• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.42-49
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• 2021

As cyber attacks become more intelligent, there is difficulty in detecting advanced attacks in various fields such as industry, defense, and medical care. IPS (Intrusion Prevention System), etc., but the need for centralized integrated management of each security system is increasing. In this paper, we collect big data for intrusion detection and build an intrusion detection platform using deep learning and CNN (Convolutional Neural Networks). In this paper, we design an intelligent big data platform that collects data by observing and analyzing user visit logs and linking with big data. We want to collect big data for intrusion detection and build an intrusion detection platform based on CNN model. In this study, we evaluated the performance of the Intrusion Detection System (IDS) using the KDD99 dataset developed by DARPA in 1998, and the actual attack categories were tested with KDD99's DoS, U2R, and R2L using four probing methods.

• 인터넷정보학회논문지
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• 제7권3호
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• pp.119-132
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• 2006

지금까지 잘 알려진 네트워크 기반 보안 기법들은 공격에 수동적이고 우회한 공격이 가능하다는 취약점을 가지고 있어 인라인(in_line) 모드의 공격에 능동적 대응이 가능한 오용탐지 기반의 침입방지시스템의 출현이 불가피하다. 하지만 오용탐지 기반의 침입방지시스템은 탐지 규칙에 비례하여 과도한 오경보(False Alarm)를 발생시켜 정상적인 네트워크 흐름을 방해하는 잘못된 대응으로 이어질 수 있어 기존 침입탐지시스템보다 더 위험한 문제점을 갖고 있으며, 새로운 변형 공격에 대한 탐지가 미흡하다는 단점이 있다. 본 논문에서는 이러한 문제를 보완하기 위해 오용탐지 기반의 침입방지시스템과 Anomaly System 중의 하나인 서포트 벡터 머신(Support Vector Machines; 이하 SVM)을 이용한 침입방지시스템 기술을 제안한다. 침입 방지시스템의 탐지 패턴을 SVM을 이용하여 진성경보만을 처리하는 기법으로 실험결과 기존 침입방지시스템과 비교하여, 약 20% 개선된 성능결과를 보였으며, 제안한 침입방지시스템 기법을 통하여 오탐지를 최소화하고 새로운 변종 공격에 대해서도 효과적으로 탐지 가능함을 보였다.

• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.1-12
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• 2022

Besides unexpected growth perceived by IoT's, the variety and volume of threats have increased tremendously, making it a necessity to introduce intrusion detections systems for prevention and detection of such threats. But Intrusion Detection and Prevention System (IDPS) inside the IoT network yet introduces some unique challenges due to their unique characteristics, such as privacy inference, performance, and detection rate and their frequency in the dynamic networks. Our research is focused on the privacy inferences of existing intrusion prevention and detection system approaches. We also tackle the problem of providing unified a solution to implement the open-source IDPS in the IoT architecture for assessing the performance of IDS by calculating; usage consumption and detection rate. The proposed scheme is considered to help implement the human health monitoring system in IoT networks

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.179-192
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• 2023

The widespread use of Cloud Computing, Internet of Things (IoT), and social media in the Information Communication Technology (ICT) field has resulted in continuous and unavoidable cyber-attacks on users and critical infrastructures worldwide. Traditional security measures such as firewalls and encryption systems are not effective in countering these sophisticated cyber-attacks. Therefore, Intrusion Detection and Prevention Systems (IDPS) are necessary to reduce the risk to an absolute minimum. Although IDPSs can detect various types of cyber-attacks with high accuracy, their performance is limited by a high false alarm rate. This study proposes a new technique called Fuzzy Logic - Objective Risk Analysis (FLORA) that can significantly reduce false positive alarm rates and maintain a high level of security against serious cyber-attacks. The FLORA model has a high fuzzy accuracy rate of 90.11% and can predict vulnerabilities with a high level of certainty. It also has a mechanism for monitoring and recording digital forensic evidence which can be used in legal prosecution proceedings in different jurisdictions.

• International Journal of Computer Science & Network Security
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• 제24권4호
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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.

• 한국통신학회논문지
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• 제40권6호
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• pp.1070-1080
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• 2015

본 논문은 Science DMZ(Demilitarized Zone) 적용을 위한 SDN(Software Defined Networking) 기반의 네트워크 침입 방지 시스템을 소개한다. 제안된 시스템은 침입 탐지 기능을 침입 방지 장치로부터 분리하고 SDN 기술을 확장해 탐지 기능과 방어 기능을 상호 연동시킴으로써 네트워크 보안 장비의 유연성(flexibility)과 확장성(extensibility)을 높이고 패킷 검사(packet inspection) 등으로 야기되는 패킷 손실을 방지하는데 목적이 있다. 본 논문에서는 제안한 프레임워크의 한 응용 시나리오를 소개하고 국가과학기술연구망에 구축된 네트워킹 DMZ환경에 시험 적용함으로써 활용 가능성을 검증한다.

• 디지털산업정보학회논문지
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• 제6권4호
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• pp.153-160
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• 2010

MANET composed mobile nodes without central concentration control like base station communicate through multi-hop route among nodes. Accordingly, it is hard to maintain stability of network because topology of network change at any time owing to movement of mobile nodes. MANET has security problems because of node mobility and needs intrusion detection system that can detect attack of malicious nodes. Therefore, system is protected from malicious attack of intruder in this environment and it has to correspond to attack immediately. In this paper, we propose intrusion detection system based on rules in order to more accurate intrusion detection. Cluster head perform role of monitor node to raise monitor efficiency of packet. In order to evaluate performance of proposed method, we used jamming attack, selective forwarding attack, repetition attack.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권6호
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• pp.2895-2921
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• 2018

Security has become one of the major concerns in mobile adhoc networks (MANETs). Data and voice communication amongst roaming battlefield entities (such as platoon of soldiers, inter-battlefield tanks and military aircrafts) served by MANETs throw several challenges. It requires complex securing strategy to address threats such as unauthorized network access, man in the middle attacks, denial of service etc., to provide highly reliable communication amongst the nodes. Intrusion Detection and Prevention System (IDPS) undoubtedly is a crucial ingredient to address these threats. IDPS in MANET is managed by Command Control Communication and Intelligence (C3I) system. It consists of networked computers in the tactical battle area that facilitates comprehensive situation awareness by the commanders for timely and optimum decision-making. Key issue in such IDPS mechanism is lack of Smart Learning Engine. We propose a novel behavioral based "Smart Multi-Instance Multi-Label Intrusion Detection and Prevention System (MIML-IDPS)" that follows a distributed and centralized architecture to support a Robust C3I System. This protocol is deployed in a virtually clustered non-uniform network topology with dynamic election of several virtual head nodes acting as a client Intrusion Detection agent connected to a centralized server IDPS located at Command and Control Center. Distributed virtual client nodes serve as the intelligent decision processing unit and centralized IDPS server act as a Smart MIML decision making unit. Simulation and experimental analysis shows the proposed protocol exhibits computational intelligence with counter attacks, efficient memory utilization, classification accuracy and decision convergence in securing C3I System in a Tactical Battlefield environment.

• Journal of applied mathematics & informatics
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• 제30권5_6호
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• pp.847-864
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• 2012

In recent years, using computer networks (wired and wireless networks) has been widespread in many applications. As computer networks become increasingly complex, the accompanied potential threats also grow to be more sophisticated and as such security has become one of the major concerns in them. Prevention methods alone are not sufficient to make them secure; therefore, detection should be added as another defense before an attacker can breach the system. Intrusion Detection Systems (IDSs) have become a key component in ensuring systems and networks security. An IDS monitors network activities in order to detect malicious actions performed by intruders and then initiate the appropriate countermeasures. In this paper, we present a survey and taxonomy of intrusion detection systems and then evaluate and compare them.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.2086-2098
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• 2018

Security plays a vital role and is the key challenge in Mobile Ad-hoc Networks (MANET). Infrastructure-less nature of MANET makes it arduous to envisage the genre of topology. Due to its inexhaustible access, information disseminated by roaming nodes to other nodes is susceptible to many hazardous attacks. Intrusion Detection and Prevention System (IDPS) is undoubtedly a defense structure to address threats in MANET. Many IDPS methods have been developed to ascertain the exceptional behavior in these networks. Key issue in such IDPS is lack of fast self-organized learning engine that facilitates comprehensive situation awareness for optimum decision making. Proposed "Intelligent Behavioral Hybridized Intrusion Detection and Prevention System (IBH_IDPS)" is built with computational intelligence to detect complex multistage attacks making the system robust and reliable. The System comprises of an Intelligent Client Agent and a Smart Server empowered with fuzzy inference rule-based service engine to ensure confidentiality and integrity of network. Distributed Intelligent Client Agents incorporated with centralized Smart Server makes it capable of analyzing and categorizing unethical incidents appropriately through unsupervised learning mechanism. Experimental analysis proves the proposed model is highly attack resistant, reliable and secure on devices and shows promising gains with assured delivery ratio, low end-to-end delay compared to existing approach.