The outbreak of COVID-19 pandemic has exposed an urgent need for effective contact tracing solutions through mobile phone applications to prevent the infection from spreading further. However, due to the nature of contact tracing, public concern on privacy issues has been a bottleneck to the existing solutions, which is significantly affecting the uptake of contact tracing applications across the globe. In this paper, we present a blockchain-enabled privacy-preserving contact tracing scheme: BeepTrace, where we propose to adopt blockchain bridging the user/patient and the authorized solvers to desensitize the user ID and location information. Compared with recently proposed contact tracing solutions, our approach shows higher security and privacy with the additional advantages of being battery friendly and globally accessible. Results show viability in terms of the required resource at both server and mobile phone perspectives. Through breaking the privacy concerns of the public, the proposed BeepTrace solution can provide a timely framework for authorities, companies, software developers and researchers to fast develop and deploy effective digital contact tracing applications, to conquer COVID-19 pandemic soon. Meanwhile, the open initiative of BeepTrace allows worldwide collaborations, integrate existing tracing and positioning solutions with the help of blockchain technology.

In this research paper, blockchain-based trust management model is proposed to enhance trust relationship among beacon nodes and to eradicate malicious nodes in Wireless Sensor Networks (WSNs). This composite trust evaluation involves behavioral-based trust as well as data-based trust. Various metrics such as closeness, honesty, intimacy and frequency of interaction are taken into account to compute behavioral-based trust of beacon nodes. Further, the composite (behavior and data) trust value of each beacon nodes is broadcast to Base Stations (BS) to generate a blockchain of trust values. Subsequently, the management model discards the beacon node with least trust value and that ensures reliability and consistency of localization in WSNs. The simulated results of the proposed algorithm are compared with the existing ones in terms of detection accuracy, False Positive Rate (FPR) and False Negative Rate (FNR) and Average Energy Consumption (AEC).

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While our conventional cryptography methods, such for AES (encryption), SHA-256 (hashing) and RSA/Elliptic Curve (signing), work well on systems which have reasonable processing power and memory capabilities, these do not scale well into a world with embedded systems and sensor networks. Thus, lightweight cryptography methods are proposed to overcome many of the problems of conventional cryptography. This includes constraints related to physical size, processing requirements, memory limitation and energy drain. This paper outlines many of the techniques that are defined as replacements for conventional cryptography within an Internet of things space and discuss some trends in the design of lightweight algorithms.

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.