Machine Learning-Based Decision Support to Secure Internet of Things Sensing

Abstract

Internet of Things (IoT) has weaknesses due to the vulnerabilities in the wireless medium and massively interconnected nodes that form an extensive attack surface for adversaries. It is essential to ensure security including IoT networks and applications. The thesis focus on three streams in IoT scenario, including fake task attack detection in Mobile Crowdsensing (MCS), blockchain technique-integrated system security and privacy protection in MCS, and network intrusion detection in IoT. In this thesis, to begin, in order to detect fake tasks in MCS with promising performance, a detailed analysis is provided by modeling a deep belief network (DBN) when the available sensory data is scarce for analysis. With oversampling to cope with the class imbalance challenge, a Principal Component Analysis (PCA) module is implemented prior to the DBN and weights of various features of sensing tasks are analyzed under varying inputs. Additionally, an ensemble learning-based solution is proposed for MCS platforms to mitigate illegitimate tasks. Meanwhile, a k-means-based classification is integrated with the proposed ensemble method to extract region-specific features as input to the machine learning-based fake task detection. A novel approach that is based on horizontal Federated Learning (FL) is proposed to identify fake tasks that contain a number of independent detection devices and an aggregation entity. Moreover, the submitted tasks are collected and managed conventionally by a centralized MCS platform. A centralized MCS platform is not safe enough to protect and prevent tampering sensing tasks since it confronts the single point of failure which reduces the effectiveness and robustness of MCS system. In order to address the centralized issue and identify fake tasks, a blockchain-based decentralized MCS is designed. Integration of blockchain into MCS enables a decentralized framework. The distributed nature of a blockchain chain prevents sensing tasks from being tampered. The blockchain network uses a Practical Byzantine Fault Tolerance (PBFT) consensus that can tolerate 1/3 faulty nodes, making the implemented MCS system robust and sturdy. Lastly, Machine Learning (ML)-based frameworks are widely investigated to identity attacks in IoT networks, namely Network Intrusion Detection System (NIDS). ML models perform divergent detection performance in each class, so it is challenging to select one ML model applicable to all classes prediction. With this in mind, an innovative ensemble learning framework is proposed, two ensemble learning approaches, including All Predict Wisest Decides (APWD) and Predictor Of the Lowest Cost (POLC), are proposed based on the training of numerous ML models. According to the individual model outcomes, a wise model performing the best detection performance (e.g., F1 score) or contributing the lowest cost is determined. Moreover, an innovated ML-based framework is introduced, combining NIDS and host-based intrusion detection system (HIDS). The presented framework eliminates NIDS restrictions via observing the entire traffic information in host resources (e.g., logs, files, folders).