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Author: search.filters.author.Dua, AmitSubject: search.filters.subject.Security

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    Secure message communication among vehicles using elliptic curve cryptography in smart cities
    (IEEE, 2016) Dua, Amit
    Message exchange among vehicles is an integral part of communication in smart cities. Messages are exchanged to inform the other vehicles about emergency situations such as-safety alerts, and location privacy. Due to the usage of an insecure wireless medium, malicious activities in vehicles, i.e., illegal use of the false messages, can astray other vehicles. Security in communication among the vehicles can be provided by encrypting the messages using various security keys. However, it has been found from the literature that existing schemes for secure communication require large key size, and therefore may these schemes may not be applicable to smart cities. To address these issues, a secure message communication scheme among vehicles based on elliptic curve cryptography (ECC) is proposed. The proposed scheme needs smaller key size leading to mathematically simple and cost effective solution. Furthermore, the scheme provides mutual authentication, confidentiality, and forward secrecy. Security analysis prove that the proposed scheme is suitable to be adapted in smart city environment.
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    Security of Cryptocurrencies in blockchain technology: State-of-art, challenges and future prospects
    (Elsevier, 2020-08) Gupta, Shashank; Dua, Amit
    In contemporary era of technologies, blockchain has acquired tremendous attention from various domains. It has wide spectrum of applications ranging from finance to social services and has greatly influenced the emerging business world. Since, blockchain technology is getting embedded in the e-commerce services, the cryptocurrencies are gaining huge prevalence. Bitcoin and ethereum are few such crypto currencies, which have utilized decentralized nature of blockchain. Blockchain can be considered as a distributed database system containing immutable ledgers, which are prone to attack by malicious users. Although, from the initial digital currency to the present smart contract, the utilities of blockchain have been harnessed, the innovative technology has to rely on cryptography for its security. There are several reports, which emphases on the vulnerabilities and security of blockchain, however, there is a lack of a comprehensive and methodical survey in both application and technical views. In this survey article, the authors cover various aspects related to blockchain including its taxonomies and the situations in which a particular category of blockchain should be applied. The authors also focusses on the structure of blockchain and the working of the ongoing transactions in the cryptocurrency network. In addition, the authors also specify various categories of consensus protocols, smart contracts, forks, techniques for generating the consensus. A detailed taxonomy of blockchain along with their features and related real-world applications is also discussed. In addition, existing key platforms of blockchain related to the cryptocurrencies, hyperledger and multichain are also discussed. Existing emerging vulnerabilities of blockchain related to the recent attacks on bitcoin and etherum is also presented along with the defensive methodologies and future trends in blockchain.
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    ConvXSS: A deep learning-based smart ICT framework against code injection attacks for HTML5 web applications in sustainable smart city infrastructure
    (Elsevier, 2022-05) Dua, Amit; Gupta, Shashank
    In this paper we propose ConvXSS, a novel deep learning approach for the detection of XSS and code injection attacks, followed by context-based sanitization of the malicious code if the model detects any malicious code in the application. Firstly, we briefly discuss XSS and code injection attacks that might pose threat to sustainable smart cities. Along with this, we discuss various approaches proposed previously for the detection and alleviation of these attacks followed by their respective limitations. Then we propose our deep learning model adopting whose novelty is based on the approach followed for Data Pre-Processing. Then we finally propose Context-based Sanitization to replace the malicious part of the code with sanitized code. Numerical experiments conducted on various datasets have shown various results out of which the best model has an accuracy of 99.42%, a precision of 99.81% and a recall of 99.35%. When compared with other state of the art techniques in this domain, our approach shows at par or in the best case, better results in terms of detection speed and accuracy of CSS attacks.