Biblio

This paper is a conceptual paper that explores how the sensemaking process by intelligence analysts completed within a cognitive immersive environment might be impacted by the inclusion of a progressive dialog system. The tools enabled in the sensemaking room (a specific instance within the cognitive immersive environment) were informed by tools from the intelligence analysis domain. We explore how a progressive dialog system would impact the use of tools such as the collaborative brainstorming exercise [1]. These structured analytic techniques are well established in intelligence analysis training literature, and act as ways to access the intended users' cognitive schema as they use the cognitive immersive room and move through the sensemaking process. A prior user study determined that the sensemaking room encouraged users to be more concise and representative with information while using the digital brainstorming tool. We anticipate that addition of the progressive dialog function will enable a more cohesive link between information foraging and sensemaking behaviors for analysts.

As a core problem of multi-agent systems, multiagent pathfinding has an important impact on the efficiency of multi-agent systems. Because of this, many novel multi-agent pathfinding methods have been proposed over the years. However, these methods have focused on different agents with different goals for research, and less research has been done on scenarios where different agents have the same goal. We propose a multiagent pathfinding method incorporating a multi-objective gray wolf optimization algorithm to solve the multi-agent pathfinding problem with the same objective. First, constrained optimization modeling is performed to obtain objective functions about agent wholeness and security. Then, the multi-objective gray wolf optimization algorithm is improved for solving the constrained optimization problem and further optimized for scenarios with insufficient computational resources. To verify the effectiveness of the multi-objective gray wolf optimization algorithm, we conduct experiments in a series of simulation environments and compare the improved multi-objective grey wolf optimization algorithm with some classical swarm intelligence optimization algorithms. The results show that the multi-agent pathfinding method incorporating the multi-objective gray wolf optimization algorithm is more efficient in handling multi-agent pathfinding problems with the same objective.

The Open Web Application Security Project (OWASP) (a non-profit foundation that works to improve computer security) considered, in 2021, injection as one of the biggest risks in web applications. SQL injection despite being a vulnerability easily avoided has a great insurgency in web applications, and its impact is quite nefarious. To identify and exploit vulnerabilities in a system, algorithms based on Swarm Intelligence (SI) can be used. This article proposes and describes a new approach that uses SI and attack vectors to identify Structured Query Language (SQL) Injection vulnerabilities. The results obtained show the efficiency of the proposed approach.

This paper proposes an improved version of the newly developed Honey Badger Algorithm (HBA), called Generalized opposition Based-Learning HBA (GOBL-HBA), for solving the mesh routers placement problem. The proposed GOBLHBA is based on the integration of the generalized opposition-based learning strategy into the original HBA. GOBL-HBA is validated in terms of three performance metrics such as user coverage, network connectivity, and fitness value. The evaluation is done using various scenarios with different number of mesh clients, number of mesh routers, and coverage radius values. The simulation results revealed the efficiency of GOBL-HBA when compared with the classical HBA, Genetic Algorithm (GA), and Particle Swarm optimization (PSO).

In the Swarm Intelligence domain, the firefly algorithm(s) is the most significant algorithm applied in most all optimization areas. FA and variants are easily understood and implemented. FA is capable of solving different domain problems. For solving diverse range of engineering problems requires modified FA or Hybrid FA algorithms, but it is possible additional scope of improvement. In recent times swarm intelligence based intelligent optimization algorithms have been used for Research purposes. FA is one of most important intelligence Swarm algorithm that can be applied for the problems of Global optimization. FA algorithm is capable of achieving best results for complicated issues. In this research study we have discussed and different characteristics of FA and presented brief Review of FA. Along with other metahauristic algorithm we have discussed FA algorithm’s different variant like multi objective, and hybrid. The applications of firefly algorithm are bestowed. The aim of the paper is to give future direction for research in FA.

This paper aims to address the problem of Deepfake Detection using a Two-Stream Capsule Network. First we review methods used to create Deepfake content, as well as methods proposed in the literature to detect such Deepfake content. We then propose a novel architecture to detect Deepfakes, which consists of a two-stream Capsule network running in parallel that takes in both RGB images/frames as well as Error Level Analysis images. Results show that the proposed approach exhibits the detection accuracy of 73.39 % and 57.45 % for the Deepfake Detection Challenge (DFDC) and the Celeb-DF datasets respectively. These results are, however, from a preliminary implementation of the proposed approach. As part of future work, population-based optimization techniques such as Particle Swarm Optimization (PSO) will be used to tune the hyper parameters for better performance.

With the continuous emergence of cyber attacks, the security of industrial control system (ICS) has become a hot issue in academia and industry. Intrusion detection technology plays an irreplaceable role in protecting industrial system from attacks. However, the imbalance between normal samples and attack samples seriously affects the performance of intrusion detection algorithms. This paper proposes SE-IDS, which uses generative adversarial networks (GAN) to expand the minority to make the number of normal samples and attack samples relatively balanced, adopts particle swarm optimization (PSO) to optimize the parameters of LightGBM. Finally, we evaluated the performance of the proposed model on the industrial network dataset.

In Particle Swarm Optimization Algorithm (PSO), the learning factors \$c\_1\$ and \$c\_2\$ are used to update the speed and location of a particle. However, the setting of those two important parameters has great effect on the performance of the PSO algorithm, which has limited its range of applications. To avoid the tedious parameter tuning, we introduce a transfer learning based adaptive parameter setting strategy to PSO in this paper. The proposed transfer learning strategy can adjust the two learning factors more effectively according to the environment change. The performance of the proposed algorithm is tested on sets of widely-used benchmark multi-objective test problems for DTLZ. The results comparing and analysis are conduced by comparing it with the state-of-art evolutionary multi-objective optimization algorithm NSGA-III to verify the effectiveness and efficiency of the proposed method.

Domain Name System (DNS) is the Internet's system for converting alphabetic names into numeric IP addresses. It is one of the early and vulnerable network protocols, which has several security loopholes that have been exploited repeatedly over the years. The clustering task for the automatic recognition of these attacks uses machine learning approaches based on semi-supervised learning. A family of bio-inspired algorithms, well known as Swarm Intelligence (SI) methods, have recently emerged to meet the requirements for the clustering task and have been successfully applied to various real-world clustering problems. In this paper, Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Kmeans, which is one of the most popular cluster algorithms, have been applied. Furthermore, hybrid algorithms consisting of Kmeans and PSO, and Kmeans and ABC have been proposed for the clustering process. The Canadian Institute for Cybersecurity (CIC) data set has been used for this investigation. In addition, different measures of clustering performance have been used to compare the different algorithms.

Aiming at the problems of low accuracy and poor effect caused by the lack of data labels in most real network traffic, an optimized density peak clustering based on the improved salp swarm algorithm is proposed for traffic anomaly detection. Through the optimization of cosine decline and chaos strategy, the salp swarm algorithm not only accelerates the convergence speed, but also enhances the search ability. Moreover, we use the improved salp swarm algorithm to adaptively search the best truncation distance of density peak clustering, which avoids the subjectivity and uncertainty of manually selecting the parameters. The experimental results based on NSL-KDD dataset show that the improved salp swarm algorithm achieves faster convergence speed and higher precision, increases the average anomaly detection accuracy of 4.74% and detection rate of 6.14%, and reduces the average false positive rate of 7.38%.

Support Vector Machine (SVM) is a relatively novel classification technology, which has shown higher performance than traditional learning methods in many applications. Therefore, some security researchers have proposed an intrusion detection method based on SVM. However, the SVM algorithm is very sensitive to the choice of kernel function and parameter adjustment. Once the parameter selection is unscientific, it will lead to poor classification accuracy. To solve this problem, this paper presents a Grey Wolf Optimizer Algorithm based on Particle Swarm Optimization (PSOGWO) algorithm to improve the Intrusion Detection System (IDS) based on SVM. This method uses PSOGWO algorithm to optimize the parameters of SVM to improve the overall performance of intrusion detection based on SVM. The "optimal detection model" of SVM classifier is determined by the fusion of PSOGWO algorithm and SVM. The comparison experiments based on NSL-KDD dataset show that the intrusion detection method based on PSOGWO-SVM achieves the optimization of the parameters of SVM, and has improved significantly in terms of detection rate, convergence speed and model balance. This shows that the method has better performance for network intrusion detection.

Web caching is one strategy that can be used to speed up response times by storing frequently accessed data in the cache server. Given the cache server limited capacity, it is necessary to determine the priority of cached data that can enter the cache server. This study simulated cached data prioritization based on an objective function as a characteristic of problem-solving using an optimization approach. The objective function of web caching is formulated based on the variable data size, count access, and frequency-time access. Then we use the knapsack problem method to find the optimal solution. The Simulations run three swarm intelligence algorithms Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Binary Particle Swarm Optimization (BPSO), divided into several scenarios. The simulation results show that the GA algorithm relatively stable and fast to convergence. The ACO algorithm has the advantage of a non-random initial solution but has followed the pheromone trail. The BPSO algorithm is the fastest, but the resulting solution quality is not as good as ACO and GA.

Web caching is a strategy that can be used to speed up website access on the client-side. This strategy is implemented by storing as many popular web objects as possible on the cache server. All web objects stored on a cache server are called cached data. Requests for cached web data on the cache server are much faster than requests directly to the origin server. Not all web objects can fit on the cache server due to their limited capacity. Therefore, optimizing cached data in a web caching strategy will determine which web objects can enter the cache server to have maximum profit. This paper simulates a web caching strategy optimization with a knapsack problem approach using the Ant Colony optimization (ACO), Genetic Algorithm (GA), and a combination of the two. Knapsack profit is seen from the number of web objects that can be entered into the cache server but with the minimum objective function value. The simulation results show that the combination of ACO and GA is faster to produce an optimal solution and is not easily trapped by the local optimum.

Recently, cyber security in power systems has captured significant interest. This is because the world has seen a surge in cyber attacks on power systems. One of the prolific cyber attacks in modern power systems are False Data Injection Attacks (FDIA). In this paper, we analyzed the impact of FDIA on the operation cost of power systems. Also, we introduced a novel Advanced Persistent Threat (APT) based attack strategy that maximizes the operating costs when attacking specific nodes in the system. We model the attack strategy using an optimization problem and use metaheuristics algorithms to solve the optimization problem and execute the attack. We have found that our attacks can increase the power generation cost by up to 15.6%, 60.12%, and 74.02% on the IEEE 6-Bus systems, 30-Bus systems, and 118-Bus systems, respectively, as compared to normal operation.

The reform of electricity marketization has bred multiple market agents. In order to maximize the total social benefits on the premise of ensuring the security of the system and taking into account the interests of multiple market agents, a bi-level optimal allocation model of distribution network with multiple agents participating is proposed. The upper level model considers the economic benefits of energy and service providers, which are mainly distributed power investors, energy storage operators and distribution companies. The lower level model considers end-user side economy and actively responds to demand management to ensure the highest user satisfaction. The K-means multi scenario analysis method is used to describe the time series characteristics of wind power, photovoltaic power and load. The particle swarm optimization (PSO) algorithm is used to solve the bi-level model, and IEEE33 node system is used to verify that the model can effectively consider the interests of multiple agents while ensuring the security of the system.

The deployment of datasets in the heterogeneous edge-cloud computing paradigm has received increasing attention in state-of-the-art research. However, due to their large sizes and the existence of private scientific datasets, finding an optimal data placement strategy that can minimize data transmission as well as improve performance, remains a persistent problem. In this study, the advantages of both edge and cloud computing are combined to construct a data placement model that works for multiple scientific workflows. Apparently, the most difficult research challenge is to provide a data placement strategy to consider shared datasets, both within individual and among multiple workflows, across various geographically distributed environments. According to the constructed model, not only the storage capacity of edge micro-datacenters, but also the data transfer between multiple clouds across regions must be considered. To address this issue, we considered the characteristics of this model and identified the factors that are causing the transmission delay. The authors propose using a discrete particle swarm optimization algorithm with differential evolution (DE-DPSO) to distribute dataset during workflow execution. Based on this, a new data placement strategy named DE-DPSO-DPS is proposed. DE-DPSO-DPS is evaluated using several experiments designed in simulated heterogeneous edge-cloud computing environments. The results demonstrate that our data placement strategy can effectively reduce the data transmission time and achieve superior performance as compared to traditional strategies for data-sharing scientific workflows.

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.

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.

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.

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.

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.

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.

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.

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.

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.