• The KIPS Transactions:PartC
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• v.15C no.5
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• pp.351-358
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• 2008

Recently, as network flooding attacks such as DoS/DDoS and Internet Worm have posed devastating threats to network services, rapid detection and proper response mechanisms are the major concern for secure and reliable network services. However, most of the current Intrusion Detection Systems(IDSs) focus on detail analysis of packet data, which results in late detection and a high system burden to cope with high-speed network environment. In this paper we propose a lightweight and fast detection mechanism for traffic flooding attacks. Firstly, we use SNMP MIB statistical data gathered from SNMP agents, instead of raw packet data from network links. Secondly, we use a machine learning approach based on a Support Vector Machine(SVM) for attack classification. Using MIB and SVM, we achieved fast detection with high accuracy, the minimization of the system burden, and extendibility for system deployment. The proposed mechanism is constructed in a hierarchical structure, which first distinguishes attack traffic from normal traffic and then determines the type of attacks in detail. Using MIB data sets collected from real experiments involving a DDoS attack, we validate the possibility of our approaches. It is shown that network attacks are detected with high efficiency, and classified with low false alarms.

• Journal of KIISE:Information Networking
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• v.37 no.4
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• pp.255-262
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• 2010

Recently, automated signature generation systems(ASGSs) have been developed in order to cope with zero-day attacks with malicious codes exploiting vulnerabilities which are not yet publically noticed. To enhance the usefulness of the signatures generated by (ASGSs) it is essential to identify signatures only with the high accuracy of intrusion detection among a number of generated signatures and to provide them to target security systems in a timely manner. This automated signature exchange, distribution, and update operations have to be performed in a secure and universal manner beyond the border of network administrations, and also should be able to eliminate the noise in a signature set which causes performance degradation of the security systems. In this paper, we present a system architecture to support the identification of high quality signatures and to share them among security systems through a scheme which can evaluate the detection accuracy of individual signatures, and also propose a set of algorithms dealing with exchanging, distributing and updating signatures. Though the experiment on a test-bed, we have confirmed that the high quality signatures are automatically saved at the level that the noise rate of a signature set is reduced. The system architecture and the algorithm proposed in the paper can be adopted to a automated signature sharing framework.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.69-74
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• 2021

Internet access is also increasing as online activities increase due to the influence of Corona 19. However, network attacks are also diversifying by malicious users, and DDoS among the attacks are increasing year by year. These attacks are detected by intrusion detection systems and can be prevented at an early stage. Various data sets are used to verify intrusion detection algorithms, but in this paper, CICIDS2017, the latest traffic, is used. DDoS attack traffic was analyzed using the decision tree. In this paper, we analyzed the traffic by using the decision tree. Through the analysis, a decisive feature was found, and the accuracy of the decisive feature was confirmed by proceeding the decision tree to prove the accuracy of detection. And the contents of false positive and false negative traffic were analyzed. As a result, learning the feature and the two features showed that the accuracy was 98% and 99.8% respectively.

• The KIPS Transactions:PartC
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• v.11C no.3
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• pp.319-326
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• 2004

To help network intrusion detection systems(NIDSs) keep up with the demands of today's networks, that we the increasing network throughput and amount of attacks, a radical new approach in hardware and software system architecture is required. In this paper, we propose a Network Processor(NP) based In-Line mode NIDS that supports the packet payload inspection detecting the malicious behaviors, as well as the packet filtering and the traffic metering. In particular, we separate the filtering and metering functions from the deep packet inspection function using two-level searching scheme, thus the complicated and time-consuming operation of the deep packet inspection function does not hinder or flop the basic operations of the In-line mode system. From a proto-type NP-based NIDS implemented at a PC platform with an x86 processor running Linux, two Gigabit Ethernet ports, and 2.5Gbps Agere PayloadPlus(APP) NP solution, the experiment results show that our proposed scheme can reliably filter and meter the full traffic of two gigabit ports at the first level even though it can inspect the packet payload up to 320 Mbps in real-time at the second level, which can be compared to the performance of general-purpose processor based Inspection. However, the simulation results show that the deep packet searching is also possible up to 2Gbps in wire speed when we adopt 10Gbps APP solution.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.237-245
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• 2022

The prominence of IoTs (Internet of Things) and exponential advancement of computer networks has resulted in massive essential applications. Recognizing various cyber-attacks or anomalies in networks and establishing effective intrusion recognition systems are becoming increasingly vital to current security. MLTs (Machine Learning Techniques) can be developed for such data-driven intelligent recognition systems. Researchers have employed a TFDNNs (Tensor Flow Deep Neural Networks) and DCNNs (Deep Convolution Neural Networks) to recognize pirated software and malwares efficiently. However, tuning the amount of neurons in multiple layers with activation functions leads to learning error rates, degrading classifier's reliability. HTFDNNs ( Hybrid tensor flow DNNs) and MRNs (Modified Residual Networks) or Resnet CNNs were presented to recognize software piracy and malwares. This study proposes HTFDNNs to identify stolen software starting with plagiarized source codes. This work uses Tokens and weights for filtering noises while focusing on token's for identifying source code thefts. DLTs (Deep learning techniques) are then used to detect plagiarized sources. Data from Google Code Jam is used for finding software piracy. MRNs visualize colour images for identifying harms in networks using IoTs. Malware samples of Maling dataset is used for tests in this work.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.1
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• pp.267-282
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• 2000

With the development of computer&network technology and the growth of its dependance, computer failures not only lose human and material resources but also make organization's competition weak as a side-effect of information society. Therefore, people consider computer security as important factor. Intrusion Detection Systems (IDS) detect intrusions and take an appropriate action against them in order to protect a computer from system failure due to illegal intrusion. A variety of methods and models for IDS have been developed until now, but the existing methods or models aren't enough to detect intrusions because of the complexity of computer network the vulnerability of the object system, insufficient understanding for information security and the appearance of new illegal intrusion method. We propose a new IDS model to be suitable at the information system organized by homogeneous hosts and design for the IDS model and implement the prototype of it for feasibility study. The IDS model consist of many distributed unit sensor IDSs at homogeneous hosts and if any of distributed unit sensor IDSs detect anomaly system call among system call sequences generated by a process, the anomaly system call can be dynamically shared with other unit sensor IDSs. This makes the IDS model can effectively detect new intruders about whole information system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2781-2800
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• 2016

Emerging attacks aim to access proprietary assets and steal data for business or political motives, such as Operation Aurora and Operation Shady RAT. Skilled Intruders would likely remove their traces on targeted hosts, but their network movements, which are continuously recorded by network devices, cannot be easily eliminated by themselves. However, without complete knowledge about both inbound/outbound and internal traffic, it is difficult for security team to unveil hidden traces of intruders. In this paper, we propose an autonomous anomaly detection system based on behavior profiling and relation mining. The single-hop access profiling model employ a novel linear grouping algorithm PSOLGA to create behavior profiles for each individual server application discovered automatically in historical flow analysis. Besides that, the double-hop access relation model utilizes in-memory graph to mine time-sequenced access relations between different server applications. Using the behavior profiles and relation rules, this approach is able to detect possible anomalies and violations in real-time detection. Finally, the experimental results demonstrate that the designed models are promising in terms of accuracy and computational efficiency.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.411-418
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• 2020

In the field of information security, IDS(Intrusion Detection System) is normally classified in two different categories: signature-based IDS and anomaly-based IDS. Many studies in anomaly-based IDS have been conducted that analyze network traffic data generated in cyberspace by machine learning algorithms. In this paper, we studied pre-processing methods to overcome performance degradation problems cashed by rare classes. We experimented classification performance of a Machine Learning algorithm by reconstructing data set based on rare classes and semi rare classes. After reconstructing data into three different sets, wrapper and filter feature selection methods are applied continuously. Each data set is regularized by a quantile scaler. Depp neural network model is used for learning and validation. The evaluation results are compared by true positive values and false negative values. We acquired improved classification performances on all of three data sets.

• Journal of Information Technology Services
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• v.2 no.2
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• pp.97-109
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• 2003

As the importance and the need for network security is increased, many organization uses the various security systems. They enable to construct the consistent integrated security environment by sharing the vulnerable information among firewall, intrusion detection system, and vulnerable scanner. And Policy-based network provides a means by which the management process can be simplified and largely automated. In this article we build a foundation of policy-based network modeling environment. The procedure and structure for policy rule induction from vulnerabilities stored in SVDB (Simulation based Vulnerability Data Based) is conducted. It also transforms the policy rules into PCIM (Policy Core Information Model).

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.171-174
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• 2001

기존의 침입 탐지 시스템(Intrusion Detection System)은 점점 복잡해져 가는 네트워크, 다양화되고 지능화되는 해킹 기술과 바이러스의 공격으로부터 시스템을 보호하기 위해 처리해야 하는 정보의 양과 복잡한 알고리즘으로 인해 실시간 서비스의 구현이 힘들다는 문제점이 있다. 본 논문에서는 시스템, 네트워크 리소스의 효율적인 분배를 통해 실시간으로 침입자를 탐지할 수 있는 네트워크 토폴로지 즉, 디지털 면역 네트워크(Digital Immune Network, DIN)를 제시한다. DIN은 침입의 탐지를 위하여 생체 면역 시스템의 B세포, T세포 개념의 알고리즘이 적용되고, 견고성 향상을 위해 메쉬 네트워크 구조가 적용되어 호스트 연합(Host Alliance)을 구성함으로써 호스트들의 병렬처리를 통해 리소스 낭비를 막고 실시간 서비스가 제공될 수 있도록 하였다.