Intrusion Detection System Sensors Research Articles

Design and Implementation of Intrusion Detection System (IDS) for Wireless Local Area Network (WLAN) Security at SMKN 5 Bengkulu City

The network at SMK Negeri 5 Bengkulu City at this time often has complaints such as a decrease in internet network performance which in turn affects all computers connected to the network. Intrusion Detection System (IDS) whose job is to monitor networks and suspicious activities in a network system. The research method that the authors use is a linear sequential process model. The life cycle of implementing a network system is defined in a number of phases, namely: Problem Identification and Formulation, Literature Study, Testing, Implementation, System Design, System Requirements Analysis. The author uses Snort, Telegram Bot on Ubuntu based IDS sensor engine. The results of this thesis research conclude that the applied IDS system can function to detect intruders or intruders on the IDS sensor machine, which is displayed in the form of notifications to telegrams.

Assessment Methodology for Anomaly-Based Intrusion Detection in Cloud Computing

Cloud computing has become an attractive target for attackers as the mainstream technologies in the cloud, such as the virtualization and multitenancy, permit multiple users to utilize the same physical resource, thereby posing the so-called problem of internal facing security. Moreover, the traditional network-based intrusion detection systems (IDSs) are ineffective to be deployed in the cloud environments. This is because that such IDSs employ only the network information in their detection engine and this, therefore, makes them ineffective for the cloud-specific vulnerabilities. In this paper, we propose a novel assessment methodology for anomaly-based IDSs for cloud computing which takes into account both network and system-level information for generating the evaluation dataset. In addition, our approach deploys the IDS sensors in each virtual machine in order to develop a cooperative anomaly detection engine. The proposed assessment methodology is then deployed in a testbed cloud environment to generate an IDS dataset which includes both network and system-level features. Finally, we evaluate the performance of several machine learning algorithms over the generated dataset. Our experimental results demonstrate that the proposed IDS assessment approach is effective for attack detection in the cloud as most of the algorithms are able to identify the attacks with a high level of accuracy.

Perancangan dan Implementasi Instrusion Detection System di Jaringan Universitas Diponegoro

The use of information technology gives the advantage of open access for its users, but a new problem arises that there is a threat from unauthorized users. Intrusion Detection System (IDS) is applied to assist administrator to monitoring network security. IDS displays illegal access information in a raw form which is require more time to read the detected threats. This final project aims to design an IDS with web application which is made for pulling information on IDS sensor database, then processing and representing them in tables and graphs that are easy to understand. The web application also has IpTables firewall module to block attacker's IP address. The hardware used is Cisco IPS 4240, two computers Compaq Presario 4010F as client and gateway, and Cisco Catalyst 2960 switch. The software used is Ubuntu 12.0 LTS Precise operating system, BackTrack 5 R1 operating system, PHP 5.4 programming language, MySQL 5 database, and web-based system configuration tool Webmin. Testing is done using several BackTrack applications with the aim of Cisco IPS 4240 is capable of detecting accordance with the applicable rules. Each events of any attack attempt or threat was obtained from IDS sensor database in XML form. XML file is sent using Security Device Event Exchange (SDEE) protocol. The web application is tested by looking at the output tables and graphs that displays the appropriate results of sensor detection. This study generated an intrusion detection system that is easier to monitor. Network packets copied by the Cisco 2960 switch and then forwarded to the sensor. Intruder detection is done by Cisco IPS 4240 sensor. Log detection processed by the web application into tables and graphs. Intrusion detection systems are intended to improve network security.

Enhancing IDS performance through comprehensive alert post-processing

Intrusion detection systems (IDS) are among the most common countermeasures against network attacks. In order to improve the alerts obtained from them, various methods of post-processing have been proposed. These methods usually try to alleviate specific drawbacks of intrusion detection. We propose a system that is a post-processing solution. The input of our system is a set of multiple IDS sensors alert sets. Each set's alerts are aggregated in order to improve their quality, before multiple alert sets merge into one general alert set. Then, a low clustering procedure allows the system to hypothesize about missed security events and to create relevant alerts. The main clustering phase comes next, before the final step, in which a clusters graph is generated to produce a high level presentation of the security events. The system has been tested using the DARPA 2000 dataset, as well as a live network dataset, and has produced satisfactory results.

A Composite Privacy Leakage Indicator

This paper proposes a Subjective Logic based composite privacy leakage metric that both takes into account the amount of information leakage and also that information with high entropy in some cases may be considered encrypted. It is furthermore shown both analytically and experimentally that Min-entropy is considered better than Shannon, Renyi or Max entropy for identifying encrypted content for the composite metric. This is in particular useful for implementing privacy-enhanced Intrusion Detection Systems (IDS), where sampled encrypted traffic can be considered to have low risk of revealing sensitive information. The combined metric can be used in a Policy Enforcement Point that acts as a proxy/anonymiser in order to to reduce the leakage of private or sensitive information from the IDS sensors to an outsourced Managed Security Service provider. Although the composite privacy indicator is IDS specific, the authorisation architecture is general, and may also be useful for anonymising or pseusonymising sensitive information from or to other types of sensors that need to be exposed to the Internet. The solution is based on the eXtensible Access Control Markup Language policy language extended with support for Subjective Logic, in order to provide a method for expressing fine-grained access control policies that are based on uncertain evidences.

Research on Distributed Intrusion Detection Model Based on Information Fusion

The research actuality of Intrusion Detection System(IDS) were analyzed, Due to the defects of IDS such as high positive rate of IDS and incapable of effective detection of dispersed coordinated attacks on the time and space, the ideas of the multi-source information fusion were introduced in the paper, a multi-level IDS reasoning framework and prototype system were presented. The prototype adds analysis engine to the existing IDS Sensor, We used Bayesian Network as a tool for multi-source information fusion, and we used goal-tree to analyze the attempts of coordinated attacks and quantify the security risk of system. Compared to the existing IDS, the prototype is more integrated and more capable in finding coordinated attacks with lower false positive rate.

Optimal IDS Sensor Placement and Alert Prioritization Using Attack Graphs

We optimally place intrusion detection system (IDS) sensors and prioritize IDS alerts using attack graph analysis. We begin by predicting all possible ways of penetrating a network to reach critical assets. The set of all such paths through the network constitutes an attack graph, which we aggregate according to underlying network regularities, reducing the complexity of analysis. We then place IDS sensors to cover the attack graph, using the fewest number of sensors. This minimizes the cost of sensors, including effort of deploying, configuring, and maintaining them, while maintaining complete coverage of potential attack paths. The sensor-placement problem we pose is an instance of the NP-hard minimum set cover problem. We solve this problem through an efficient greedy algorithm, which works well in practice. Once sensors are deployed and alerts are raised, our predictive attack graph allows us to prioritize alerts based on attack graph distance to critical assets.

IDS alerts correlation using grammar-based approach

Intrusion Detection System (IDS) is a security technology that attempts to identify intrusions. Defending against multi-step intrusions which prepare for each other is a challenging task. In this paper, we propose a novel approach to alert post-processing and correlation, the Alerts Parser. Different from most other alert correlation methods, our approach treats the alerts as tokens and uses modified version of the LR parser to generate parse trees representing the scenarii in the alerts. An Attribute Context-Free Grammar (ACF-grammar) is used for representing the multi-step attacks. Attack scenarii information and prerequisites/consequences knowledge are included together in the ACF-grammar enhancing the correlation results. The modified LR parser depends on these ACF-grammars to generate parse trees. The experiments were performed on two different sets of network traffic traces, using different open-source and commercial IDS sensors. The discovered scenarii are represented by Correlation Graphs (CGs). The experimental results show that Alerts Parser can work in parallel, effectively correlate related alerts with low false correlation rate, uncover the attack strategies, and generate concise CGs.

Modelling intrusion for intrusion tolerance system by correlating alerts from IDS sensor

Modelling the intrusion is an open problem in Intrusion Tolerance System. But we find that alerts from the sensors of Intrusion Detection System (IDS) are helpful to solve this problem. An intrusion model based on correlating these alerts is presented in this paper, in which we place our emphasis on correlating these alerts to the intruders' inherence. Firstly, we correlate the alerts from IDS sensor into meta-attack in the constructing algorithm and then define cover as the reduction of meta-attack. Secondly, we transform the cover to intrusion model and give the proofs of the equivalences among intrusion model, meta-attack and its cover. Thirdly, we present an algorithm for describing the intrusion model without employing manual work. Finally, we do some correlation experiments to evaluate and show the performances of both the intrusion model and the algorithms for constructing and describing this model.

Improving the quality of alerts and predicting intruder’s next goal with Hidden Colored Petri-Net

Intrusion detection systems (IDS) often provide poor quality alerts, which are insufficient to support rapid identification of ongoing attacks or predict an intruder’s next likely goal. In this paper, we propose a novel approach to alert postprocessing and correlation, the Hidden Colored Petri-Net (HCPN). Different from most other alert correlation methods, our approach treats the alert correlation problem as an inference problem rather than a filter problem. Our approach assumes that the intruder’s actions are unknown to the IDS and can be inferred only from the alerts generated by the IDS sensors. HCPN can describe the relationship between different steps carried out by intruders, model observations (alerts) and transitions (actions) separately, and associate each token element (system state) with a probability (or confidence). The model is an extension to Colored Petri-Net (CPN). It is so called “hidden” because the transitions (actions) are not directly observable but can be inferred by looking through the observations (alerts). These features make HCPN especially suitable for discovering intruders’ actions from their partial observations (alerts) and predicting intruders’ next goal. Our experiments on DARPA evaluation datasets and the attack scenarios from the Grand Challenge Problem (GCP) show that HCPN has promise as a way to reducing false positives and negatives, predicting intruder’s next possible action, uncovering intruders’ intrusion strategies after the attack scenario has happened, and providing confidence scores.