The great advancements in communication, cloud computing, and the internet of things (IoT) have opened critical challenges in security. With these developments, cyberattacks are also rapidly growing since the current ...
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The great advancements in communication, cloud computing, and the internet of things (IoT) have opened critical challenges in security. With these developments, cyberattacks are also rapidly growing since the current security mechanisms do not provide efficient solutions. Recently, various artificial intelligence (AI) based solutions have been proposed for different security applications, including intrusion detection. In this paper, we propose an efficient AI-based mechanism for intrusion detection systems (IDS) in IoT systems. We leverage the advancements of deep learnings and metaheuristics (MH) algorithms that approved their efficiency in solving complex engineering problems. We propose a feature extraction method using the convolutional neural networks (CNNs) to extract relevant features. Also, we develop a new feature selection method using a new variant of the transient search optimization (TSO) algorithm, called TSODE, using the operators of differential evolution (DE) algorithm. The proposed TSODE uses the DE to improve the process of balancing between exploitation and exploration phases. Furthermore, we use three public datasets, KDDCup-99, NSL-KDD, BoT-IoT, and CICIDS-2017 to assess the performance of the developed method, which achieved higher accuracy compared to several existing approaches.
How to simultaneously locate multiple global peaks and achieve certain accuracy on the found peaks are two key challenges in solving multimodal optimization problems (MMOPs). In this article, a landscape-aware differe...
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We are now in the first phase of the Fifth Generation (5G) network, and its full potential is still a long way to reach. Network operators and manufacturers are preparing to release new 5G end-users services and produ...
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We are now in the first phase of the Fifth Generation (5G) network, and its full potential is still a long way to reach. Network operators and manufacturers are preparing to release new 5G end-users services and products. Each service must be performant to meet the need of users. Network Slicing (NS) is now one of the most popular 5G technologies in the research community. For network softwarization, Network Slicing affords flexibility and scalability with embedded Quality of Experience (QoE) and Quality of Services (QoS) features. In this paper, we explore the latest developments in related research fields. We summarize the 3GPP network slicing evolution, the Radio Access Network, and Core Network architectures. We describe background concepts correlated with inter-slice and intra-slice, as well as their management and orchestration architectures. We extend our discussion to the taxonomy of NGWN resource allocation via a deep comparison of the optimization algorithms. Finally, we enrich our study by including open issues and some recommendations for the future.
The application of drones provides a powerful solution for "the last-mile" logistics services, while the large-scale implementation of logistics drone services will threaten the safety of buildings, pedestri...
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The application of drones provides a powerful solution for "the last-mile" logistics services, while the large-scale implementation of logistics drone services will threaten the safety of buildings, pedestrians, vehicles, and other elements in the urban environment. The balance of risk cost and service benefit is accordingly crucial to managing logistics drones. In this study, we proposed a cost-benefit assessment model for quantifying risk cost and service benefit in the urban environment. In addition, a global heuristic path search rule was developed to solve the path planning problem based on risk mitigation and customer service. The cost-benefit assessment model quantifies the risk cost from three environmental elements (buildings, pedestrians, and vehicles) threatened by drone operations based on the collision probability, and the service benefit based on the characteristics of logistics service customers. To explore the effectiveness of the model in this paper, we simulate and analyse the effects of different risk combinations, unknown risk zones, and risk-benefit preferences on the path planning results. The results show that compared with the traditional shortest-distance method, the drone path planning method proposed in this paper can accurately capture the distribution of risks and customers in the urban environment. It is highly reusable in ensuring service benefits while reducing risk costs and generating a cost-effective path for logistics drones. We also compare the algorithm in this paper with the A* algorithm and verify that our algorithm improves the solution quality in complex environments.
There are many meta-heuristic algorithms inspired by nature for solving optimization problems. One of these algorithms is the Gorilla Troop optimization (GTO) algorithm, which has been recently proposed for solving co...
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The paper presents multi-objective optimization technique for the exploration of unknown space. Exploration refers to the building of an estimated finite map of the environment using sensor data. Conventionally, in ro...
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The paper presents multi-objective optimization technique for the exploration of unknown space. Exploration refers to the building of an estimated finite map of the environment using sensor data. Conventionally, in robotics, the optimization is performed utilizing a single optimization technique with a particular objective function. This although simplifies the process but greatly compromise the map accuracy and exploration depth. Realizing this aspect, we present a new exploration technique with augmented objective functions, so as maximize the search area and map accuracy of the exploration space. The proposed framework termed as Multi-Objective Whale optimization Algorithm (MO-WOA), is based on bio-inspired Whale Optimizer. It starts with the initialization of the whale's population, which are referred to as way-points. These way-points are assumed to be constant once they are set in the initial stage/iterations. The next step involves the position update from the non-dominated way-points catered by the robots. The algorithm utilizing this optimization approach formulates the optimal way-points. The performance metrics are presented through extensive simulations. The results efficacy is then demonstrated by comparing the results of the proposed algorithm with those achieved from contemporary techniques namely Coordinated Multi-Robot Exploration (CME), conventional Whale Optimizer (WO) integrated with CME and Arithmetic optimization (AO). Results demonstrate that the proposed algorithm significantly improved the exploration parameters by enhancing the explored area and reducing the search time. (c) 2022 Elsevier B.V. All rights reserved.
In recent years, many intelligent optimization algorithms have been applied to the class integration and test order (CITO) problem. These algorithms also have been proved to be able to efficiently solve the problem. H...
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In recent years, many intelligent optimization algorithms have been applied to the class integration and test order (CITO) problem. These algorithms also have been proved to be able to efficiently solve the problem. Here, the design of fitness function is a key task to generate the optimal solution. To better solve the class integration and test order problem, we propose a new fitness function to generate the optimal solution that achieves a balanced compromise between the different measures (objectives) such as the total number of stubs and the total stubbing complexity in this paper. We used some programs to compare and evaluate the different approaches. The experimental results show that our proposed approach is encouraging to some extent in solving the class integration and test order problem.
We study approximation algorithms for the forest cover and bounded forest cover problems. A probabilistic 2+ϵ approximation algorithm for the forest cover problem is given using the method of dual fitting. A determini...
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The breadth-first search (BFS) algorithm is a fundamental algorithm in graph theory, and it’s parallelization can significantly improve performance. Therefore, there have been numerous efforts to leverage the powerfu...
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The Maximum Satisfiability Problem (MAX-SAT) is a crucial NP-hard optimization problem with applications in artificial intelligence, circuit design, scheduling, and combinatorial optimization. In this work, we provide...
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