• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.52-60
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• 2024

APT (Advanced Persistent Threat) attack is a dangerous, targeted attack form with clear targets. APT attack campaigns have huge consequences. Therefore, the problem of researching and developing the APT attack detection solution is very urgent and necessary nowadays. On the other hand, no matter how advanced the APT attack, it has clear processes and lifecycles. Taking advantage of this point, security experts recommend that could develop APT attack detection solutions for each of their life cycles and processes. In APT attacks, hackers often use phishing techniques to perform attacks and steal data. If this attack and phishing phase is detected, the entire APT attack campaign will be crash. Therefore, it is necessary to research and deploy technology and solutions that could detect early the APT attack when it is in the stages of attacking and stealing data. This paper proposes an APT attack detection framework based on the Network traffic analysis technique using open-source tools and deep learning models. This research focuses on analyzing Network traffic into different components, then finds ways to extract abnormal behaviors on those components, and finally uses deep learning algorithms to classify Network traffic based on the extracted abnormal behaviors. The abnormal behavior analysis process is presented in detail in section III.A of the paper. The APT attack detection method based on Network traffic is presented in section III.B of this paper. Finally, the experimental process of the proposal is performed in section IV of the paper.

• 디지털산업정보학회논문지
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• 제16권1호
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• pp.67-78
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• 2020

Typical security solutions such as intrusion detection system are not suitable for detecting advanced persistent attack(APT), because they cannot draw the big picture from trivial events of security solutions. Researches on techniques for detecting multiple stage attacks by analyzing the correlations between security events or alerts are being actively conducted in academic field. However, these studies still use events from existing security system, and there is insufficient research on the structure of the entire security system suitable for advanced persistent attacks. In this paper, we propose an attack path and intention recognition system suitable for multiple stage attacks like advanced persistent attack detection. The proposed system defines the trace format and overall structure of the system that detects APT attacks based on the correlation and behavior analysis, and is designed with a structure of detection system using deep learning and big data technology, etc.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 학술대회지
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• pp.540-543
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• 2004

Address Resolution Protocol (ARP) cache poisoning is a MAC layer attack that can only be carried out when an attacker is connected to the same local network as the target machines. ARP is not a new problem, but wireless network introduces a new attack point and more vulnerable to the attack. The attack on wireless network cannot be detected by current detection tool installed on wired network. In order to detect the ARP poisoning attack, there must be a ARP poisoning detection tool for wireless LAN environment. This paper proposes linux-based ARP poisoning detection system equipped with wireless LAN card and Host AP device driver

• 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.

• 정보보호학회논문지
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• 제21권2호
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• pp.101-110
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• 2011

최근 Application에 대한 공격을 통하여 네트워크와 데이터베이스에 대한 방어정책인 침입탐지 룰(rule)을 무력화시키고, 침해사고를 유발한다. 이러한 공격으로부터 내부 네트워크와 데이터베이스의 안전성을 확보하기 위한 통합보안에 관한 연구가 필요하다. 본 논문에서는 침입탐지 룰을 설정한 Application에 대한 공격을 차단하기 위한 통합보안 시스템 구축에 관한 연구이다. 네트워크 기반의 공격을 탐지하여 대응하고, 내부 Integration Security System을 Virtual clustering과 Load balancing 기법으로 공격을 분산시키며, Packet 모니터링과 분석을 통하여 공격 목적지 Packet에 대한 방어정책 설정, 공격 Packet 분석, 기록, 룰 업데이트를 한다. 또한 공격 유형별 방어정책을 설정하여 Virtual Machine 분할 정책을 통한 접근 트래픽 해소, 공격차단에 적용하는 Integration Security System을 제안하고 방어를 실험한다. 본 연구 결과는 외부 해커의 공격에 대한 통합보안 방어를 위한 현실적인 자료를 제공하게 될 것이다.

• 융합보안논문지
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• 제11권6호
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• pp.25-30
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• 2011

MANET은 이동 노드로만 구성되어 있고 중앙 관리 시스템이 존재하지 않기 때문에 보안에 더욱 취약한 구조를 가지고 있다. 이러한 무선 네트워크를 위협하는 공격들 중에 그 피해가 가장 심각한 공격이 바로 DDoS 공격이다. 최근 들어 DDoS 공격은 목표 대상과 수법이 다양해지고 지능화 되어가고 있다. 본 논문에서는 비정상 트래픽을 정확히 분류하여 DDoS 탐지율을 높이기 위한 기법을 제안하였다. MANET을 구성하는 노드들을 클러스터로 형성한 후 클러스터 헤드가 감시 에이젼트 기능을 수행하게 하였다. 그리고 감시 에이젼트가 모든 트래픽을 수집한 후 비정상 트래픽 패턴을 탐지하기 위하여 결정트리 기법을 적용하였으며 트래픽 패턴을 판단하여 공격을 탐지하였다. 실험을 통해 본 논문에서 제안한 탐지 기법의 높은 공격 탐지율을 확인하였다.

• 디지털산업정보학회논문지
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• 제13권1호
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• pp.49-57
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• 2017

IoT can be connected through a single network not only objects which can be connected to existing internet but also objects which has communication capability. This IoT environment will be a huge change to the existing communication paradigm. However, the big security problem must be solved in order to develop further IoT. Security mechanisms reflecting these characteristics should be applied because devices participating in the IoT have low processing ability and low power. In addition, devices which perform abnormal behaviors between objects should be also detected. Therefore, in this paper, we proposed D-IDS technique for efficient detection of malicious attack nodes between devices participating in the IoT. The proposed technique performs the central detection and distribution detection to improve the performance of attack detection. The central detection monitors the entire network traffic at the boundary router using SVM technique and detects abnormal behavior. And the distribution detection combines RSSI value and reliability of node and detects Sybil attack node. The performance of attack detection against malicious nodes is improved through the attack detection process. The superiority of the proposed technique can be verified by experiments.

• 정보보호학회논문지
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• 제29권5호
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• pp.1027-1037
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• 2019

기존의 사이버 공격 탐지 솔루션은 일반적으로 시그니처 기반 내지 악성행위 분석을 통한 방식의 탐지를 수행하므로, 알려지지 않은 방식에 의한 공격은 탐지하기 어렵다는 한계가 있다. 시스템에서는 상시로 발생하는 다양한 정보들이 시스템의 상태를 반영하고 있으므로, 이들 정보를 수집하여 정상상태를 학습하고 이상상태를 탐지하는 방식으로 알려지지 않은 공격을 탐지할 수 있다. 본 논문은 정상상태 학습 및 탐지에 활용하기 위하여 문자열을 그 순서와 의미를 보존하며 정량적 수치로 변환하는 머신러닝 임베딩(Embedding) 기법과 이상상태의 탐지를 위하여 다수의 정상데이터에서 소수의 비정상 데이터를 탐지하는 머신러닝 이상치 탐지(Novelty Detection) 기법을 이용하여 사이버 공격에 의한 시스템 이상상태를 탐지하는 방안을 제안한다.

• 정보보호학회논문지
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• 제34권4호
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• pp.587-596
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• 2024

사이버 공격, 위협이 복잡해지고 빠르게 진화하면서, 4차 산업 혁명의 핵심 기술인 인공지능(AI)을 이용하여 사이버 위협 탐지 시스템 구축이 계속해서 주목받고 있다. 특히, 기업 및 정부 조직의 보안 운영 센터(Security Operations Center)에서는 보안 오케스트레이션, 자동화, 대응을 뜻하는 SOAR(Security Orchestration, Automation and Response) 솔루션 구현을 위해 AI를 활용하는 사례가 증가하고 있으며, 이는 향후 예견되는 근거를 바탕으로 한 지식인 사이버 위협 인텔리전스(Cyber Threat Intelligence, CTI) 구축 및 공유를 목적으로 한다. 본 논문에서는 네트워크 트래픽, 웹 방화벽(WAF) 로그 데이터를 대상으로 한 사이버 위협 탐지 기술 동향을 소개하고, TF-IDF(Term Frequency-Inverse Document Frequency) 기술과 자동화된 머신러닝(AutoML)을 이용하여 웹 트래픽 로그 공격 유형을 분류하는 방법을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4909-4926
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• 2020

Cyberattacks penetrate the server and perform various malicious acts such as stealing confidential information, destroying systems, and exposing personal information. To achieve this, attackers perform various malicious actions by infecting endpoints and accessing the internal network. However, the current countermeasures are only anti-viruses that operate in a signature or pattern manner, allowing initial unknown attacks. Endpoint Detection and Response (EDR) technology is focused on providing visibility, and strong countermeasures are lacking. If you fail to respond to the initial attack, it is difficult to respond additionally because malicious behavior like Advanced Persistent Threat (APT) attack does not occur immediately, but occurs over a long period of time. In this paper, we propose a technique that detects an unknown attack using an event log without prior knowledge, although the initial response failed with anti-virus. The proposed technology uses a combination of AutoEncoder and 1D CNN (1-Dimention Convolutional Neural Network) based on semi-supervised learning. The experiment trained a dataset collected over a month in a real-world commercial endpoint environment, and tested the data collected over the next month. As a result of the experiment, 37 unknown attacks were detected in the event log collected for one month in the actual commercial endpoint environment, and 26 of them were verified as malicious through VirusTotal (VT). In the future, it is expected that the proposed model will be applied to EDR technology to form a secure endpoint environment and reduce time and labor costs to effectively detect unknown attacks.