Enhancing the security of RCIA ultra-lightweight authentication protocol by using random number generator (RNG) technique

With the growing demand for low-cost Radio Frequency Identification (RFID) system, there is a necessity to design RFID ultra-lightweight authentication protocols to be compatible with the system and also resistant against possible attacks. However, the existing ultra-lightweight authentication protocols are susceptible to wide range of attacks. This study is an attempt to enhance the security of Robust Confidentiality, Integrity, and Authentication (RCIA) ultra-lightweight authentication protocols especially with regard to privacy issue. In the RCIA protocol, IDs value is sent between reader and tag as a constant value. The constant value will enable attacker to trace the location of the tag which violates the privacy users. In order to enhance the security of RCIA protocol, Random Number Generator (RNG) technique has been used. This technique relies on generating random numbers in the tag side, based on Bitwise operations. The idea of this technique is to change the IDs of a tag on every query session so that it will not stay as a constant value. The implementation of Enhanced RCIA has been conducted by using a simulation. The simulation provided the ability to show that the operations of RCIA protocol as to compare with the enhanced RCIA. The outcome shows that the enhanced RCIA outperforms existing one in terms of privacy

Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems

Multi-objective swarm intelligence-based (MOSI-based) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) with conflicting objectives. Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. The reference point is determined by the decision maker to guide the search process to a particular region in the true Pareto front. However, HHMO algorithm produces a poor approximation to the Pareto front because lack of information sharing in its population update strategy, equal division of convergence parameter and randomly generated initial population. A two-step enhanced non-dominated sorting HHMO (2SENDSHHMO) algorithm has been proposed to solve this problem. The algorithm includes (i) a population update strategy which improves the movement of hawks in the search space, (ii) a parameter adjusting strategy to control the transition between exploration and exploitation, and (iii) a population generating method in producing the initial candidate solutions. The population update strategy calculates a new position of hawks based on the flush-and-ambush technique of Harris’s hawks, and selects the best hawks based on the non-dominated sorting approach. The adjustment strategy enables the parameter to adaptively changed based on the state of the search space. The initial population is produced by generating quasi-random numbers using Rsequence followed by adapting the partial opposition-based learning concept to improve the diversity of the worst half in the population of hawks. The performance of the 2S-ENDSHHMO has been evaluated using 12 MOPs and three engineering MOPs. The obtained results were compared with the results of eight state-of-the-art multi-objective optimization algorithms. The 2S-ENDSHHMO algorithm was able to generate non-dominated solutions with greater convergence and diversity in solving most MOPs and showed a great ability in jumping out of local optima. This indicates the capability of the algorithm in exploring the search space. The 2S-ENDSHHMO algorithm can be used to improve the search process of other MOSI-based algorithms and can be applied to solve MOPs in applications such as structural design and signal processing

Enhancing the security of RCIA ultra-lightweight authentication protocol by using Random Number Generator (RNG) technique

This study is an attempt to enhance the security of Robust Confidentiality, Integrity, and Authentication (RCIA) ultra-lightweight authentication protocols.In the RCIA protocol, IDs value is sent between reader and tag as a constant value.This makes RCIA susceptible to traceability attack which lead to the privacy issue. In order to overcome this problem, Random Number Generator (RNG) technique based on Bitwise operations has been used in the tag side.The idea of this technique is to change the IDs of a tag on every query session so that it will not stay as a constant value.The implementation of Enhanced RCIA has been conducted by using a simulation.The simulation provided the ability to show that the operations of RCIA protocol as to compare with the enhanced RCIA.The outcome shows that the enhanced RCIA outperforms existing one in terms of privacy

Taxonomy of memory usage in swarm intelligence-based metaheuristics

Metaheuristics under the swarm intelligence (SI) class have proven to be efficient and have become popular methods for solving different optimization problems. Based on the usage of memory, metaheuristics can be classified into algorithms with memory and without memory (memory-less). The absence of memory in some metaheuristics will lead to the loss of the information gained in previous iterations. The metaheuristics tend to divert from promising areas of solutions search spaces which will lead to non-optimal solutions. This paper aims to review memory usage and its effect on the performance of the main SI-based metaheuristics. Investigation has been performed on SI metaheuristics, memory usage and memory-less metaheuristics, memory characteristics and memory in SI-based metaheuristics. The latest information and references have been further analyzed to extract key information and mapped into respective subsections. A total of 50 references related to memory usage studies from 2003 to 2018 have been investigated and show that the usage of memory is extremely necessary to increase effectiveness of metaheuristics by taking the advantages from their previous successful experiences. Therefore, in advanced metaheuristics, memory is considered as one of the fundamental elements of an efficient metaheuristic. Issues in memory usage have also been highlighted. The results of this review are beneficial to the researchers in developing efficient metaheuristics, by taking into consideration the usage of memory

Non-dominated sorting Harris’s hawk multi-objective optimizer based on reference point approach

A non-dominated sorting Harris’s hawk multi- objective optimizer (NDSHHMO) algorithm is presented in this paper. The algorithm is able to improve the population diversity, convergence of non-dominated solutions toward the Pareto front, and prevent the population from trapping into local optimal. This was achieved by integrating fast non-dominated sorting with the original Harris’s hawk multi-objective optimizer (HHMO). Non-dominated sorting divides the objective space into levels based on fitness values and then selects non-dominated solutions to produce the next generation of hawks. A set of well-known multi-objective optimization problems has been used to evaluate the performance of the proposed NDSHHMO algorithm. The results of the NDSHHMO algorithm were verified against the results of an HHMO algorithm. Experimental results demonstrate the efficiency of the proposed NDSHHMO algorithm in terms of enhancing the ability of convergence toward the Pareto front and significantly improve the search ability of the HHMO

Review of Multi-Objective Swarm Intelligence Optimization Algorithms

Multi-objective swarm intelligence (MOSI) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) that consists of two or more conflict objectives, in which improving an objective leads to the degradation of the other. The MOSI algorithms are based on the integration of single objective algorithms and multi-objective optimization (MOO) approach. The MOO approaches include scalarization, Pareto dominance, decomposition and indicator-based. In this paper, the status of MOO research and state-of-the-art MOSI algorithms namely, multi-objective particle swarm, artificial bee colony, firefly algorithm, bat algorithm, gravitational search algorithm, grey wolf optimizer, bacterial foraging and moth-flame optimization algorithms have been reviewed. These reviewed algorithms were mainly developed to solve continuous MOPs. The review is based on how the algorithms deal with objective functions using MOO approaches, the benchmark MOPs used in the evaluation and performance metrics. Furthermore, it describes the advantages and disadvantages of each MOO approach and provides some possible future research directions in this area. The results show that several MOO approaches have not been used in most of the proposed MOSI algorithms. Integrating other different MOO approaches may help in developing more effective optimization algorithms, especially in solving complex MOPs. Furthermore, most of the MOSI algorithms have been evaluated using MOPs with two objectives, which clarifies open issues in this research area

REVIEW OF THE MULTI-OBJECTIVE SWARM INTELLIGENCE OPTIMIZATION ALGORITHMS

Multi-objective swarm intelligence (MOSI) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) that consists of two or more conflict objectives, in which improving an objective leads to the degradation of the other. The MOSI algorithms are based on the integration of single objective algorithms and multi-objective optimization (MOO) approach. The MOO approaches include scalarization, Pareto dominance, decomposition and indicator-based. In this paper, the status of MOO research and state-of-the-art MOSI algorithms namely, multi-objective particle swarm, artificial bee colony, firefly algorithm, bat algorithm, gravitational search algorithm, grey wolf optimizer, bacterial foraging and moth-flame optimization algorithms have been reviewed. These reviewed algorithms were mainly developed to solve continuous MOPs. The review is based on how the algorithms deal with objective functions using MOO approaches, the benchmark MOPs used in the evaluation and performance metrics. Furthermore, it describes the advantages and disadvantages of each MOO approach and provides some possible future research directions in this area. The results show that several MOO approaches have not been used in most of the proposed MOSI algorithms. Integrating other different MOO approaches may help in developing more effective optimization algorithms, especially in solving complex MOPs. Furthermore, most of the MOSI algorithms have been evaluated using MOPs with two objectives, which clarifies open issues in this research area