Date of Award
Master of Science
Electrical and Computer Engineering
Networking and distributed computing system have provided computational resources for machine learning (ML) application for a long time. Network system itself also can benefit from ML technologies. For example high performance packet classification is a key component to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing demand in the line rate for core networks, a great challenge is to use hand-tuned heuristic approaches to design a scalable and high performance packet classification solution. By exploiting the sparsity present in a ruleset, in this thesis an algorithm is proposed to use few effective bits (EBs) to extract a large number of candidate rules with just a few number of memory access. These effective bits are learned with deep reinforcement learning and they are used to create a bitmap to filter out the majority of rules which do not need to be fully matched to improve the online system performance. Utilizing reinforcement learning allows the proposed solution to be learning based rather than heuristic based algorithms. So proposed learning-based selection method is independent of the ruleset, which can be applied to different rulesets without relying on the heuristics. Proposed multibit tries classification engine outperforms lookup time both in worst and average case by 55% and reduce memory footprint, compared to traditional decision tree without EBs. Furthermore, many field packet classification are required for openFlow supported switches. With the proliferation of fields in the packet header, a traditional 5-field classification technique isn’t applicable for an efficient classification engine for those openFlow supported switches. Although the algorithmic insights obtained from 5-field classification techniques could still be applied for many field classification engine. To decompose given fields of a ruleset, different grouping metrics like standard deviation of individual fields and a novel metric called Diversity Index (DI) is considered for such many field scenarios. A detailed discussion and evaluation of how to decompose rule fields/dimension into subgroup, how a decision tree construction can be considered as reinforcement learning problem, and how to encode state and action space, reward calculation to effectively build trees for each subgroup with a global optimization objective is introduced in this work. Finally, to identify benign or malicious heterogeneous type of traffic present in a modern home network, a deep neural network based approach is introduced. A split architecture of such traffic classifier, in application of home network intrusion detection system consists of multiple machine learning (ML) models. These models trained on two separate dataset for heterogeneous traffic types. An analysis of run-time implementation performance of the proposed IDS models is also discussed.
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