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2021-07-27
Xu, Jiahui, Wang, Chen, Li, Tingting, Xiang, Fengtao.  2020.  Improved Adversarial Attack against Black-box Machine Learning Models. 2020 Chinese Automation Congress (CAC). :5907–5912.
The existence of adversarial samples makes the security of machine learning models in practical application questioned, especially the black-box adversarial attack, which is very close to the actual application scenario. Efficient search for black-box attack samples is helpful to train more robust models. We discuss the situation that the attacker can get nothing except the final predict label. As for this problem, the current state-of-the-art method is Boundary Attack(BA) and its variants, such as Biased Boundary Attack(BBA), however it still requires large number of queries and kills a lot of time. In this paper, we propose a novel method to solve these shortcomings. First, we improved the algorithm for generating initial adversarial samples with smaller L2 distance. Second, we innovatively combine a swarm intelligence algorithm - Particle Swarm Optimization(PSO) with Biased Boundary Attack and propose PSO-BBA method. Finally, we experiment on ImageNet dataset, and compared our algorithm with the baseline algorithm. The results show that:(1)our improved initial point selection algorithm effectively reduces the number of queries;(2)compared with the most advanced methods, our PSO-BBA method improves the convergence speed while ensuring the attack accuracy;(3)our method has a good effect on both targeted attack and untargeted attack.
2021-06-01
Mohammed, Alshaimaa M., Omara, Fatma A..  2020.  A Framework for Trust Management in Cloud Computing Environment. 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE). :7–13.
Cloud Computing is considered as a business model for providing IT resources as services through the Internet based on pay-as-you-go principle. These IT resources are provided by Cloud Service Providers (CSPs) and requested by Cloud Service Consumers (CSCs). Selecting the proper CSP to deliver services is a critical and strategic process. According to the work in this paper, a framework for trust management in cloud computing has been introduced. The proposed framework consists of five stages; Filtrating, Trusting, Similarity, Ranking and Monitoring. In the Filtrating stage, the existing CSPs in the system will be filtered based on their parameters. The CSPs trust values are calculated in the Trusting stage. Then, the similarity between the CSC requirements and the CSPs data is calculated in the Similarity stage. The ranking of CSPs will be performed in Ranking stage. According to the Monitoring stage, after finishing the service, the CSC sends his feedbacks about the CSP who delivered the service to be used to monitor this CSP. To evaluate the performance of the proposed framework, a comparative study has been done for the Ranking and Monitoring stages using Armor dataset. According to the comparative results it is found that the proposed framework increases the reliability and performance of the cloud environment.
2021-03-29
Al-Janabi, S. I. Ali, Al-Janabi, S. T. Faraj, Al-Khateeb, B..  2020.  Image Classification using Convolution Neural Network Based Hash Encoding and Particle Swarm Optimization. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI). :1–5.
Image Retrieval (IR) has become one of the main problems facing computer society recently. To increase computing similarities between images, hashing approaches have become the focus of many programmers. Indeed, in the past few years, Deep Learning (DL) has been considered as a backbone for image analysis using Convolutional Neural Networks (CNNs). This paper aims to design and implement a high-performance image classifier that can be used in several applications such as intelligent vehicles, face recognition, marketing, and many others. This work considers experimentation to find the sequential model's best configuration for classifying images. The best performance has been obtained from two layers' architecture; the first layer consists of 128 nodes, and the second layer is composed of 32 nodes, where the accuracy reached up to 0.9012. The proposed classifier has been achieved using CNN and the data extracted from the CIFAR-10 dataset by the inception model, which are called the Transfer Values (TRVs). Indeed, the Particle Swarm Optimization (PSO) algorithm is used to reduce the TRVs. In this respect, the work focus is to reduce the TRVs to obtain high-performance image classifier models. Indeed, the PSO algorithm has been enhanced by using the crossover technique from genetic algorithms. This led to a reduction of the complexity of models in terms of the number of parameters used and the execution time.
Dai, Q., Shi, L..  2020.  A Game-Theoretic Analysis of Cyber Attack-Mitigation in Centralized Feeder Automation System. 2020 IEEE Power Energy Society General Meeting (PESGM). :1–5.
The intelligent electronic devices widely deployed across the distribution network are inevitably making the feeder automation (FA) system more vulnerable to cyber-attacks, which would lead to disastrous socio-economic impacts. This paper proposes a three-stage game-theoretic framework that the defender allocates limited security resources to minimize the economic impacts on FA system while the attacker deploys limited attack resources to maximize the corresponding impacts. Meanwhile, the probability of successful attack is calculated based on the Bayesian attack graph, and a fault-tolerant location technique for centralized FA system is elaborately considered during analysis. The proposed game-theoretic framework is converted into a two-level zero-sum game model and solved by the particle swarm optimization (PSO) combined with a generalized reduced gradient algorithm. Finally, the proposed model is validated on distribution network for RBTS bus 2.
2020-12-14
Xu, S., Ouyang, Z., Feng, J..  2020.  An Improved Multi-objective Particle Swarm Optimization. 2020 5th International Conference on Computational Intelligence and Applications (ICCIA). :19–23.
For solving multi-objective optimization problems, this paper firstly combines a multi-objective evolutionary algorithm based on decomposition (MOEA/D) with good convergence and non-dominated sorting genetic algorithm II (NSGA-II) with good distribution to construct. Thus we propose a hybrid multi-objective optimization solving algorithm. Then, we consider that the population diversity needs to be improved while applying multi-objective particle swarm optimization (MOPSO) to solve the multi-objective optimization problems and an improved MOPSO algorithm is proposed. We give the distance function between the individual and the population, and the individual with the largest distance is selected as the global optimal individual to maintain population diversity. Finally, the simulation experiments are performed on the ZDT\textbackslashtextbackslashDTLZ test functions and track planning problems. The results indicate the better performance of the improved algorithms.
Cai, L., Hou, Y., Zhao, Y., Wang, J..  2020.  Application research and improvement of particle swarm optimization algorithm. 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). :238–241.
Particle swarm optimization (PSO), as a kind of swarm intelligence algorithm, has the advantages of simple algorithm principle, less programmable parameters and easy programming. Many scholars have applied particle swarm optimization (PSO) to various fields through learning it, and successfully solved linear problems, nonlinear problems, multiobjective optimization and other problems. However, the algorithm also has obvious problems in solving problems, such as slow convergence speed, too early maturity, falling into local optimization in advance, etc., which makes the convergence speed slow, search the optimal value accuracy is not high, and the optimization effect is not ideal. Therefore, many scholars have improved the particle swarm optimization algorithm. Taking into account the improvement ideas proposed by scholars in the early stage and the shortcomings still existing in the improvement, this paper puts forward the idea of improving particle swarm optimization algorithm in the future.
Gu, Y., Liu, N..  2020.  An Adaptive Grey Wolf Algorithm Based on Population System and Bacterial Foraging Algorithm. 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). :744–748.
In this thesis, an modified algorithm for grey wolf optimization in swarm intelligence optimization algorithm is proposed, which is called an adaptive grey wolf algorithm (AdGWO) based on population system and bacterial foraging optimization algorithm (BFO). In view of the disadvantages of premature convergence and local optimization in solving complex optimization problems, the AdGWO algorithm uses a three-stage nonlinear change function to simulate the decreasing change of the convergence factor, and at the same time integrates the half elimination mechanism of the BFO. These improvements are more in line with the actual situation of natural wolves. The algorithm is based on 23 famous test functions and compared with GWO. Experimental results demonstrate that this algorithm is able to avoid sinking into the local optimum, has good accuracy and stability, is a more competitive algorithm.
Tousi, S. Mohamad Ali, Mostafanasab, A., Teshnehlab, M..  2020.  Design of Self Tuning PID Controller Based on Competitional PSO. 2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC). :022–026.
In this work, a new particle swarm optimization (PSO)-based optimization algorithm, and the idea of a running match is introduced and employed in a non-linear system PID controller design. This algorithm aims to modify the formula of velocity calculating of the general PSO method to increase the diversity of the searching process. In this process of designing an optimal PID controller for a non-linear system, the three gains of the PID controller form a particle, which is a parameter vector and will be updated iteratively. Many of those particles then form a population. To reach the PID gains which are optimum, using modified velocity updating formula and position updating formula, the position of all particles of the population will be moved into the optimization direction. In the meanwhile, an objective function may be minimized as the performance of the controller get improved. To corroborate the controller functioning of this method, a non-linear system known as inverted pendulum will be controlled by the designed PID controller. The results confirm that the new method can show excellent performance in the non-linear PID controller design task.
Goudos, S. K., Diamantoulakis, P. D., Boursianis, A. D., Papanikolaou, V. K., Karagiannidis, G. K..  2020.  Joint User Association and Power Allocation Using Swarm Intelligence Algorithms in Non-Orthogonal Multiple Access Networks. 2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST). :1–4.
In this paper, we address the problem of joint user association and power allocation for non-orthogonal multiple access (NOMA) networks with multiple base stations (BSs). A user grouping procedure into orthogonal clusters, as well as an allocation of different physical resource blocks (PRBs) is considered. The problem of interest is mathematically described using the maximization of the weighted sum rate. We apply two different swarm intelligence algorithms, namely, the recently introduced Grey Wolf Optimizer (GWO), and the popular Particle Swarm Optimization (PSO), in order to solve this problem. Numerical results demonstrate that the above-described problem can be satisfactorily addressed by both algorithms.
Zhou, J.-L., Wang, J.-S., Zhang, Y.-X., Guo, Q.-S., Li, H., Lu, Y.-X..  2020.  Particle Swarm Optimization Algorithm with Variety Inertia Weights to Solve Unequal Area Facility Layout Problem. 2020 Chinese Control And Decision Conference (CCDC). :4240–4245.
The unequal area facility layout problem (UA-FLP) is to place some objects in a specified space according to certain requirements, which is a NP-hard problem in mathematics because of the complexity of its solution, the combination explosion and the complexity of engineering system. Particle swarm optimization (PSO) algorithm is a kind of swarm intelligence algorithm by simulating the predatory behavior of birds. Aiming at the minimization of material handling cost and the maximization of workshop area utilization, the optimization mathematical model of UA-FLPP is established, and it is solved by the particle swarm optimization (PSO) algorithm which simulates the design of birds' predation behavior. The improved PSO algorithm is constructed by using nonlinear inertia weight, dynamic inertia weight and other methods to solve static unequal area facility layout problem. The effectiveness of the proposed method is verified by simulation experiments.
Willcox, G., Rosenberg, L., Domnauer, C..  2020.  Analysis of Human Behaviors in Real-Time Swarms. 2020 10th Annual Computing and Communication Workshop and Conference (CCWC). :0104–0109.
Many species reach group decisions by deliberating in real-time systems. This natural process, known as Swarm Intelligence (SI), has been studied extensively in a range of social organisms, from schools of fish to swarms of bees. A new technique called Artificial Swarm Intelligence (ASI) has enabled networked human groups to reach decisions in systems modeled after natural swarms. The present research seeks to understand the behavioral dynamics of such “human swarms.” Data was collected from ten human groups, each having between 21 and 25 members. The groups were tasked with answering a set of 25 ordered ranking questions on a 1-5 scale, first independently by survey and then collaboratively as a real-time swarm. We found that groups reached significantly different answers, on average, by swarm versus survey ( p=0.02). Initially, the distribution of individual responses in each swarm was little different than the distribution of survey responses, but through the process of real-time deliberation, the swarm's average answer changed significantly ( ). We discuss possible interpretations of this dynamic behavior. Importantly, the we find that swarm's answer is not simply the arithmetic mean of initial individual “votes” ( ) as in a survey, suggesting a more complex mechanism is at play-one that relies on the time-varying behaviors of the participants in swarms. Finally, we publish a set of data that enables other researchers to analyze human behaviors in real-time swarms.
Ababii, V., Sudacevschi, V., Braniste, R., Nistiriuc, A., Munteanu, S., Borozan, O..  2020.  Multi-Robot System Based on Swarm Intelligence for Optimal Solution Search. 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). :1–5.
This work presents the results of the Multi-Robot System designing that works on the basis of Swarm Intelligence models and is used to search for optimal solutions. The process of searching for optimal solutions is performed based on a field of gradient vectors that can be generated by ionizing radiation sources, radio-electro-magnetic devices, temperature generating sources, etc. The concept of the operation System is based on the distribution in the search space of a multitude of Mobile Robots that form a Mesh network between them. Each Mobile Robot has a set of ultrasonic sensors for excluding the collisions with obstacles, two sensors for identifying the gradient vector of the analyzed field, resources for wireless storage, processing and communication. The direction of the Mobile Robot movement is determined by the rotational speed of two DC motors which is calculated based on the models of Artificial Neural Networks. Gradient vectors generated by all Mobile Robots in the system structure are used to calculate the movement direction.
Dong, D., Ye, Z., Su, J., Xie, S., Cao, Y., Kochan, R..  2020.  A Malware Detection Method Based on Improved Fireworks Algorithm and Support Vector Machine. 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). :846–851.
The increasing of malwares has presented a serious threat to the security of computer systems in recent years. Traditional signature-based anti-virus systems are not able to detect metamorphic and previously unseen malwares and it inspires people to use machine learning methods such as Naive Bayes and Decision Tree to identity malicious executables. Among these methods, detecting malwares by using Support Vector Machine (SVM) is one of the most effective approaches. However, the parameters of SVM have serious impacts on its classification performance. In order to find the optimal parameter combination and avoid the problem of falling into local optimal solution, many methods based on evolutionary algorithms are proposed, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) and others. But these algorithms still face the problem of being trapped into local solution spaces in different degree. In this paper, an improved fireworks algorithm is presented and applied to search parameters of SVM: penalty factor c and kernel function parameter g. To research the performance of the proposed algorithm, numeric experiments are made and compared with some typical algorithms, the experimental results demonstrate it outperforms other algorithms.
2020-06-08
Khan, Saif Ali, Aggarwal, R. K, Kulkarni, Shashidhar.  2019.  Enhanced Homomorphic Encryption Scheme with PSO for Encryption of Cloud Data. 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS). :395–400.
Cloud computing can be described as a distributed design that is accessible to different forms of security intrusions. An encoding technique named homomorphic encoding is used for the encoding of entities which are utilized for the accession of data from cloud server. The main problems of homomorphic encoding scheme are key organization and key allocation. Because of these issues, effectiveness of homomorphic encryption approach decreases. The encoding procedure requires the generation of input, and for this, an approach named Particle swarm optimization is implemented in the presented research study. PSO algorithms are nature encouraged meta-heuristic algorithms. These algorithms are inhabitant reliant. In these algorithms, societal activities of birds and fishes are utilized as an encouragement for the development of a technical mechanism. Relying on the superiority of computations, the results are modified with the help of algorithms which are taken from arbitrarily allocated pattern of particles. With the movement of particles around the searching area, the spontaneity is performed by utilizing a pattern of arithmetical terminology. For the generation of permanent number key for encoding, optimized PSO approach is utilized. MATLAB program is used for the implementation of PSO relied homomorphic algorithm. The investigating outcomes depicts that this technique proves very beneficial on the requisites of resource exploitation and finishing time. PSO relied homomorphic algorithm is more applicable in terms of completion time and resource utilization in comparison with homomorphic algorithm.
2020-06-01
Lili, Yu, Lei, Zhang, Jing, Li, Fanbo, Meng.  2018.  A PSO clustering based RFID middleware. 2018 4th International Conference on Control, Automation and Robotics (ICCAR). :222–225.
In current, RFID (Radio Frequency Identification) Middleware is widely used in nearly all RFID applications, and provides service for raw data capturing, security data reading/writing as well as sensors controlling. However, as the existing Middlewares were organized with rigorous data comparison and data encryption, it is seriously affecting the usefulness and execution efficiency. Thus, in order to improve the utilization rate of effective data, increase the reading/writing speed as well as preserving the security of RFID, this paper proposed a PSO (Particle swarm optimization) clustering scheme to accelerate the procedure of data operation. Then with the help of PSO cluster, the RFID Middleware can provide better service for both data security and data availability. At last, a comparative experiment is proposed, which is used to further verify the advantage of our proposed scheme.
2020-05-04
Jie, Bao, Liu, Jingju, Wang, Yongjie, Zhou, Xuan.  2019.  Digital Ant Mechanism and Its Application in Network Security. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :710–714.
Digital ant technology is a new distributed and self-organization cyberspace defense paradigm. This paper describes digital ants system's developing process, characteristics, system architecture and mechanisms to illustrate its superiority, searches the possible applications of digital ants system. The summary of the paper and the trends of digital ants system are pointed out.
2020-04-24
Gao, Boyo, Shi, Libao, Ni, Yixin.  2019.  A dynamic defense-attack game scheme with incomplete information for vulnerability analysis in a cyber-physical power infrastructure. 8th Renewable Power Generation Conference (RPG 2019). :1—8.
The modern power system is experiencing rapid development towards a smarter cyber-physical power grid. How to comprehensively and effectively identify the vulnerable components under various cyber attacks has attracted widespread interest and attention around the world. In this paper, a game-theoretical scheme is developed to analyze the vulnerabilities of transmission lines and cyber elements under locally coordinated cyber-physical attacks in a cyber-physical power infrastructure. A two-step scenario including resources allocation made by system defender in advance and subsequent coordinated cyber-physical attacks are designed elaborately. The designed scenario is modeled as a game of incomplete information, which is then converted into a bi-level mathematical programming problem. In the lower level model, the attacker aims at maximizing system losses by attacking some transmission lines. While in the higher level model, the defender allocates defensive resources, trying to maximally reduce the losses considering the possible attacks. The payoffs of the game are calculated by leveraging a strategy of searching accident chains caused by cascading failure analyzed in this paper. A particle swarm optimization algorithm is applied to solve the proposed nonlinear bi-level programming model, and the case studies on a 34-bus system are conducted to verify the effectiveness of the proposed scheme.
Jiang, He, Wang, Zhenhua, He, Haibo.  2019.  An Evolutionary Computation Approach for Smart Grid Cascading Failure Vulnerability Analysis. 2019 IEEE Symposium Series on Computational Intelligence (SSCI). :332—338.
The cyber-physical security of smart grid is of great importance since it directly concerns the normal operating of a system. Recently, researchers found that organized sequential attacks can incur large-scale cascading failure to the smart grid. In this paper, we focus on the line-switching sequential attack, where the attacker aims to trip transmission lines in a designed order to cause significant system failures. Our objective is to identify the critical line-switching attack sequence, which can be instructional for the protection of smart grid. For this purpose, we develop an evolutionary computation based vulnerability analysis framework, which employs particle swarm optimization to search the critical attack sequence. Simulation studies on two benchmark systems, i.e., IEEE 24 bus reliability test system and Washington 30 bus dynamic test system, are implemented to evaluate the performance of our proposed method. Simulation results show that our method can yield a better performance comparing with the reinforcement learning based approach proposed in other prior work.
2020-01-27
Qureshi, Ayyaz-Ul-Haq, Larijani, Hadi, Javed, Abbas, Mtetwa, Nhamoinesu, Ahmad, Jawad.  2019.  Intrusion Detection Using Swarm Intelligence. 2019 UK/ China Emerging Technologies (UCET). :1–5.
Recent advances in networking and communication technologies have enabled Internet-of-Things (IoT) devices to communicate more frequently and faster. An IoT device typically transmits data over the Internet which is an insecure channel. Cyber attacks such as denial-of-service (DoS), man-in-middle, and SQL injection are considered as big threats to IoT devices. In this paper, an anomaly-based intrusion detection scheme is proposed that can protect sensitive information and detect novel cyber-attacks. The Artificial Bee Colony (ABC) algorithm is used to train the Random Neural Network (RNN) based system (RNN-ABC). The proposed scheme is trained on NSL-KDD Train+ and tested for unseen data. The experimental results suggest that swarm intelligence and RNN successfully classify novel attacks with an accuracy of 91.65%. Additionally, the performance of the proposed scheme is also compared with a hybrid multilayer perceptron (MLP) based intrusion detection system using sensitivity, mean of mean squared error (MMSE), the standard deviation of MSE (SDMSE), best mean squared error (BMSE) and worst mean squared error (WMSE) parameters. All experimental tests confirm the robustness and high accuracy of the proposed scheme.
Kala, T. Sree, Christy, A..  2019.  An Intrusion Detection System using Opposition based Particle Swarm Optimization Algorithm and PNN. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). :184–188.
Network security became a viral topic nowadays, Anomaly-based Intrusion Detection Systems [1] (IDSs) plays an indispensable role in identifying the attacks from networks and the detection rate and accuracy are said to be high. The proposed work explore this topic and solve this issue by the IDS model developed using Artificial Neural Network (ANN). This model uses Feed - Forward Neural Net algorithms and Probabilistic Neural Network and oppositional based on Particle Swarm optimization Algorithm for lessen the computational overhead and boost the performance level. The whole computing overhead produced in its execution and training are get minimized by the various optimization techniques used in these developed ANN-based IDS system. The experimental study on the developed system tested using the standard NSL-KDD dataset performs well, while compare with other intrusion detection models, built using NN, RB and OPSO algorithms.
Fuchs, Caro, Spolaor, Simone, Nobile, Marco S., Kaymak, Uzay.  2019.  A Swarm Intelligence Approach to Avoid Local Optima in Fuzzy C-Means Clustering. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–6.
Clustering analysis is an important computational task that has applications in many domains. One of the most popular algorithms to solve the clustering problem is fuzzy c-means, which exploits notions from fuzzy logic to provide a smooth partitioning of the data into classes, allowing the possibility of multiple membership for each data sample. The fuzzy c-means algorithm is based on the optimization of a partitioning function, which minimizes inter-cluster similarity. This optimization problem is known to be NP-hard and it is generally tackled using a hill climbing method, a local optimizer that provides acceptable but sub-optimal solutions, since it is sensitive to initialization and tends to get stuck in local optima. In this work we propose an alternative approach based on the swarm intelligence global optimization method Fuzzy Self-Tuning Particle Swarm Optimization (FST-PSO). We solve the fuzzy clustering task by optimizing fuzzy c-means' partitioning function using FST-PSO. We show that this population-based metaheuristics is more effective than hill climbing, providing high quality solutions with the cost of an additional computational complexity. It is noteworthy that, since this particle swarm optimization algorithm is self-tuning, the user does not have to specify additional hyperparameters for the optimization process.
Kalaivani, S., Vikram, A., Gopinath, G..  2019.  An Effective Swarm Optimization Based Intrusion Detection Classifier System for Cloud Computing. 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS). :185–188.
Most of the swarm optimization techniques are inspired by the characteristics as well as behaviour of flock of birds whereas Artificial Bee Colony is based on the foraging characteristics of the bees. However, certain problems which are solved by ABC do not yield desired results in-terms of performance. ABC is a new devised swarm intelligence algorithm and predominately employed for optimization of numerical problems. The main reason for the success of ABC algorithm is that it consists of feature such as fathomable and flexibility when compared to other swarm optimization algorithms and there are many possible applications of ABC. Cloud computing has their limitation in their application and functionality. The cloud computing environment experiences several security issues such as Dos attack, replay attack, flooding attack. In this paper, an effective classifier is proposed based on Artificial Bee Colony for cloud computing. It is evident in the evaluation results that the proposed classifier achieved a higher accuracy rate.
2019-12-16
Lin, Jerry Chun-Wei, Zhang, Yuyu, Chen, Chun-Hao, Wu, Jimmy Ming-Tai, Chen, Chien-Ming, Hong, Tzung-Pei.  2018.  A Multiple Objective PSO-Based Approach for Data Sanitization. 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI). :148–151.
In this paper, a multi-objective particle swarm optimization (MOPSO)-based framework is presented to find the multiple solutions rather than a single one. The presented grid-based algorithm is used to assign the probability of the non-dominated solution for next iteration. Based on the designed algorithm, it is unnecessary to pre-define the weights of the side effects for evaluation but the non-dominated solutions can be discovered as an alternative way for data sanitization. Extensive experiments are carried on two datasets to show that the designed grid-based algorithm achieves good performance than the traditional single-objective evolution algorithms.
2019-09-09
Rathi, P. S., Rao, C. M..  2018.  An Enhanced Threshold Based Cryptography with Secrete Sharing and Particle Swarm Optimization for Data Sending in MANET. 2018 3rd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS). :87-91.

There are two types of network architectures are presents those are wired network and wireless network. MANETs is one of the examples of wireless network. Each and every network has their own features which make them different from other types of network. Some of the features of MANETs are; infrastructure less network, mobility, dynamic network topology which make it different and more popular from wired network but these features also generate different problems for achieving security due to the absence of centralized authority inside network as well as sending of data due to its mobility features. Achieving security in wired network is little-bit easy compare to MANETs because in wired network user need to just protect main centralized authority for achieving security whereas in MANETs there is no centralized authority available so protecting server in MANETs is difficult compare to wired network. Data sending and receiving process is also easy in wired network but mobility features makes this data sending and receiving process difficult in MANETs. Protecting server or central repository without making use of secrete sharing in wired network will create so many challenges and problem in terms of security. The proposed system makes use of Secrete sharing method to protect server from malicious nodes and `A New particle Swarm Optimization Method for MANETs' (NPSOM) for performing data sending and receiving operation in optimization way. NPSOM technique get equated with the steady particle swarm optimizer (PSO) technique. PSO was essentially designed by Kennedy, Eberhart in 1995. These methods are based upon 4 dissimilar types of parameters. These techniques were encouraged by common performance of animals, some of them are bird assembling and fish tuition, ant colony. The proposed system converts this PSO in the form of MANETs where Particle is nothing but the nodes in the network, Swarm means collection of multiple nodes and Optimization means finding the best and nearer root to reach to destination. Each and every element study about their own previous best solution which they are having with them for the given optimization problem, likewise they see for the groups previous best solution which they got for the same problem and finally they correct its solution depending on these values. This same process gets repeated for finding of the best and optimal solutions value. NPSOM technique, used in proposed system there every element changes its location according to the solution which they got previously and which is poorest as well as their collection's earlier poorest solution for finding best, optimal value. In this proposed system we are concentrating on, sidestepping element's and collections poorest solution which they got before.

2019-03-15
Wang, C., Zhao, S., Wang, X., Luo, M., Yang, M..  2018.  A Neural Network Trojan Detection Method Based on Particle Swarm Optimization. 2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT). :1-3.

Hardware Trojans (HTs) are malicious modifications of the original circuits intended to leak information or cause malfunction. Based on the Side Channel Analysis (SCA) technology, a set of hardware Trojan detection platform is designed for RTL circuits on the basis of HSPICE power consumption simulation. Principal Component Analysis (PCA) algorithm is used to reduce the dimension of power consumption data. An intelligent neural networks (NN) algorithm based on Particle Swarm Optimization (PSO) is introduced to achieve HTs recognition. Experimental results show that the detection accuracy of PSO NN method is much better than traditional BP NN method.