透過您的圖書館登入
IP:18.191.240.243

並列摘要


Alert correlation is an important technique for managing large the volume of intrusion alerts that are raised by heterogenous Intrusion Detection Systems (IDSs). The recent trend of research in this area is towards extracting attack strategies from raw intrusion alerts. It is generally believed that pure intrusion detection no longer can satisfy the security needs of organizations. Intrusion response and prevention are now becoming crucially important for protecting the network and minimizing damage. Knowing the real security situation of a network and the strategies used by the attackers enables network administrators to launches appropriate response to stop attacks and prevent them from escalating. This is also the primary goal of using alert correlation technique. However, most of the current alert correlation techniques only focus on clustering inter-connected alerts into different groups without further analyzing the strategies of the attackers. Some techniques for extracting attack strategies have been proposed in recent years, but they normally require defining a larger number of rules.This paper focuses on developing a new alert correlation technique that can help to automatically extract attack strategies from a large volume of intrusion alerts, without specific prior knowledge about these alerts. The proposed approach is based on two different neural network approaches, namely, Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The probabilistic output of these two methods is used to determine with which previous alerts this current alert should be correlated. This suggests the causal relationship of two alerts, which is helpful for constructing attack scenarios. One of the distinguishing feature of the proposed technique is that an Alert Correlation Matrix (ACM) is used to store correlation strengthes of any two types of alerts. ACM is updated in the training process, and the information (correlation strength) is then used for extracting high level attack strategies.

被引用紀錄


楊汶姍(2017)。製備穩定之油相奈米銀顆粒懸浮液〔碩士論文,淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2017.00866
Guotan, L. (2012). 多階段攻擊之入侵預測與識別 [doctoral dissertation, National Chung Cheng University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0033-2110201613505787

延伸閱讀