Browsing by Author "Kashif, Muhammad"

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BCPriPIoT: BlockChain utilized privacy-preservation mechanism for IoT devices
(IEEE, 2021) Kashif, Muhammad; Çakmakçı, Kübra Kalkan; Computer Science; ÇAKMAKCİ, Kübra Kalkan; Kashif, Muhammad
Security and privacy are the primary concerns for IoT devices but because of their inherent limitation both in terms of processing and energy, IoT devices cannot be deployed at their full scale. To alleviate such security and privacy issues, the interaction of blockchain with IoT systems has acquired significant attention these days because blockchain has presented an underlying mechanism of immutability, audibility, and transparency for data storage. However due to the intrinsic nature of a blockchain containing complex mathematical proof concepts such as Merkle Hash Tree and Proof of Work (PoW) which demands high computation power making it less viable for IoT devices to be connected with. To overcome these issues, a novel scheme is proposed in this paper, which deploys private by design based blockchain architecture for IoT devices using low complex consensus algorithm and low computation cryptographic mechanism which suits best for IoT devices to address the privacy concerns. Unlike the traditional blockchain network in which every node maintained a copy of the transaction, we have proposed a new architecture in which block validation and block generation logic has been modified so that a transaction will be limited to the trusted recipient only. The proposed scheme outperforms the contemporary approaches both in terms of throughput and latency as observed through simulation results as well as maintaining the privacy concerns which will encourage the actual implementation of IoT applications in the real world. Moreover, the evaluation analysis demonstrate that the approach has major potential in a trusted network computing system and provides a substantial secure environment for IoT users.
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EPIoT: Enhanced privacy preservation based blockchain mechanism for internet-of-things
(Elsevier, 2024-01) Kashif, Muhammad; Çakmakçı, Kübra Kalkan; Computer Science; ÇAKMAKCİ, Kübra Kalkan; Kashif, Muhammad
With the increasing popularity of the Internet of things (IoT) and giving the end users the opportunity of collecting and analyzing the data by these IoT devices give rise to ultimate privacy concern and is attracting significant attention nowadays. These IoT devices may contain highly sensitive data and data sharing processes which may lead to security and privacy concerns. To surmount these issues, the interaction of IoT with blockchain for a secure transaction is accepted as a candidate solution. However, the innate behavior of blockchain containing complex mathematical proofs and consensus protocol requires high computational power making it less favorable for IoT devices to be connected with. Motivated by a private by-design framework and emphasizing greater control and setting of privacy preferences by the data owner, this paper complements our previous work on privacy preservation in IoT networks. In this paper, we design and propound a complete blockchain-based privacy-preserving framework by deploying service-oriented layers concepts and low computation cryptography, and a less complex consensus protocol to address the privacy concern. Moreover, this paper will unravel the complete end-to-end architecture of IoT-based blockchain purposely build for secure transactions in IoT networks. Security analysis is conducted using AVISPA tool to show that the proposed algorithms attain the desired security goals. This is followed by extensive simulation experiments and ultimate output results cultivating it much favorably for the deployment of IoT applications in real life.
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Machine learning based activity learning for behavioral contexts in Internet of things (IoT)
(Springer Nature, 2020-12) Safyan, M.; Sarwar, S.; Ul Qayyum, Z.; Iqbal, M.; Li, S. C.; Kashif, Muhammad; Kashif, Muhammad
Ontology based activity learning models play a vital role in diverse fields of Internet of Things (IoT) such as smart homes, smart hospitals or smart communities etc. The prevalent challenges with ontological models are their static nature and inability of self-evolution. The models cannot be completed at once and smart home inhabitants cannot be restricted to limit their activities. Also, inhabitants are not predictable in nature and may perform "Activities of Daily Life (ADL)" not listed in ontological model. This gives rise to the need of developing an integrated framework based on unified conceptual backbone (i.e. activity ontologies), addressing the lifecycle of activity recognition and producing behavioral models according to inhabitant's routine. In this paper, an ontology evolution process has been proposed that learns particular activities from existing set of activities in daily life (ADL). It learns new activities that have not been identified by the recognition model, adds new properties with existing activities and learns inhabitant's newest behavior of performing activities through Artificial Neural Network (ANN). The better degree of true positivity is evidence of activity recognition with detection of noisy sensor data. Effectiveness of proposed approach is evident from improved rate of activity learning, activity detection and ontology evolution.