• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.876-880
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• 2008

Outliers are the observations which are very larger or smaller than most observations in the given data set. These are shown by some sources. The result of the analysis with outliers may be depended on them. In general, we do data analysis after removing outliers. But, in data mining applications such as fraud detection and intrusion detection, outliers are included in training data because they have crucial information. In regression models, simple and multiple regression models need to eliminate outliers from given training data by standadized and studentized residuals to construct good model. In this paper, we use support vector regression(SVR) based on statistical teaming theory to analyze data with outliers in regression. We verify the improved performance of our work by the experiment using synthetic data sets.

• Smart Media Journal
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• v.4 no.4
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• pp.80-85
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• 2015

As the number of applications using the internet the rapidly increasing incidence of cyber attacks made on the internet has been increasing. In the equipment of L3 DDoS attack detection equipment in the world and incomplete detection of application layer based intelligent. Next-generation networks domestic product in high-performance wired and wireless network threat response techniques to meet the diverse requirements of the security solution is to close one performance is insufficient compared to the situation in terms of functionality foreign products, malicious code detection and signature generation research primarily related to has progressed malware detection and analysis of the research center operating in Window OS. In this paper, we describe the current status survey and analysis of the latest variety of new attack techniques and analytical skills with the latest cyber-attack analysis prejudice the security situation.

• Journal of Digital Convergence
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• v.13 no.1
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• pp.313-319
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• 2015

As the number of applications using the internet the rapidly increasing incidence of cyber attacks made on the internet has been increasing. In the equipment of L3 DDoS attack detection equipment in the world and incomplete detection of application layer based intelligent. Next-generation networks domestic product in high-performance wired and wireless network threat response techniques to meet the diverse requirements of the security solution is to close one performance is insufficient compared to the situation in terms of functionality foreign products, malicious code detection and signature generation research primarily related to has progressed malware detection and analysis of the research center operating in Window OS. In this paper, we describe the current status survey and analysis of the latest variety of new attack techniques and analytical skills with the latest cyber-attack analysis prejudice the security situation.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.65-75
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• 2017

The Detection Model is the model to find the result of a certain purpose using artificial intelligent, data mining, intelligent algorithms In Cyber Security, it usually uses to detect intrusion, malwares, cyber incident, and attacks etc. There are an amount of unlabeled data that are collected in a real environment such as security data. Since the most of data are not defined the class labels, it is difficult to know type of data. Therefore, the label determination process is required to detect and analysis with accuracy. In this paper, we proposed a KDFL(K-means and D-S Fusion based Labeling) method using D-S inference and k-means(unsupervised) algorithms to decide label of data records by fusion, and a detection model architecture using a proposed labeling method. A proposed method has shown better performance on detection rate, accuracy, F1-measure index than other methods. In addition, since it has shown the improved results in error rate, we have verified good performance of our proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.576-599
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• 2013

Protocol reverse engineering is useful for many security applications, including intelligent fuzzing, intrusion detection and fingerprint generation. Since manual reverse engineering is a time-consuming and tedious process, a number of automatic techniques have been proposed. However, the accuracy of these techniques is limited due to the complexity of binary instructions, and the derived formats have missed constraints that are critical for security applications. In this paper, we propose a new approach for protocol format extraction. Our approach reasons about only the evaluation behavior of a program on the input message from concolic execution, and enables field identification and constraint inference with high accuracy. Moreover, it performs binary analysis with low complexity by reducing modern instruction sets to BIL, a small, well-specified and architecture-independent language. We have implemented our approach into a system called Icefex and evaluated it over real-world implementations of DNS, eDonkey, FTP, HTTP and McAfee ePO protocols. Experimental results show that our approach is more accurate and effective at extracting protocol formats than other approaches.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1313-1322
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• 2016

Recently, many virus and hacking attacks on public organizations and financial institutions by internet are becoming increasingly intelligent and sophisticated. The Advanced Persistent Threat has been considered as an important cyber risk. This attack is basically accomplished by spreading malicious codes through complex networks. To detect and extract PE files in smart manufacturing industry networks, an efficient processing method which is performed before analysis procedure on malicious codes is proposed. We implement a preprocessor of open intrusion detection system Snort for fast extraction of PE files and install on a hardware sensor equipment. As a result of practical experiment, we verify that the network sensor can extract the PE files which are often suspected as a malware.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.351-360
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• 2006

Proposed security system attacks it, and detect it, and a filter generation, a business to be prompt of interception filtering dates at attack information public information. inner IPS to attack detour setting and a traffic band security, different connection security system, and be attack packet interceptions and service and port interception setting. Exchange new security rule and packet filtering for switch type implementation through dynamic reset memory by real time, and deal with a packet. The attack detection about DDoS, SQL Stammer, Bug bear, Opeserv worm etc. of the 2.5 Gbs which was an attack of a hacker consisted in network performance experiment by real time. Packet by attacks of a hacker was cut off, and ensured the normal inside and external network resources besides the packets which were normal by the results of active renewal.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• Convergence Security Journal
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• v.6 no.3
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• pp.1-12
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• 2006

Malicious codes have been widely documented and detected in information security breach occurrences of Microsoft Windows platform. Legacy information security systems are particularly vulnerable to breaches, due to Window kernel-based malicious codes, that penetrate existing protection and remain undetected. To date there has not been enough quality study into and information sharing about Windows kernel and inner code mechanisms, and this is the core reason for the success of these codes into entering systems and remaining undetected. This paper focus on classification and formalization of type target and mechanism of various Windows kernel-based attacks, and will present suggestions for effective response methodologies in the categories of, "Kernel memory protection", "Process & driver protection" and "File system & registry protection". An effective Windows kernel protection system will be presented through the collection and analysis of Windows kernel and inside mechanisms, and through suggestions for the implementation methodologies of unreleased and new Windows kernel protection skill. Results presented in this paper will explain that the suggested system be highly effective and has more accurate for intrusion detection ratios, then the current legacy security systems (i.e., virus vaccines and Windows IPS, etc) intrusion detection ratios. So, It is expected that the suggested system provides a good solution to prevent IT infrastructure from complicated and intelligent Windows kernel attacks.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.55-62
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• 2010

Monitoring systems are able to report certain events at region of interest(ROI) and to take an appropriate action. From industrial product line full of robots to fire detection, intrusion detection, smart grid application, environmental pollution alarm system, monitoring system has widely used in diverse industry sector. Recently, due to advance of wireless communication technology and availability of low cost sensors, intelligent and/or smart monitoring systems such as sensor networks has been developed. Several deployment methods are introduced to meet various monitoring needs and deployment performance criteria are also summarized to be used to identify weak point and be useful at designing monitoring systems. Both efficiency during deployment and usefulness after the deployment should be assessed. Efficiency factors during deployment are elapsed time, energy required, deployment cost, safety, sensor node failure rate, scalability. Usefulness factors after deployment are ROI coverage, connectivity, uniformity, target density similarity, energy consumption rate per unit time and so on.