• 정보보호학회논문지
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• 제14권5호
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• pp.69-78
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• 2004

인터넷의 급속한 확장과 새로운 공격 형태의 출현으로 인해 공격 기법 패러다임의 변화가 시작되었다. 그러나, 대부분의 침입 탐지 시스템은 오용 탐지 기반의 알려진 공격 유형만을 탐지하며, 새로운 공격에 대해서는 능동적인 대응이 어려운 실정이다. 이에 새로운 공격 유형에 대한 탐지 능력을 높이기 위해 이상 탐지의 여러 기법들을 적용하려는 시도들이 나타나고 있다. 본 논문에서는 그래픽 기반의 베이지안 프레임워크를 이용하여 감사 데이터에 의한 행위 프로파일링 방법을 제안하고 이상 탐지와 분석을 위한 행위 프로파일을 시각화하고자 한다. 호스트/네트워크의 감사 데이터를 이상 탐지를 위한 준 구조적 데이터 형식의 행위 프로파일인 BF-XML로 변환하고, BF-XML을 SVG로 시각화를 시뮬레이션한다.

• 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.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2004년도 춘계종합학술대회
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• pp.391-394
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• 2004

인터넷의 급속한 확장으로 인해 네트워크 공격기법의 패러다임의 변화가 시작되었으며 새로울 공격 형태가 나타나고 있으나 대부분의 침입 탐지 기술은 오용 탐지 기술을 기반으로 하는 시스템이주를 이루고 있어 알려진 공격 유형만을 탐지하고, 새로운 공격에 능동적인 대응이 어려운 실정이다. 이에 새로운 공격 유형에 대한 탐지력을 높이기 위해 인체 면역 메커니즘을 적용하려는 시도들이 나타나고 있다. 본 논문에서는 데이터 마이닝 기법을 이용하여 네트워크 패킷에 대한 정상 행위 프로파일을 생성하고 생성된 프로파일을 자기공간화 하여 인체면역계의 자기, 비자기 구분기능을 이용해 자기 인식 알고리즘을 구현하여 이상행위를 탐지하고자 한다. 자기인식 알고리즘의 하나인 Negative Selection Algorithm을 기반으로 anomaly detector를 생성하여 자기공간을 모니터하여 변화를 감지하고 이상행위를 검출한다. DARPA Network Dataset을 이용하여 시뮬레이션을 수행하여 침입 탐지율을 통해 알고리즘의 유효성을 검증한다.

• 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.

• 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.

• 한국컴퓨터정보학회논문지
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• 제21권7호
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• pp.1-8
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• 2016

Nowadays, distributed denial of service (DDoS) attacks on web sites reward attackers financially or politically because our daily lifes tightly depends on web services such as on-line banking, e-mail, and e-commerce. One of DDoS attacks to web servers is called HTTP-GET flood attack which is becoming more serious. Most existing techniques are running on the application layer because these attack packets use legitimate network protocols and HTTP payloads; that is, network-level intrusion detection systems cannot distinguish legitimate HTTP-GET requests and malicious requests. In this paper, we propose a practical detection technique against HTTP-GET flood attacks, based on the access behavior of inline objects in a webpage using NetFlow data. In particular, our proposed scheme is working on the network layer without any application-specific deep packet inspections. We implement the proposed detection technique and evaluate the ability of attack detection on a simple test environment using NetBot attacker. Moreover, we also show that our approach must be applicable to real field by showing the test profile captured on a well-known e-commerce site. The results show that our technique can detect the HTTP-GET flood attack effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5354-5369
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• 2019

Sensor networks are deployed in unheeded environment to monitor the situation. In view of the unheeded environment and by the nature of their communication channel sensor nodes are vulnerable to various attacks most commonly malicious packet dropping attacks namely blackhole, grayhole attack and sinkhole attack. In each of these attacks, the attackers capture the sensor nodes to inject fake details, to deceive other sensor nodes and to interrupt the network traffic by packet dropping. In all such attacks, the compromised node advertises itself with fake routing facts to draw its neighbor traffic and to plunge the data packets. False routing advertisement play vital role in deceiving genuine node in network. In this paper, behavior based routing misbehavior detection (BRMD) is designed in wireless sensor networks to detect false advertiser node in the network. Herein the sensor nodes are monitored by its neighbor. The node which attracts more neighbor traffic by fake routing advertisement and involves the malicious activities such as packet dropping, selective packet dropping and tampering data are detected by its various behaviors and isolated from the network. To estimate the effectiveness of the proposed technique, Network Simulator 2.34 is used. In addition packet delivery ratio, throughput and end-to-end delay of BRMD are compared with other existing routing protocols and as a consequence it is shown that BRMD performs better. The outcome also demonstrates that BRMD yields lesser false positive (less than 6%) and false negative (less than 4%) encountered in various attack detection.

• 디지털산업정보학회논문지
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• 제8권1호
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• pp.125-137
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• 2012

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. For such reason, lots of intrusion detection system has been developed. Intrusion detection system has abilities to detect abnormal behavior and unknown intrusions also it can detect intrusions by using patterns studied from various penetration methods. Various algorithms are studying now such as the statistical method for detecting abnormal behavior, extracting abnormal behavior, and developing patterns that can be expected. Etc. This study using clustering of data mining and association rule analyzes detecting areas based on two models and helps design detection system which detecting abnormal behavior, unknown attack, misuse attack in a large network.

• 정보보호학회논문지
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• 제31권3호
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• pp.365-371
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• 2021

인터넷 사용자의 급증으로 웹 어플리케이션은 해커의 주요 공격대상이 되고 있다. 웹 공격을 막기 위한 기존의 WAF(Web Application Firewall)는 공격자의 전반적인 행위보다는 HTTP 요청 패킷 하나하나를 탐지 대상으로 하고 있으며, 새로운 유형의 공격에 대해서는 탐지하기 어려운 것으로 알려져 있다. 본 연구에서는 알려지지 않은 패턴의 공격을 탐지하기 위해 기계학습을 활용한 사용자 행위 기반의 웹 공격 탐지 기법을 제안한다. 공격자가 정상적인 사용자인 것처럼 위장할 수 있는 부분을 제외한 영역에 집중하여 사용자 행위 정보를 정의였으며, 벤치마크 데이터셋인 CSIC 2010을 활용하여 웹 공격 탐지 실험을 수행하였다. 실험결과 Decision Forest 알고리즘에서 약 99%의 정확도를 얻었고, 동일한 데이터셋을 활용한 기존 연구와 비교하여 본 논문의 효율성을 증명하였다.

• 한국정보처리학회논문지
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• 제6권11호
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• pp.3011-3019
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• 1999

Denial of service is about knocking off services, without permission for example through crashing the whole system. This kind of attacks are easy to launch and it is hard to protect a system against them. The basic problem is that Unix assumes that users on the system or on other systems will be well behaved. This paper analyses system-based inside denial of services attack(DoS) and system metric for performance of each machine provided. And formalize the conclusions results in ways that clearly expose the performance impact of those observations. So, we present new approach. It is detecting DoS attack using performance signature for system and program behavior. We present new approach. It is detecting DoS attack using performance signature for system and program behavior. We believe that metric will be to guide to automated development of a program to detect the attack. As a results, we propose the AIDPS(Architecture for Intrusion Detection using Performance Signature) model to detect DoS attack using performance signature.