Intrusion Alert Correlation Technique Analysis for Heterogeneous Log

Intrusion alert correlation is multi-step processes that receives alerts from heterogeneous log resources as input and produce a high-level description of the malicious activity on the network. The objective of this study is to analyse the current alert correlation technique and identify the significant criteria in each technique that can improve the Intrusion Detection System(IDS) problem such as prone to alert flooding, contextual problem, false alert and scalability. The existing alert correlation techniques had been reviewed and analysed. From the analysis, six capability criteria have been identified to improve the current alert correlation technique. They are capability to do alert reduction, alert clustering,identify multistep attack, reduce false alert, detect known attack and detect unknown attack

Data Reduction in Intrusion Alert Correlation

Network intrusion detection sensors are usually built around low level models of network traffic. This means that their output is of a similarly low level and as a consequence, is difficult to analyze. Intrusion alert correlation is the task of automating some of this analysis by grouping related alerts together. Attack graphs provide an intuitive model for such analysis. Unfortunately alert flooding attacks can still cause a loss of service on sensors, and when performing attack graph correlation, there can be a large number of extraneous alerts included in the output graph. This obscures the fine structure of genuine attacks and makes them more difficult for human operators to discern. This paper explores modified correlation algorithms which attempt to minimize the impact of this attack

Data Reduction in Intrusion Alert Correlation

Network intrusion detection sensors are usually built around low level models of network traffic. This means that their output is of a similarly low level and as a consequence, is difficult to analyze. Intrusion alert correlation is the task of automating some of this analysis by grouping related alerts together. Attack graphs provide an intuitive model for such analysis. Unfortunately alert flooding attacks can still cause a loss of service on sensors, and when performing attack graph correlation, there can be a large number of extraneous alerts included in the output graph. This obscures the fine structure of genuine attacks and makes them more difficult for human operators to discern. This paper explores modified correlation algorithms which attempt to minimize the impact of this attack

Alert Correlation through a Multi Components Architecture

Alert correlation is a process that analyzes the raw alerts produced by one or more intrusion detection systems, reduces nonrelevant ones, groups together alerts based on similarity and causality relationships between them and finally makes aconcise and meaningful view of occurring or attempted intrusions. Unfortunately, most correlation approaches use just a few components that aim only specific correlation issues and so cause reduction in correlation rate. This paper uses a general correlation model that has already been presented in [9] and is consisted of a comprehensive set of components. Then some changes are applied in the component that is related to multi-step attack scenario to detect them better and so to improve semantic level of alerts. The results of experiments with DARPA 2000 data set obviously show the effectiveness of the proposed approach.DOI:http://dx.doi.org/10.11591/ijece.v3i4.277

Feature selection using information gain for improved structural-based alert correlation

Grouping and clustering alerts for intrusion detection based on the similarity of features is referred to as structurally base alert correlation and can discover a list of attack steps. Previous researchers selected different features and data sources manually based on their knowledge and experience, which lead to the less accurate identification of attack steps and inconsistent performance of clustering accuracy. Furthermore, the existing alert correlation systems deal with a huge amount of data that contains null values, incomplete information, and irrelevant features causing the analysis of the alerts to be tedious, time-consuming and error-prone. Therefore, this paper focuses on selecting accurate and significant features of alerts that are appropriate to represent the attack steps, thus, enhancing the structural-based alert correlation model. A two-tier feature selection method is proposed to obtain the significant features. The first tier aims at ranking the subset of features based on high information gain entropy in decreasing order. The second tier extends additional features with a better discriminative ability than the initially ranked features. Performance analysis results show the significance of the selected features in terms of the clustering accuracy using 2000 DARPA intrusion detection scenario-specific dataset

Intrusion detection system alert correlation with operating system level logs

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2009Includes bibliographical references (leaves: 63-66)Text in English; Abstract: Turkish and Englishvii, 67 leavesInternet is a global public network. More and more people are getting connected to the Internet every day to take advantage of the Internetwork connectivity. It also brings in a lot of risk on the Internet because there are both harmless and harmful users on the Internet. While an organization makes its information system available to harmless Internet users, at the same time the information is available to the malicious users as well. Most organizations deploy firewalls to protect their private network from the public network. But, no network can be hundred percent secured. This is because; the connectivity requires some kind of access to be granted on the internal systems to Internet users. The firewall provides security by allowing only specific services through it. The firewall implements defined rules to each packet reaching to its network interface. The IDS complements the firewall security by detected if someone tries to break in through the firewall or manages to break in the firewall security and tried to have access on any system in the trusted site and alerted the system administrator in case there is a breach in security. However, at present, IDSs suffer from several limitations. To address these limitations and learn network security threats, it is necessary to perform alert correlation. Alert correlation focuses on discovering various relationships between individual alerts. Intrusion alert correlation techniques correlate alerts into meaningful groups or attack scenarios for ease to understand by human analysts. In order to be sure about the alert correlation working properly, this thesis proposed to use attack scenarios by correlating alerts on the basis of prerequisites and consequences of intrusions. The architecture of the experimental environment based on the prerequisites and consequences of different types of attacks, the proposed approach correlates alerts by matching the consequence of some previous alerts and the prerequisite of some later ones with OS-level logs. As a result, the accuracy of the proposed method and its advantage demonstrated to focus on building IDS alert correlation with OS-level logs in information security systems

A New Metric for Prioritising Intrusion Alerts Using Correlation and Outlier Analysis

In a medium sized network, an Intrusion Detection System (IDS) could produce thousands of alerts a day many of which may be false positives. In the vast number of triggered intrusion alerts, identifying those to prioritise is highly challenging. Alert Correlation and prioritisation are both viable analytical methods which are commonly used to understand and prioritise alerts. However, to the author’s knowledge, very few dynamic prioritisation metrics exist. In this paper, a new prioritisation metric - OutMet, which is based on measuring the degree to which an alert belongs to anomalous behaviour is proposed. OutMet combines alert correlation and prioritisation analysis and in given attack scenarios, is capable of reducing false positives by upto 100%. The metric is tested and evaluated using the recently developed cyber-range dataset provided by Northrop Grumman