ORCID Profile
0000-0001-5571-4148
Current Organisation
Deakin University
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Publisher: Elsevier BV
Date: 02-2020
Publisher: Springer Science and Business Media LLC
Date: 28-11-2018
Publisher: Springer Science and Business Media LLC
Date: 03-04-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-03-2023
Publisher: MDPI AG
Date: 05-01-2023
Abstract: Drone-based surveillance has become widespread due to its flexibility and ability to access hazardous areas, particularly in industrial complexes. As digital camera capabilities improve, more visual information can be stored in high-resolution images, resulting in larger image sizes. Therefore, algorithms for encrypting digital images sent from drones must be both secure and highly efficient. This paper presents a novel algorithm based on DNA computing and a finite state machine (FSM). DNA and FSM are combined to design a key schedule with high flexibility and statistical randomness. The image encryption algorithm is designed to achieve both confusion and diffusion properties simultaneously. The DNA bases themselves provide diffusion, while the random integers extracted from the DNA bases contribute to confusion. The proposed algorithm underwent a thorough set of statistical analyses to demonstrate its security. Experimental findings show that the proposed algorithm can resist many well-known attacks and encrypt large-sized images at a higher throughput compared to other algorithms. High experimental results for the proposed algorithm include correlation coefficients of 0.0001 and Shannon entropy of 7.999. Overall, the proposed image encryption algorithm meets the requirements for use in drone-based surveillance applications.
Publisher: Springer Science and Business Media LLC
Date: 21-07-2021
Publisher: Elsevier BV
Date: 11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 07-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: Springer Science and Business Media LLC
Date: 07-02-2019
Publisher: IEEE
Date: 08-2016
Publisher: Elsevier BV
Date: 08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Hindawi Limited
Date: 2017
DOI: 10.1155/2017/9040518
Abstract: In recent years, there has been a rising interest in authenticated encryption with associated data (AEAD) which combines encryption and authentication into a unified scheme. AEAD schemes provide authentication for a message that is ided into two parts: associated data which is not encrypted and the plaintext which is encrypted. However, there is a lack of chaos-based AEAD schemes in recent literature. This paper introduces a new 128-bit chaos-based AEAD scheme based on the single-key Even-Mansour and Type-II generalized Feistel structure. The proposed scheme provides both privacy and authentication in a single-pass using only one 128-bit secret key. The chaotic tent map is used to generate whitening keys for the Even-Mansour construction, round keys, and random s-boxes for the Feistel round function. In addition, the proposed AEAD scheme can be implemented with true random number generators to map a message to multiple possible ciphertexts in a nondeterministic manner. Security and statistical evaluation indicate that the proposed scheme is highly secure for both the ciphertext and the authentication tag. Furthermore, it has multiple advantages over AES-GCM which is the current standard for authenticated encryption.
Publisher: Springer Science and Business Media LLC
Date: 30-07-2015
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 04-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 11-2019
Publisher: MDPI AG
Date: 27-07-2021
DOI: 10.3390/SYM13081363
Abstract: Blockchain networks are based on cryptographic notions that include asymmetric-key encryption, hash functions and consensus protocols. Despite their popularity, mainstream protocols, such as Proof of Work or Proof of Stake still have drawbacks. Efforts to enhance these protocols led to the birth of alternative consensus protocols, catering to specific areas, such as medicine or transportation. These protocols remain relatively unknown despite having unique merits worth investigating. Although past reviews have been published on popular blockchain consensus protocols, they do not include most of these lesser-known protocols. Highlighting these alternative consensus protocols contributes toward the advancement of the state of the art, as they have design features that may be useful to academics, blockchain practitioners and researchers. In this paper, we bridge this gap by providing an overview of alternative consensus protocols proposed within the past 3 years. We evaluate their overall performance based on metrics such as throughput, scalability, security, energy consumption, and finality. In our review, we examine the trade-offs that these consensus protocols have made in their attempts to optimize scalability and performance. To the best of our knowledge, this is the first paper that focuses on these alternative protocols, highlighting their unique features that can be used to develop future consensus protocols.
Publisher: Wiley
Date: 27-12-2019
DOI: 10.1002/NEM.2090
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 05-2022
Publisher: Springer Science and Business Media LLC
Date: 02-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 14-05-2020
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: IEEE
Date: 11-2015
Publisher: Springer Singapore
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 11-12-2018
Publisher: Informa UK Limited
Date: 21-12-2020
Publisher: Informa UK Limited
Date: 22-02-2017
Publisher: Springer Science and Business Media LLC
Date: 25-10-2019
Publisher: ACM
Date: 19-01-2019
Publisher: Springer Singapore
Date: 2021
Publisher: Hindawi Limited
Date: 11-10-2022
DOI: 10.1155/2022/5339926
Abstract: Malware detection refers to the process of detecting the presence of malware on a host system, or that of determining whether a specific program is malicious or benign. Machine learning-based solutions first gather information from applications and then use machine learning algorithms to develop a classifier that can distinguish between malicious and benign applications. Researchers and practitioners have long paid close attention to the issue. Most previous work has addressed the differences in feature importance or the computation of feature weights, which is unrelated to the classification model used, and therefore, the implementation of a selection approach with limited feature hiccups, and increases the execution time and memory usage. BFEDroid is a machine learning detection strategy that combines backward, forward, and exhaustive subset selection. This proposed malware detection technique can be updated by retraining new applications with true labels. It has higher accuracy (99%), lower memory consumption (1680), and a shorter execution time (1.264SI) than current malware detection methods that use feature selection.
Publisher: Elsevier BV
Date: 03-2022
Publisher: Informa UK Limited
Date: 12-09-2020
No related grants have been discovered for Je Sen Teh.