Techniques and tools for analyzing intrusion alerts

2004

Several alert correlation methods were proposed in the past several years to construct high-level attack scenarios from low-level intrusion alerts reported by intrusion detection systems (IDSs). These correlation methods have different strengths and limitations; none of them clearly dominate the others. However, all of these methods depend heavily on the underlying IDSs, and perform poorly when the IDSs miss critical attacks. In order to improve the performance of intrusion alert correlation and reduce the impact of missed attacks, this paper presents a series of techniques to integrate two complementary types of alert correlation methods: (1) those based on the similarity between alert attributes, and (2) those based on prerequisites and consequences of attacks. In particular, this paper presents techniques to hypothesize and reason about attacks possibly missed by IDSs based on the indirect causal relationship between intrusion alerts and the constraints they must satisfy. This paper also discusses additional techniques to validate the hypothesized attacks through raw audit data and to consolidate the hypothesized attacks to generate concise attack scenarios. The experimental results in this paper demonstrate the potential of these techniques in building high-level attack scenarios and reasoning about possibly missed attacks.

Http Dx Doi Org 10 1080 02564602 2014 906864, 2014

In recent years, network intrusion attempts have been on the rise. Malicious attempts, including hacking, botnets, and worms are used to intrude and compromise the organization's networks affecting their confidentiality, integrity and availability of resources. In order to detect these malicious activities, intrusion detection systems (IDSs) have been widely deployed in corporate networks. IDS sends alerts to security personnel in case of anomalous activities in the network. Unfortunately, one of the IDSs' drawbacks is they produce a large number of false positives and non-relevant positives alerts that could overwhelm the security personnel. Existing efforts to address this are done via identification of the similarities and causality relationships between alerts, grouping them into different clusters and prioritizing them after conducting the assessment on them. In this paper, we present commonly used alert correlation approaches and highlight the advantages and disadvantages from various perspectives. Existing alert correlation models are critically reviewed and compared in this paper. Subsequently, we emphasize four main considerations in alert correlation design which are: attack scenario either single packet or multi-stage attack, its architecture either centralized or distributed, performance assessment on accuracy of alert detection, and its processing time and the data to be used for testing.

ISC Int. J. Inf. Secur., 2012

Alert correlation systems attempt to discover the relations among alerts produced by one or more intrusion detection systems to determine the attack scenarios and their main motivations. In this paper a new IDS alert correlation method is proposed that can be used to detect attack scenarios in real-time. The proposed method is based on a causal approach due to the strength of causal methods in practice. To provide a picture of the current intrusive activity on the network, we need a real-time alert correlation. Most causal methods can be deployed offline but not in real-time due to time and memory limitations. In the proposed method, the knowledge base of the attack patterns is represented in a graph model called the Causal Relations Graph. In the offline mode, we construct Queue trees related to alerts' probable correlations. In the real-time mode, for each received alert, we can find its correlations with previously received alerts by performing a search only in the correspond...

2007

With the growing deployment of networks and the Internet, the importance of network security has increased. Recently, however, systems that detect intrusions, which are important in security countermeasures, have been unable to provide proper analysis or an effective defense mechanism. Instead, they have overwhelmed human operators with a large volume of intrusion detection alerts. In this paper, we present an alert correlation technique based on causal relationships between alerts. The goal of the proposed technique is not only to group alerts together, but also to represent the correlated alerts in a way that they reflect the corresponding attack scenarios.

2002

Current intrusion detection systems (IDSs) usually focus on detecting low-level attacks and/or anomalies; none of them can capture the logical steps or attack strategies behind these attacks. Consequently, the IDSs usually generate a large amount of alerts. In situations where there are intensive intrusive actions, not only will actual alerts be mixed with false alerts, but the amount of alerts will also become unmanageable. As a result, it is difficult for human users or intrusion response systems to understand the intrusions behind the alerts and take appropriate actions. This paper presents the development of an off-line intrusion alert correlator based on prerequisites of intrusions, which is our first step to address the aforementioned problem. Intuitively, the prerequisite of an intrusion is the necessary condition for the intrusion to be successful. For example, the existence of a vulnerable service is the prerequisite of a remote buffer overflow attack against the service. Based on the prerequisite and the consequence of each type of attacks, our intrusion alert correlator correlates the alerts by matching the consequence of some previous alerts and the prerequisite of some later ones. As a result, our intrusion alert correlator is able to correlate related alerts and uncover the attack strategies behind sequences of attacks. As an application based on relational database management system (RDBMS), the intrusion alert correlator takes advantage of the functionalities of RDBMS and can be easily integrated with other RDBMS-based intrusion analysis tools (e.g., ISS's RealSecure). Our experiments with the DARPA 2000 intrusion detection evaluation datasets have demonstrated the great potential of our approach in reducing false alerts and discovering high-level attack strategies.

International Journal of Computer Applications Technology and Research, 2016

The advancement of modern computers, networks and internet has led to the widespread adoption and application of Information Communication Technology in modern organizations. As a result, large amount of information is generated, processed and distributed through digital devices. On the other side, digital crimes have increased in number and sophistication and they compromise the organization's critical information infrastructure affecting the confidentiality, integrity and availability of its information resources. In order to detect these malicious activities, organizations deploys multiple Network Intrusion Detection Systems (NIDSs) in their corporate networks. They generate huge amount of low quality alerts and in different formats when an attack has already taken place. Thus Alert and event correlation is required to preprocess, analyze and correlate the alerts produced by one or more network intrusion detection systems and events generated from different systems and security tools to provide a more succinct and high-level view of occurring or attempted intrusions. This work will review current alert correlation systems in terms of approaches and propose design consideration for an efficient alert correlation technique. We conclude by highlighting the opportunity to include attack prediction component in a real time multiple sensors environment.

Information Fusion, 2009

Managing and supervising security in large networks has become a challenging task, as new threats and flaws are being discovered on a daily basis. This requires an in depth and up-to-date knowledge of the context in which security-related events occur. Several tools have been proposed to support security operators in this task, each of which focuses on some specific aspects of the monitoring. Many alarm fusion and correlation approaches have also been investigated. However, most of these approaches suffer from two major drawbacks. First, they only take advantage of the information found in alerts, which is not sufficient to achieve the goals of alert correlation, that is to say to reduce the overall amount of alerts, while enhancing their semantics. Second, these techniques have been designed on an ad hoc basis and lack a shared data model that would allow them to reason about events in a cooperative way. In this paper, we propose a federative data model for security systems to query and assert knowledge about security incidents and the context in which they occur. This model constitutes a consistent and formal ground to represent information that is required to reason about complementary evidences, in order to confirm or invalidate alerts raised by intrusion detection systems.

Lecture Notes in Computer Science, 2004

As traditional host-and network-based IDSs are to detect a single intrusion based on log data or packet information respectively, they inherently generate a huge number of false alerts due to lack of information on interrelated alarms. In order to reduce the number of false alarms and then detect a real intrusion, a new alert analyzing system is needed. In this paper, we propose a rule-based alert correlation system to reduce the number of false alerts, correlate them, and decide which alerts are parts of the real attack. Our alert correlation system consists of an alert manager, an alert preprocessor, an alert correlator. An alert manager takes charge of storing filtered alerts into our alert database. An alert preprocessor reduces stored alerts to facilitate further correlation analysis. An alert correlator reports global attack plans.

Attacks to information systems are becoming more sophisticated and traditional algorithms supporting Network Intrusion Detection Systems may be ineffective or cause too many false alarms. This paper describes a new algorithm for the correlation of alerts generated by Network Intrusion Detection Systems. It is specifically oriented to face multistep attacks where multiple intrusion activities belonging to the same attack scenario are performed within a small time window. This algorithm takes as its input the security alerts generated by a NIDS and, through a pseudo-bayesian alert correlation, is able to identify those that are likely to belong to the same multistep attack scenario. The proposed approach is completely unsupervised and applicable to security alerts generated by any kind of NIDS.

Computer Networks, 2011

Managing and analyzing a huge number of low-level alerts is very difficult and exhausting for network administrators. Alert correlation methods have been proposed to decrease the number of alerts and make them more intelligible. Proposed methods for alert correlation are different in terms of their performance, accuracy and adaptivity. We present a new hybrid model not only to correlate alerts