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The interconnection of resource-constrained and globally accessible things with untrusted and unreliable Internet make them vulnerable to attacks including data forging, false data injection, and packet drop that affects applications with critical decision-making processes. For data trustworthiness, reliance on provenance is considered to be an effective mechanism that tracks both data acquisition and data transmission. However, provenance management for sensor networks introduces several challenges, such as low energy, bandwidth consumption, and efficient storage. This paper attempts to identify packet drop (either maliciously or due to network disruptions) and detect faulty or misbehaving nodes in the Routing Protocol for Low-Power and Lossy Networks (RPL) by following a bi-fold provenance-enabled packed path tracing (PPPT) approach. Firstly, a system-level ordered-provenance information encapsulates the data generating nodes and the forwarding nodes in the data packet. Secondly, to closely monitor the dropped packets, a node-level provenance in the form of the packet sequence number is enclosed as a routing entry in the routing table of each participating node. Lossless in nature, both approaches conserve the provenance size satisfying processing and storage requirements of IoT devices. Finally, we evaluate the efficacy of the proposed scheme with respect to provenance size, provenance generation time, and energy consumption.
This paper analyses the various authentication systems implemented for enhanced security and private re-position of an individuals log-in credentials. The first part of the paper describes the multi-factor authentication (MFA) systems, which, though
In the Internet-of-Things, the number of connected devices is expected to be extremely huge, i.e., more than a couple of ten billion. It is however well-known that the security for the Internet-of-Things is still open problem. In particular, it is di
Ever since the advent of computing, managing data has been of extreme importance. With innumerable devices getting added to network infrastructure, there has been a proportionate increase in the data which needs to be stored. With the advent of Inter
Internet of Things (IoT) based applications face an increasing number of potential security risks, which need to be systematically assessed and addressed. Expert-based manual assessment of IoT security is a predominant approach, which is usually inef
With the prevalence of Internet of Things (IoT) applications, IoT devices interact closely with our surrounding environments, bringing us unparalleled smartness and convenience. However, the development of secure IoT solutions is getting a long way l