جستجوی مقالات مرتبط با کلیدواژه "cuckoo algorithm" در نشریات گروه "فناوری اطلاعات"

The Internet of Things (IoT) refers to the connection of various devices to each other via the internet. Conceptually, the IoT can be defined as a dynamic, self-configuring network infrastructure based on standards and participatory communication protocols. The main goal of the IoT is to lead towards a better and safer community. However, one of the fundamental challenges in developing the IoT is the issue of security, and intrusion detection systems are one of the main methods to create security in the IoT. On the other hand, Convolutional Neural Network (CNN), with its specific features, is one of the best methods for analyzing network data. This network is a type of deep neural network composed of multiple layers that can ultimately reduce the dimensions of features. Additionally, the cuckoo algorithm has parameters required for configuration in the initial search, which are very few and can naturally and efficiently cope with multi-state problems. In this paper, a new method for intrusion detection in the IoT using CNN and feature selection by the cuckoo algorithm is presented. Simulation results indicate the satisfactory performance of the proposed method.

The Internet of Things (IoT) refers to the connection of various devices to each other via the internet. Conceptually, the IoT can be defined as a dynamic, self-configuring network infrastructure based on standards and participatory communication protocols. The main goal of the IoT is to lead towards a better and safer community. However, one of the fundamental challenges in developing the IoT is the issue of security, and intrusion detection systems are one of the main methods to create security in the IoT. On the other hand, Convolutional Neural Network (CNN), with its specific features, is one of the best methods for analyzing network data. This network is a type of deep neural network composed of multiple layers that can ultimately reduce the dimensions of features. Additionally, the cuckoo algorithm has parameters required for configuration in the initial search, which are very few and can naturally and efficiently cope with multi-state problems. In this paper, a new method for intrusion detection in the IoT using CNN and feature selection by the cuckoo algorithm is presented. Simulation results indicate the satisfactory performance of the proposed method.

Recommending systems are systems that, by taking limited information from the user and features such as what the user has searched for in the past and what product they have rated, can correctly identify the user and the desired items Offer the user. The user's desired items are suggested to him through the user profile. In this research, a new method is presented to recommend the user's interests in the form of the user's personalized profile. The way to do this is to use other users' searched information in the form of a database to recommend to new users. The procedure is that we first collect a log file from the items searched by users, then we pre-process this log file to remove the data from the raw state and clean it. Then, using data weighting and using the score function, we extract the most searched items of users in the past and provide them to the user in the form of a recommendation system based on participatory filtering. Finally, we use our data using an algorithm. We optimize the cuckoo that this information can be of interest to the user. The results of this study showed 99% accuracy and 97% frequency, which can to a large extent correctly predict the user's favorite items and pages and start with the problem that is the problem of most recommender systems To confront.

Software-Defined networks (SDNs) are a new generation of computer networks that have eliminated many of the problems of traditional networks. These networks use a three-tier architecture in which the physical layers, controller, and management are located at different levels. This new architecture has made the network very dynamic, and many of the previous problems in the network have been solved. As the size of the network increases, using a controller across the network will cause issues such as increasing the average latency between the switches and the controller, as well as forming a bottleneck in the controller. For this reason, it is recommended to use multiple physical controllers on the control plane. Due to the cost of purchasing and maintaining the controller, it is necessary to solve the mentioned problem with the least controllers. The question is, to achieve a goal such as reducing latency to an acceptable threshold, at least how many controllers are needed, where the controllers should be located, and which switches should be monitored by which controller? Since this is an NP-Hard problem, methods based on meta-heuristic algorithms can be effective in solving it. In this article, we have solved the problem of controller placement in software-based networks to reduce latency using the cuckoo meta-heuristic algorithm. The simulation results show that the efficiency of our proposed method is between 16 to 70 percent better than the method proposed by the PSO algorithm.

Due to the growing number of videos available on the web, it seems necessary to have a system that can extract users' favorite videos from a huge amount of information that is increasing day by day. One of the best ways to do this is to use referral systems. In this research, a method is provided to improve the recommender systems in the field of film recommendation to the user. In this research, DBSCAN clustering algorithm is used for data clustering. Then we will optimize our data using the cuckoo algorithm, then the genetic algorithm is used to predict the data, and finally, using a recommender system based on participatory refinement, a list of different movies that can be of interest to the user is provided. The results of evaluating the proposed method indicate that this recommender system obtained a score of 99% in the accuracy of the system and a score of 95% in the call section Suggest the user's favorite videos correctly to the user.