• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.924-935
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• 2023

Purpose: The central aim of this study is to leverage machine learning techniques for the classification of Intrusion Detection System (IDS) data, with a specific focus on identifying the variables responsible for enhancing overall performance. Method: First, we classified 'R2L(Remote to Local)' and 'U2R (User to Root)' attacks in the NSL-KDD dataset, which are difficult to detect due to class imbalance, using seven machine learning models, including Logistic Regression (LR) and K-Nearest Neighbor (KNN). Next, we use the SHapley Additive exPlanation (SHAP) for two classification models that showed high performance, Random Forest (RF) and Light Gradient-Boosting Machine (LGBM), to check the importance of variables that affect classification for each model. Result: In the case of RF, the 'service' variable and in the case of LGBM, the 'dst_host_srv_count' variable were confirmed to be the most important variables. These pivotal variables serve as key factors capable of enhancing performance in the context of classification for each respective model. Conclusion: In conclusion, this paper successfully identifies the optimal models, RF and LGBM, for classifying 'R2L' and 'U2R' attacks, while elucidating the crucial variables associated with each selected model.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1841-1848
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• 2013

In this paper, the plans for improvement of real-time security monitoring accuracy and expansion of control region were investigated through comprehensive and systematic collection and analysis of the anomalous activities that inflow and outflow in the network on a large scale in order to overcome the existing security monitoring system based on stylized detection patterns which could correspond to only very limited cyber attacks. This study established an anomaly observation system to collect, store and analyze a diverse infringement threat information flowing into the darknet network, and presented the information classification system of cyber threats, unknown anomalies and high-risk anomalous activities through the statistics based trend analysis of hacking. If this security monitoring system utilizing darknet traffic as presented in the study is applied, it was indicated that detection of all infringement threats was increased by 12.6 percent compared with conventional case and 120 kinds of new type and varietal attacks that could not be detected in the past were detected.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.155-166
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• 2005

Currently, almost of all the Internet users have as many IDs as the number of sites they have subscribed for, The users should memorize and input every independent ID and password whenever they want to login to the system. Although ID management system is used to solve this problem, 10 and password management will become more complicated in the forthcoming ubiquitous computing environments because so many computers will be interconnected on various kinds of wired and wireless networks, Furthermore, it is not enough to use the existing single Circle of Trust (COT) ID management system for the forthcoming computing environments. To solve this problem, the paper proposes ID federation models in multiple COT domain and implements an ID federation system that can be scaled to mobile computing environment as well as wired computing environment. The proposed ID federation models has been verified to operate with no problem between the systems in different trust domains by doing the ID scaability test.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.473-482
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• 2007

Network-based IDS(Intrusion Detection System) gathers network packet data and analyzes them into attack or normal. They raise alarm when possible intrusion happens. But they often output a large amount of low-level of incomplete alert information. Consequently, a large amount of incomplete alert information that can be unmanageable and also be mixed with false alerts can prevent intrusion response systems and security administrator from adequately understanding and analyzing the state of network security, and initiating appropriate response in a timely fashion. So it is important for the security administrator to reduce the redundancy of alerts, integrate and correlate security alerts, construct attack scenarios and present high-level aggregated information. False alarm rate is the ratio between the number of normal connections that are incorrectly misclassified as attacks and the total number of normal connections. In this paper we propose a false alarm classification model to reduce the false alarm rate using classification analysis of data mining techniques. The proposed model can classify the alarms from the intrusion detection systems into false alert or true attack. Our approach is useful to reduce false alerts and to improve the detection rate of network-based intrusion detection systems.

• Journal of the Korea Computer Industry Society
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• v.4 no.12
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• pp.965-974
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• 2003

Today's network consists of a large number of routers and servers running a variety of applications. In this paper, we have designed and constructed the general simulation environment of network security model composed of multiple IDSs agent and a firewall agent which coordinate by CNP (Contract Net Protocol). The CNP, the methodology for efficient integration of computer systems on heterogeneous environment such as distributed systems, is essentially a collection of agents, which cooperate to resolve a problem. Command console in the CNP is a manager who controls the execution of agents or a contractee, who performs intrusion detection. In the knowledge-based network security model, each model of simulation environment is hierarchically designed by DEVS (Discrete Event system Specification) formalism. The purpose of this simulation is the application of rete pattern-matching algorithm speeding up the inference cycle phases of the intrusion detection expert system. we evaluate the characteristics and performance of CNP architecture with rete pattern-matching algorithm.

• The KIPS Transactions:PartC
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• v.11C no.1
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• pp.39-46
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• 2004

It has been happening to transfer not only the general information but also the valuable information through the universal Internet. So security accidents as the expose of secret data and document increase. But we don't have stable structure for transmitting important data. Accordingly, in this paper we intend to use network based Intrusion Detection System modules and detect the extrusion of important data through the network, and propose and design the method for investigating concealment data to protect important data and investigate the secret document against the terrorism. We analyze the method for investigating concealment data, especially we use existing steganalysis techniques, so we propose and design the module emphasizing on the method for investigating stego-data in E-mail of attach files or Web-data of JPG, WAVE etc. Besides, we analyze the outcome through the experiment of the proposed stego-data detection system.

• The KIPS Transactions:PartC
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• v.14C no.1 s.111
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• pp.1-6
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• 2007

In order to operate a secure network, it is very important for the network to raise positive detection as well as lower negative detection for reducing the damage from network intrusion. By using SVM on the intrusion detection field, we expect to improve real-time detection of intrusion data. However, due to classification based on calculating values after having expressed input data in vector space by SVM, continuous data type can not be used as any input data. Therefore, we present the hybrid model between SVM and decision tree method to make up for the weak point. Accordingly, we see that intrusion detection rate, F-P error rate, F-N error rate are improved as 5.6%, 0.16%, 0.82%, respectively.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.2
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• pp.73-80
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• 2009

Security is critically important for ubiquitous sensor networks that are usually used for the military and serveillance in environments that are opened to attacks, such as, eavesdroping, replay attacks of abnormal messages, forgery of the messages to name a few. ZigBee has emerged as a strong contender for ubiquitous sensor networks. ZigBee is used for low data rate and low power wireless sensor network applications. To deploy ubiquitous sensor networks, the collected information requires protection from an adversary over the network in many cases. The security mechanism should be provided for collecting the information over the network. However, the ZigBee protocol has some security weaknesses. In this paper, these weaknesses are discussed and a method to improve security aspect of the ZigBee protocol is presented along with a comparison of the message complexity of the proposed security protocol with that of the current ZigBee protocol.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.605-612
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• 2011

Recently, cyber attacks against public communications networks are getting more complicated and varied. Moreover, in some cases, one country could make systematic attacks at a national level against another country to steal its confidential information and intellectual property. Therefore, the issue of cyber attacks is now regarded as a new major threat to national security. The conventional way of operating individual information security systems such as IDS and IPS may not be sufficient to cope with those attacks committed by highly-motivated attackers with significant resources. In this paper, we discuss the technologies and standard trends about actual cyber threat and response methods, design the collaborative response framework based on the security information sharing in the inter-domain environments. The computation method of network threat level based on the collaborative response framework is proposed. The network threats are be quickly detected and real-time response can be executed using the proposed computation method.

• Convergence Security Journal
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• v.9 no.4
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• pp.21-27
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• 2009

In the environment of development of network bandwidth and intrusion technology there is limit to the pattern analysis of all massed packets through the existing pattern matching method by the intrusion detection system. To detect the packets efficiently when they are received fragmented, it has been presented the matching method only the pattern of packets consisting with the operation system such as Esnort. Pattern matching performance is improved through the use of NMAP, the basic mechanism od Esnort, by scanning the operation system of the same network system and appling pattern match selectively scanned information and the same operation system as the received packets. However, it can be appeared the case of disregarding the receivied packets depending on the diversity of the kind of operation systems and recognition mistake of operation system of nmap. In this paper, we present and verify the improved intrusion detection system shortening the pattern matching time by the creation of hashy table through the pattern hash of intrusion detection system independently with the users system environment .in the state of flux.