CHID : conditional hybrid intrusion detection system for reducing false positives and resource consumption on malicous datasets

Inspecting packets to detect intrusions faces challenges when coping with a high volume of network traffic. Packet-based detection processes every payload on the wire, which degrades the performance of network intrusion detection system (NIDS). This issue requires an introduction of a flow-based NIDS that reduces the amount of data to be processed by examining aggregated information of related packets. However, flow-based detection still suffers from the generation of the false positive alerts due to incomplete data input. This study proposed a Conditional Hybrid Intrusion Detection (CHID) by combining the flow-based with packet-based detection. In addition, it is also aimed to improve the resource consumption of the packet-based detection approach. CHID applied attribute wrapper features evaluation algorithms that marked malicious flows for further analysis by the packet-based detection. Input Framework approach was employed for triggering packet flows between the packetbased and flow-based detections. A controlled testbed experiment was conducted to evaluate the performance of detection mechanism’s CHID using datasets obtained from on different traffic rates. The result of the evaluation showed that CHID gains a significant performance improvement in terms of resource consumption and packet drop rate, compared to the default packet-based detection implementation. At a 200 Mbps, CHID in IRC-bot scenario, can reduce 50.6% of memory usage and decreases 18.1% of the CPU utilization without packets drop. CHID approach can mitigate the false positive rate of flow-based detection and reduce the resource consumption of packet-based detection while preserving detection accuracy. CHID approach can be considered as generic system to be applied for monitoring of intrusion detection systems

Anomaly-based Correlation of IDS Alarms

An Intrusion Detection System (IDS) is one of the major techniques for securing information systems and keeping pace with current and potential threats and vulnerabilities in computing systems. It is an indisputable fact that the art of detecting intrusions is still far from perfect, and IDSs tend to generate a large number of false IDS alarms. Hence human has to inevitably validate those alarms before any action can be taken. As IT infrastructure become larger and more complicated, the number of alarms that need to be reviewed can escalate rapidly, making this task very difficult to manage. The need for an automated correlation and reduction system is therefore very much evident. In addition, alarm correlation is valuable in providing the operators with a more condensed view of potential security issues within the network infrastructure. The thesis embraces a comprehensive evaluation of the problem of false alarms and a proposal for an automated alarm correlation system. A critical analysis of existing alarm correlation systems is presented along with a description of the need for an enhanced correlation system. The study concludes that whilst a large number of works had been carried out in improving correlation techniques, none of them were perfect. They either required an extensive level of domain knowledge from the human experts to effectively run the system or were unable to provide high level information of the false alerts for future tuning. The overall objective of the research has therefore been to establish an alarm correlation framework and system which enables the administrator to effectively group alerts from the same attack instance and subsequently reduce the volume of false alarms without the need of domain knowledge. The achievement of this aim has comprised the proposal of an attribute-based approach, which is used as a foundation to systematically develop an unsupervised-based two-stage correlation technique. From this formation, a novel SOM K-Means Alarm Reduction Tool (SMART) architecture has been modelled as the framework from which time and attribute-based aggregation technique is offered. The thesis describes the design and features of the proposed architecture, focusing upon the key components forming the underlying architecture, the alert attributes and the way they are processed and applied to correlate alerts. The architecture is strengthened by the development of a statistical tool, which offers a mean to perform results or alert analysis and comparison. The main concepts of the novel architecture are validated through the implementation of a prototype system. A series of experiments were conducted to assess the effectiveness of SMART in reducing false alarms. This aimed to prove the viability of implementing the system in a practical environment and that the study has provided appropriate contribution to knowledge in this field

Fuzzy intrusion detection

Visual data mining techniques are used to assess which metrics are most effective at detecting different types of attacks. The research confirms that data aggregation and data reduction play crucial roles in the formation of the metrics. Once the proper metrics are identified, fuzzy rules are constructed for detecting attacks in several categories. The attack categories are selected to match the different phases that intruders frequently use when attacking a system. A suite of attacks tools is assembled to test the fuzzy rules. The research shows that fuzzy rules applied to good metrics can provide an effective means of detecting a wide variety of network intrusion activity. This research is being used as a proof of concept for the development of system known as the Fuzzy Intrusion Recognition Engine (FIRE).This thesis examines the application of fuzzy systems to the problem of network intrusion detection. Historically, there have been two primary methods of performing intrusion detection: misuse detection and anomaly detection. In misuse detection, a database of attack signatures is maintained that match known intrusion activity. While misuse detection systems are very effective, they require constant updates to the signature database to remain effective or to detect distinctly new attacks. Anomaly detection systems attempt to discover suspicious behavior by comparing system activity against past usage profiles. In this research, network activity is collected and usage profiles established for a variety of metrics. A network data gathering and data analysis tool was developed to create the metrics from the network stream. Great care is given to identifying the metrics that are most suitable for detecting intrusion activity

A Survey of Botnet Detection Techniques by Command and Control Infrastructure

Botnets have evolved to become one of the most serious threats to the Internet and there is substantial research on both botnets and botnet detection techniques. This survey reviewed the history of botnets and botnet detection techniques. The survey showed traditional botnet detection techniques rely on passive techniques, primarily honeypots, and that honeypots are not effective at detecting peer-to-peer and other decentralized botnets. Furthermore, the detection techniques aimed at decentralized and peer-to-peer botnets focus on detecting communications between the infected bots. Recent research has shown hierarchical clustering of flow data and machine learning are effective techniques for detecting botnet peer-to-peer traffic