• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4204-4222
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• 2015

Cloud is the latest buzz word in the internet community among developers, consumers and security researchers. There have been many attacks on the cloud in the recent past where the services got interrupted and consumer privacy has been compromised. Denial of Service (DoS) attacks effect the service availability to the genuine user. Customers are paying to use the cloud, so enhancing the availability of services is a paramount task for the service provider. In the presence of DoS attacks, the availability is reduced drastically. Such attacks must be detected and prevented as early as possible and the power of computational approaches can be used to do so. In the literature, machine learning techniques have been used to detect the presence of attacks. In this paper, a novel approach is proposed, where intelligent rule based feature selection and classification are performed for DoS attack detection in the cloud. The performance of the proposed system has been evaluated on an experimental cloud set up with real time DoS tools. It was observed that the proposed system achieved an accuracy of 98.46% on the experimental data for 10,000 instances with 10 fold cross-validation. By using this methodology, the service providers will be able to provide a more secure cloud environment to the customers.

• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.594-600
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• 2010

Many studies are DDoS in Internet network, but the study is the fact that is not enough in a voice network. Therefore, we designed the extended TRW algorithm that was a DDoS attack traffic detection algorithm for the voice network which used an IP data network to solve upper problems in this article and evaluated it. The algorithm that is proposed in this paper analyzes TRW algorithm to detect existing DDoS attack in Internet network and, design connection and end connection to apply to a voice network, define probability function to count this. For inspect the algorithm, Set a threshold and using NS-2 Simulator. We measured detection rate by an attack traffic type and detection time by attack speed. At the result of evaluation 4.3 seconds for detection when transmitted INVITE attack packets per 0.1 seconds and 89.6% performance because detected 13,453 packet with attack at 15,000 time when transmitted attack packet.

• Journal of IKEEE
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• v.27 no.4
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• pp.531-536
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• 2023

This paper proposes a sliding window method with frame feature insertion for immediate attack detection on in-vehicle networks. This method guarantees real-time attack detection by labeling based on the attack status of the current frame. Experiments show that the proposed method improves detection performance by giving more weight to the current frame in CNN computation. The proposed model was designed based on a lightweight LeNet-5 architecture and it achieves 100% detection for DoS attacks. Additionally, by comparing the complexity with conventional models, the proposed model has been proven to be more suitable for resource-constrained devices like ECUs.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.814-816
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• 2002

최근 급증하고 있는 인터넷 사용자들을 위한 인터 서비스 업체들의 증가와 더불어 악의적인 공격자의공격 또한 증가하고 있다. 이러한 공격으로 인한 인터넷 업체들에게 치명적일 수 있는 신용에 대한 불신임과 서비스의 불안정이라는 피해는 기업의 이미지를 실추시키는 등 막대한 영향을 끼칠 수도 있다. 이러한 악의적인 공격 형태 중 가장 최근 가장 빈번하게 그리고 큰 피해를 주는 공격형태가 DoS(Denial-of-Service)[1]공격이다. 그러나 DoS공격에 대한 적당한 대응방법이 아직까지 미비한 상태이고, 공격에 대응하여 방어한다고 해도 그 진원지를 찾아내지 못한다면 추후 동일한 공격자(attack)에 의해 재차 공격을 받을 가능성을 배제할 수 없는 실정이다. 이에 본 논문은 DoS공격에 대한 적당한 대응하는 하나의 방법으로 공격 경로(attack path)를 찾아내고 더 나아가 공격 진원지(attack origin)의 MAC address를 알아냄으로써 공격의 진원지를 찾아내는 방법을 제안한다.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.189-194
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• 2005

Present hacking technology is undergone a change on the distributed DoS Attack which cause a lot of traffic to the network or single host. In this paper, with giving mobility to the mean deviation per protocol and it's field, and with adapting pattern matching approach to DDoS attack detection technique, we propose a method to detect the DDoS attack, to have less misdetection and to detect these attacks correctly.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.467-476
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• 2016

With the development of information technology, Small office Home office(SOHO) is picking up. SOHO generally uses Wi-Fi. The wireless LAN environment using 802.11 protocol is easily affected by DoS attacks. To deal with these threats, there is Wireless Intrusion Detection System(WIDS). However, legacy products of WIDS cannot be easily used by SOHO because they are expensive and require management burden. In this paper, Smart WIDS for SOHO is proposed and implemented on Raspberry Pi2. And, it provides the interface for attack detection notice to android smart phone. Smart WIDS detects Masquerading DoS and Resource Depletion DoS based on IEEE 802.11 so that we notice the attempt of cracking Pre-shared Key(PSK), Man-In-The-Middle(MITM), and service failure.

• Proceedings of the Korean Society of Computer Information Conference
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• 2008.06a
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• pp.175-182
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• 2008

2008년에 있었던 우리나라 금융기관과 정부기관에 대한 DoS 공격에 대한 연구이다. 실험실 환경에서 실제 DoS 공격 툴을 이용하여 공격을 실시한다. DoS 공격을 탐지하기 위하여 네트워크 상에서 Snort를 이용한 N-IDS를 설치하고, 패킷을 탐지하기 위한 Winpcap과 패킷의 저장 및 분석하기 위한 MySQL, HSC, .NET Framework 등을 설치한다. e-Watch 등의 패킷 분석 도구를 통해 해커의 DoS 공격에 대한 패킷량과 TCP, UDP 등의 정보, Port, MAC과 IP 정보 등을 분석한다. 본 논문 연구를 통하여 유비쿼터스 정보화 사회의 역기능인 사이버 DoS, DDoS 공격에 대한 자료를 분석하여 공격자에 대한 포렌식자료 및 역추적 분석 자료를 생성하여 안전한 인터넷 정보 시스템을 확보하는데 의의가 있다.

• 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 Communications and Networks
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• v.12 no.4
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• pp.375-381
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• 2010

A flooding attack, such as DoS or Worm, can be easily created or even downloaded from the Internet, thus, it is one of the main threats to servers on the Internet. This paper presents an online real-time network response system, which can determine whether a LAN is suffering from a flooding attack within a very short time unit. The detection engine of the system is based on the incremental mining of fuzzy association rules from network packets, in which membership functions of fuzzy variables are optimized by a genetic algorithm. The incremental mining approach makes the system suitable for detecting, and thus, responding to an attack in real-time. This system is evaluated by 47 flooding attacks, only one of which is missed, with no false positives occurring. The proposed online system belongs to anomaly detection, not misuse detection. Moreover, a mechanism for dynamic firewall updating is embedded in the proposed system for the function of eliminating suspicious connections when necessary.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.179-191
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• 2024

With the advancement of modern technology, cyber-attacks are always rising. Specialized defense systems are needed to protect organizations against these threats. Malicious behavior in the network is discovered using security tools like intrusion detection systems (IDS), firewall, antimalware systems, security information and event management (SIEM). It aids in defending businesses from attacks. Delivering advance threat feeds for precise attack detection in intrusion detection systems is the role of cyber-threat intelligence (CTI) in the study is being presented. In this proposed work CTI feeds are utilized in the detection of assaults accurately in intrusion detection system. The ultimate objective is to identify the attacker behind the attack. Several data sets had been analyzed for attack detection. With the proposed study the ability to identify network attacks has improved by using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.