Identifying Malicious Nodes in Multihop IoT Networks using Dual Link Technologies and Unsupervised Learning

Packet manipulation attack is one of the challenging threats in cyber-physical systems (CPSs) and Internet of Things (IoT), where information packets are corrupted during transmission by compromised devices. These attacks consume network resources, result in delays in decision making, and could potentially lead to triggering wrong actions that disrupt an overall system's operation. Such malicious attacks as well as unintentional faults are difficult to locate/identify in a large-scale mesh-like multihop network, which is the typical topology suggested by most IoT standards. In this paper, first, we propose a novel network architecture that utilizes powerful nodes that can support two distinct communication link technologies for identification of malicious networked devices (with typical singlelink technology). Such powerful nodes equipped with dual-link technologies can reveal hidden information within meshed connections that is hard to otherwise detect. By applying machine intelligence at the dual-link nodes, malicious networked devices in an IoT network can be accurately identified. Second, we propose two techniques based on unsupervised machine learning, namely hard detection and soft detection, that enable dual-link nodes to identify malicious networked devices. Our techniques exploit network diversity as well as the statistical information computed by dual-link nodes to identify the trustworthiness of resource-constrained devices. Simulation results show that the detection accuracy of our algorithms is superior to the conventional watchdog scheme, where nodes passively listen to neighboring transmissions to detect corrupted packets. The results also show that as the density of the dual-link nodes increases, the detection accuracy improves and the false alarm rate decreases

Data Credence in IoR: Vision and Challenges

As the Internet of Things permeates every aspect of human life, assessing the credence or integrity of the data generated by "things" becomes a central exercise for making decisions or in auditing events. In this paper, we present a vision of this exercise that includes the notion of data credence, assessing data credence in an efficient manner, and the use of technologies that are on the horizon for the very large scale Internet of Things

Data Credence in IoT: Vision and Challenges

As the Internet of Things permeates every aspect of human life, assessing the credence or integrity of the data generated by "things" becomes a central exercise for making decisions or in auditing events. In this paper, we present a vision of this exercise that includes the notion of data credence, assessing data credence in an efficient manner, and the use of technologies that are on the horizon for the very large scale Internet of Things

A Time-Efficient Optimization for Robust Image Watermarking using Machine Learning

Watermarking is utilized for securing multimedia data exchange. The techniques used to embed a watermark typically require optimizing scheme parameters often through applying meta-heuristic optimization techniques. Although meta-heuristic techniques have been widely used due to their performance enhancing capability, they are not suitable for time sensitive applications due to their large time consumption. In this paper, a time-efficient optimization based on machine learning is proposed to find the best embedding strength parameter for robust image watermarking in terms of both the imperceptibility and robustness. First, a watermark embedding scheme is designed in the Discrete Cosine Transform domain, which provide a proper robustness against common watermarking attacks. Then, a training process is performed through selecting set of images upon which the watermarking is applied and optimized using the Artificial Bee Colony algorithm. Observation data is collected, which includes the optimum strength values along with the feature vectors that represent the training images. The features, of an image, are extracted by calculating the optimization fitness function at different values of the embedding strength. Finally, new set of images are chosen to be watermarked using the optimum embedding parameters that are predicted through the K-Nearest Neighborhood regression method. Experimental results show that the proposed method consumes considerably less time to evaluate the optimum solutions compared to using meta-heuristic optimization. Meanwhile, the error between the optimum and predicted optimum solutions has negligible impact on the optimization objectives