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Alireza Jolfaei

Bio: Alireza Jolfaei is an academic researcher from Macquarie University. The author has contributed to research in topics: Computer science & Encryption. The author has an hindex of 22, co-authored 141 publications receiving 1803 citations. Previous affiliations of Alireza Jolfaei include Temple University & Griffith University.


Papers
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Journal ArticleDOI
TL;DR: This article proposes a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory, and proves that the proposed framework can achieve guaranteed performance.
Abstract: Edge computing provides a promising paradigm to support the implementation of Industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resource-limited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this article, we consider the optimization of channel selection that is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to long-term constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory. We provide rigorous theoretical analysis, and prove that the proposed framework can achieve guaranteed performance with a bounded deviation from the optimal performance with global state information (GSI) based on only local and causal information. Finally, simulations are conducted under both single-MTD and multi-MTD scenarios to verify the effectiveness and reliability of the proposed framework.

214 citations

Journal ArticleDOI
TL;DR: A class of n-order nonlinear systems is considered as a model of CPS while it is in presence of cyber attacks only in the forward channel, and an intelligent-classic control system is developed to compensate cyber-attacks.
Abstract: This article proposes a hybrid intelligent-classic control approach for reconstruction and compensation of cyber attacks launched on inputs of nonlinear cyber-physical systems (CPS) and industrial Internet of Things systems, which work through shared communication networks. In this article, a class of n -order nonlinear systems is considered as a model of CPS while it is in presence of cyber attacks only in the forward channel. An intelligent-classic control system is developed to compensate cyber-attacks. Neural network (NN) is designed as an intelligent estimator for attack estimation and a classic nonlinear control system based on the variable structure control method is designed to compensate the effect of attacks and control the system performance in tracking applications. In the proposed strategy, nonlinear control theory is applied to guarantee the stability of the system when attacks happen. In this strategy, a Gaussian radial basis function NN is used for online estimation and reconstruction of cyber-attacks launched on the networked system. An adaptation law of the intelligent estimator is derived from a Lyapunov function. Simulation results demonstrate the validity and feasibility of the proposed strategy in car cruise control application as the testbed.

190 citations

Journal ArticleDOI
TL;DR: This present study focuses on applying machine learning model for classifying tomato disease image dataset to proactively take necessary steps to combat such agricultural crisis.
Abstract: The human population is growing at a very rapid scale. With this progressive growth, it is extremely important to ensure that healthy food is available for the survival of the inhabitants of this planet. Also, the economy of developing countries is highly dependent on agricultural production. The overall economic balance gets affected if there is a variance in the demand and supply of food or agricultural products. Diseases in plants are a great threat to the yield of the crops thereby causing famines and economy slow down. Our present study focuses on applying machine learning model for classifying tomato disease image dataset to proactively take necessary steps to combat such agricultural crisis. In this work, the dataset is collected from publicly available plant–village dataset. The significant features are extracted from the dataset using the hybrid-principal component analysis–Whale optimization algorithm. Further the extracted data are fed into a deep neural network for classification of tomato diseases. The proposed model is then evaluated with the classical machine learning techniques to establish the superiority in terms of accuracy and loss rate metrics.

182 citations

Journal ArticleDOI
TL;DR: It is proved that in all permutationonly image ciphers, regardless of the cipher structure, the correct permutation mapping is recovered completely by a chosenplaintext attack, which significantly outperforms the state-of-theart cryptanalytic methods.
Abstract: Permutation is a commonly used primitive in multimedia (image/video) encryption schemes, and many permutation-only algorithms have been proposed in recent years for the protection of multimedia data. In permutation-only image ciphers, the entries of the image matrix are scrambled using a permutation mapping matrix which is built by a pseudo-random number generator. The literature on the cryptanalysis of image ciphers indicates that the permutation-only image ciphers are insecure against ciphertext-only attacks and/or known/chosen-plaintext attacks. However, the previous studies have not been able to ensure the correct retrieval of the complete plaintext elements. In this paper, we revisited the previous works on cryptanalysis of permutation-only image encryption schemes and made the cryptanalysis work on chosen-plaintext attacks complete and more efficient. We proved that in all permutation-only image ciphers, regardless of the cipher structure, the correct permutation mapping is recovered completely by a chosen-plaintext attack. To the best of our knowledge, for the first time, this paper gives a chosen-plaintext attack that completely determines the correct plaintext elements using a deterministic method. When the plain-images are of size ${M}\times {N}$ and with ${L}$ different color intensities, the number ${n}$ of required chosen plain-images to break the permutation-only image encryption algorithm is ${n}=\lceil \log _{L}$ ( MN ) $\rceil $ . The complexity of the proposed attack is $O$ ( $n\,\cdot \, {M N}$ ) which indicates its feasibility in a polynomial amount of computation time. To validate the performance of the proposed chosen-plaintext attack, numerous experiments were performed on two recently proposed permutation-only image/video ciphers. Both theoretical and experimental results showed that the proposed attack outperforms the state-of-the-art cryptanalytic methods.

169 citations

Journal ArticleDOI
TL;DR: Challenges and recommendations for SSC network traffic classification with the dataset of features are presented and some well-known and most used datasets with details statistical features are described.

137 citations


Cited by
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Journal ArticleDOI
TL;DR: The analysis of recent advances in genetic algorithms is discussed and the well-known algorithms and their implementation are presented with their pros and cons with the aim of facilitating new researchers.
Abstract: In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

1,271 citations

Book
01 Dec 2010
TL;DR: In this article, a self consistent treatment of the subject at the graduate level and as a reference for scientists already working in the field is presented. But the focus is on the mechanics for generating chaotic motion, methods of calculating the transitions from regular to chaotic motion and the dynamical and statistical properties of the dynamics when it is chaotic.
Abstract: This book treats nonlinear dynamics in both Hamiltonian and dissipative systems. The emphasis is on the mechanics for generating chaotic motion, methods of calculating the transitions from regular to chaotic motion, and the dynamical and statistical properties of the dynamics when it is chaotic. The book is intended as a self consistent treatment of the subject at the graduate level and as a reference for scientists already working in the field. It emphasizes both methods of calculation and results. It is accessible to physicists and engineers without training in modern mathematics. The new edition brings the subject matter in a rapidly expanding field up to date, and has greatly expanded the treatment of dissipative dynamics to include most important subjects. It can be used as a graduate text for a two semester course covering both Hamiltonian and dissipative dynamics.

996 citations

Journal ArticleDOI
TL;DR: A novel image encryption approach based on permutation-substitution (SP) network and chaotic systems that shows superior performance than previous schemes.

399 citations