resolving all of security problems at a time, but is not being widely used due to its low efficiency and high costs. Besides, the web filter that performs one of the functions of the UTM system, can not adequately detect a variety of recent sophisticated attacks on web applications. In order to resolve such problems, studies are being carried out on the web application firewall to introduce a new network security system. As such studies focus on speeding up packet processing by depending on high-priced hardware, the costs to deploy a web application firewall are rising. In addition, the current anomaly-based detection technologies that do not take into account the characteristics of the web application is causing lots of false positives and false negatives. In order to reduce false positives and false negatives, this study suggested a realtime anomaly detection method based on the analysis of the length of parameter value contained in the web client's request. In addition, it designed and suggested a WAF(Web Application Firewall) that can be applied to a low-priced system or legacy system to process application data without the help of an exclusive hardware. Furthermore, it suggested a method to resolve sluggish performance attributed to copying packets into application area for application data processing, Consequently, this study provide to deploy an effective web application firewall at a low cost at the moment when the deployment of an additional security system was considered burdened due to lots of network security systems currently used.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.9
• /
• pp.4200-4206
• /
• 2012

Recently, online-game industry has been rapidly expanding in these days. But, the various game service victimized cases are generated by the bots program. Particularly, the abnormal collection of the game money and item loses the inherent fun of a game. It reaches ultimately the definite bad effect to the game life cycle. In this paper, we propose a Bots detection method by observing the playing patterns of game characters with game log data. It analyzed behaviors of human players as well as bots and identified features to build the model to differentiate bots from human players. In an experiment, by using the served online-game, the model of a user and bots were generated was distinguished. And the reasonable result was confirmed.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.14 no.6
• /
• pp.1097-1106
• /
• 2011

Tactical Information Communication Network(TICN), a concept-type integrated Military Communication system that enables precise command control and decision making, is designed to advance into high speed, large capacity, long distance wireless relay transmission. To support mobility in battlefield environments, the application of Ad-hoc networking technology to its wireless communication has been examined. Ad-hoc network works properly only if the participating nodes cooperate in routing and packet forwarding. However, if selfish nodes not forwarding packets of other nodes and malicious nodes making the false accusation are in the network, it is faced to many threats. Therefore, detection and management of these misbehaving nodes is necessary to make confident in Ad-hoc networks. To solve this problem, we propose an efficient intrusion detection technique to detect and manage those two types of attacks. The simulation-based performance analysis shows that our approach is highly effective and can reliably detect a multitude of misbehaving node.

• Review of KIISC
• /
• v.5 no.2
• /
• pp.32-48
• /
• 1995

Our paper describes an Intrusion Detection Parallel System(IDPS) which detects an anomaly activity corresponding to the actions that interaction between near detection events. IDES uses parallel inductive approaches regarding the problem of real-time anomaly behavior detection on rule-based system. This approach uses sequential rule that describes user's behavior and characteristics dependent on time. and that audits user's activities by using rule base as data base to store user's behavior pattern. When user's activity deviates significantly from expected behavior described in rule base. anomaly behaviors are recorded. Observed behavior is flagged as a potential intrusion if it deviates significantly from the expected behavior or if it triggers a rule in the parallel inductive system.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.05a
• /
• pp.132-135
• /
• 2021

최근 네트워크의 확장으로 인한 공격 벡터의 증가로 외부자뿐 아니라 내부자를 경계해야 할 필요성이 증가함에 따라, 이를 다룬 보안 모델인 제로트러스트 모델이 주목받고 있다. 이 논문에서는 reverse proxy 와 사용자 패턴 인식 AI 를 이용한 제로트러스트 아키텍처를 제시하며 제로트러스트의 구현 가능성을 보이고, 새롭고 효율적인 전처리 과정을 통해 효과적으로 사용자를 인증할 수 있음을 제시한다. 이를 위해 사용자별로 마우스 사용 패턴, 리소스 사용 패턴을 인식하는 딥러닝 모델을 설계하였다. 끝으로 제로트러스트 모델에서 사용자 패턴 인식의 활용 가능성과 확장성을 보인다.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.5
• /
• pp.9-15
• /
• 2018

With the advent of the 5G communication era recently, advancement of the convergence technologies related to IoT has been progressed rapidly. IoT convergence technologies using various sensors are actively applied many fields in our lives, and it contributes to the popularization of these convergence technologies among many people successfully. The security problem of the IoT which connects many things on the network is critically vulnerable and is one of the most important challenge to be solved urgently. In this paper, we design an intrusion detection and self-treatment system for IoT, which can detect external attacks and anomalies in order to solve the security problems in IoT, perform self-treatment by operating the vaccine program according to the intrusion type whenever it detects certain intrusion. Furthermore, we consider the broadcasting of intrusion alarm message according to the frequency of similar circumstances in order to block intrusion contagious in IoT.

• Journal of Internet Computing and Services
• /
• v.9 no.6
• /
• pp.37-48
• /
• 2008

VoIP service is a transmission of voice data using SIP protocol on IP based network, The SIP protocol has many advantages such as providing IP based voice communication and multimedia service with cheap communication cost and so on. Therefore the SIP protocol spread out very quickly. But, SIP protocol exposes new forms of vulnerabilities on malicious attacks such as Message Flooding attack and protocol parsing attack. And it also suffers threats from many existing vulnerabilities like on IP based protocol. In this paper, we propose a new Virtual Proxy Server system in front of the existed Proxy Server for anomaly detection of SIP attack and stateful management of SIP session with enhanced security. Based on stateful virtual proxy server, out solution shows promising SIP Message Flooding attack verification and detection performance with minimized latency on SIP packet transmission.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.27 no.6
• /
• pp.1431-1439
• /
• 2017

In the security field, log analysis is important to detect malware or abnormal behavior. Recently, image visualization techniques for malware dectection becomes to a major part of security. These techniques can also be used in online games. Users can leave a game when they felt bad experience from game bot, automatic hunting programs, malicious code, etc. This churning can damage online game's profit and longevity of service if game operators cannot detect this kind of events in time. In this paper, we propose a new technique of PNG image conversion based churn prediction to improve the efficiency of data analysis for the first. By using this log compression technique, we can reduce the size of log files by 52,849 times smaller and increase the analysis speed without features analysis. Second, we apply data mining technique to predict user's churn with a real dataset from Blade & Soul developed by NCSoft. As a result, we can identify potential churners with a high accuracy of 97%.

• Journal of Internet Computing and Services
• /
• v.12 no.2
• /
• pp.85-101
• /
• 2011

The recommender system analyzes users' preference and predicts the users' preference to items in order to recommend various items such as book, movie and music for the users. The collaborative filtering method is used most widely in the recommender system. The method uses rating information of similar users when recommending items for the target users. Performance of the collaborative filtering-based recommendation is lowered when attacker maliciously manipulates the rating information on items. This kind of malicious act on a recommender system is called 'Recommendation Attack'. When the evaluation data that are in continuous change are analyzed in the perspective of data stream, it is possible to predict attack on the recommender system. In this paper, we will suggest the method to detect attack on the recommender system by using the stream trend of the item evaluation in the collaborative filtering-based recommender system. Since the information on item evaluation included in the evaluation data tends to change frequently according to passage of time, the measurement of changes in item evaluation in a fixed period of time can enable detection of attack on the recommender system. The method suggested in this paper is to compare the evaluation stream that is entered continuously with the normal stream trend in the test cycle for attack detection with a view to detecting the abnormal stream trend. The proposed method can enhance operability of the recommender system and re-usability of the evaluation data. The effectiveness of the method was verified in various experiments.

• Journal of the Korea Convergence Society
• /
• v.9 no.7
• /
• pp.1-7
• /
• 2018

Feature selection, one of data preprocessing techniques, is one of major research areas in many applications dealing with large dataset. It has been used in pattern recognition, machine learning and data mining, and is now widely applied in a variety of fields such as text classification, image retrieval, intrusion detection and genome analysis. The proposed method is based on a genetic algorithm which is one of meta-heuristic algorithms. There are two methods of finding feature subsets: a filter method and a wrapper method. In this study, we use a wrapper method, which evaluates feature subsets using a real classifier, to find an optimal feature subset. The training dataset used in the experiment has a severe class imbalance and it is difficult to improve classification performance for rare classes. After preprocessing the training dataset with SMOTE, we select features and evaluate them with various machine learning algorithms.