Improved performance high speed network intrusion detection systems (NIDS). A high speed NIDS architectures to address limitations of Packet Loss and Low Detection Rate by adoption of Dynamic Cluster Architecture and Traffic Anomaly Filtration (IADF).
SupervisorMellor, John E.
Awan, Irfan U.
KeywordIntrusion Detection Systems (IDS)
Dynamic Cluster Architecture
Low detection rate
Traffic Anomaly Filtration (IADF)
Denial of Service (DoS)
Increasing detection rate
The University of Bradford theses are licenced under a Creative Commons Licence.
InstitutionUniversity of Bradford
DepartmentSchool of Computing, Informatics and Media
MetadataShow full item record
AbstractIntrusion Detection Systems (IDS) are considered as a vital component in network security architecture. The system allows the administrator to detect unauthorized use of, or attack upon a computer, network or telecommunication infrastructure. There is no second thought on the necessity of these systems however; their performance remains a critical question. This research has focussed on designing a high performance Network Intrusion Detection Systems (NIDS) model. The work begins with the evaluation of Snort, an open source NIDS considered as a de-facto IDS standard. The motive behind the evaluation strategy is to analyze the performance of Snort and ascertain the causes of limited performance. Design and implementation of high performance techniques are considered as the final objective of this research. Snort has been evaluated on highly sophisticated test bench by employing evasive and avoidance strategies to simulate real-life normal and attack-like traffic. The test-methodology is based on the concept of stressing the system and degrading its performance in terms of its packet handling capacity. This has been achieved by normal traffic generation; fussing; traffic saturation; parallel dissimilar attacks; manipulation of background traffic, e.g. fragmentation, packet sequence disturbance and illegal packet insertion. The evaluation phase has lead us to two high performance designs, first distributed hardware architecture using cluster-based adoption and second cascaded phenomena of anomaly-based filtration and signature-based detection. The first high performance mechanism is based on Dynamic Cluster adoption using refined policy routing and Comparator Logic. The design is a two tier mechanism where front end of the cluster is the load-balancer which distributes traffic on pre-defined policy routing ensuring maximum utilization of cluster resources. The traffic load sharing mechanism reduces the packet drop by exchanging state information between load-balancer and cluster nodes and implementing switchovers between nodes in case the traffic exceeds pre-defined threshold limit. Finally, the recovery evaluation concept using Comparator Logic also enhance the overall efficiency by recovering lost data in switchovers, the retrieved data is than analyzed by the recovery NIDS to identify any leftover threats. Intelligent Anomaly Detection Filtration (IADF) using cascaded architecture of anomaly-based filtration and signature-based detection process is the second high performance design. The IADF design is used to preserve resources of NIDS by eliminating large portion of the traffic on well defined logics. In addition, the filtration concept augment the detection process by eliminating the part of malicious traffic which otherwise can go undetected by most of signature-based mechanisms. We have evaluated the mechanism to detect Denial of Service (DoS) and Probe attempts based by analyzing its performance on Defence Advanced Research Projects Agency (DARPA) dataset. The concept has also been supported by time-based normalized sampling mechanisms to incorporate normal traffic variations to reduce false alarms. Finally, we have observed that the IADF has augmented the overall detection process by reducing false alarms, increasing detection rate and incurring lesser data loss.
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An autonomous host-based intrusion detection and prevention system for Android mobile devices. Design and implementation of an autonomous host-based Intrusion Detection and Prevention System (IDPS), incorporating Machine Learning and statistical algorithms, for Android mobile devicesAbd-Alhameed, Raed A.; Shepherd, Simon J.; Mantas, G.; Ribeiro, José C.V.G. (University of BradfordSchool of Engineering, Design and Technology, 2019)This research work presents the design and implementation of a host-based Intrusion Detection and Prevention System (IDPS) called HIDROID (Host-based Intrusion Detection and protection system for andROID) for Android smartphones. It runs completely on the mobile device, with a minimal computation burden. It collects data in real-time, periodically sampling features that reflect the overall utilisation of scarce resources of a mobile device (e.g. CPU, memory, battery, bandwidth, etc.). The Detection Engine of HIDROID adopts an anomaly-based approach by exploiting statistical and machine learning algorithms. That is, it builds a data-driven model for benign behaviour and looks for the outliers considered as suspicious activities. Any observation failing to match this model triggers an alert and the preventive agent takes proper countermeasure(s) to minimise the risk. The key novel characteristic of the Detection Engine of HIDROID is the fact that it requires no malicious data for training or tuning. In fact, the Detection Engine implements the following two anomaly detection algorithms: a variation of K-Means algorithm with only one cluster and the univariate Gaussian algorithm. Experimental test results on a real device show that HIDROID is well able to learn and discriminate normal from anomalous behaviour, demonstrating a very promising detection accuracy of up to 0.91, while maintaining false positive rate below 0.03. Finally, it is noteworthy to mention that to the best of our knowledge, publicly available datasets representing benign and abnormal behaviour of Android smartphones do not exist. Thus, in the context of this research work, two new datasets were generated in order to evaluate HIDROID.
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