Computer Networks and Distributed Systems
Behzad Seif; mohammad goodarzi
Volume 7, Issue 2 , May 2021, Pages 93-102
Abstract
Today, the use of wireless sensor networks has become very popular in many applications. Due to the connection in wireless sensor networks, it is done wirelessly, so they are naturally insecure and prone to various types of attacks. In the past, various solutions were offered in this regard, each of ...
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Today, the use of wireless sensor networks has become very popular in many applications. Due to the connection in wireless sensor networks, it is done wirelessly, so they are naturally insecure and prone to various types of attacks. In the past, various solutions were offered in this regard, each of which had its problems. Therefore, in this proposed solution, an attempt was made to solve these problems. The proposed solution for securing sensor nodes uses authentication based on the ZKP protocol, which has been improved with Interlock, and game theory has also been used to more quickly identify intrusive nodes. One of the most important benefits of the proposed solution is to prevent attacks such as sleep deprivation. The proposed algorithm is able to detect quickly and is able to prevent network damage in the fastest possible time. The proposed solution was implemented and reviewed in MATLAB environment and the studies showed a very good performance of the proposed method.
Software Engineering and Information Systems
Amir Abbas Farahmand; Reza Radfar; Alireza Poorebrahimi; Mani Sharifi
Volume 7, Issue 2 , May 2021, Pages 103-126
Abstract
IoT, a state-of-the-art technology, faces many challenges in its growth and development. One of the main concerns is the potential threats posed by the spread of such technology in the world. The widespread adoption and spread of such a technology can threaten us much more seriously than the Internet ...
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IoT, a state-of-the-art technology, faces many challenges in its growth and development. One of the main concerns is the potential threats posed by the spread of such technology in the world. The widespread adoption and spread of such a technology can threaten us much more seriously than the Internet currently available. The challenges we face in adopting such technology will include both the social and the technical aspects. Technical limitations include security considerations, privacy, as well as the resource, energy, and capacity issues for such a large amount of data and processing. Besides, socially, cultural infrastructure must first be provided for the diffusion of such technologies among the community. This study aimed to investigate the factors affecting the readiness level of the acceptance of IoT technologies. The relationships are examined as six main categories identified, namely the social aspect, the cultural aspect, the human aspect, the technological aspect, the financial aspect, the management aspect, government laws, and regulations. The opinions of senior ICT executives nationwide were collected. The statistical population of this study consists of experts and users of the financial sector, stock exchange, and financial institutions. Since the statistical population is infinite, 384 randomly available individuals are selected. SMART.PLS was used to validate the model and test the relationships between variables. The results indicate the impact of the identified categories on IoT adoption readiness.
Pattern Analysis and Intelligent Systems
Abdulbaghi Ghaderzadeh; sahar Hosseinpanahi; Sarkhel Taher kareem
Volume 7, Issue 2 , May 2021, Pages 115-125
Abstract
Nowadays, spam is a major challenge regarding emails. Spam is a specific type of email that is sent to the network for malicious purposes. Spam plays an important role in stealing information and can include fake links to trick users. Machine learning and data mining techniques such as artificial neural ...
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Nowadays, spam is a major challenge regarding emails. Spam is a specific type of email that is sent to the network for malicious purposes. Spam plays an important role in stealing information and can include fake links to trick users. Machine learning and data mining techniques such as artificial neural networks are the most applicable methods to detect spam. The multi-layer artificial neural network needs to select the most important features as inputs to reduce the output error for accurate spam detection. In the proposed method, a smart method based on swarm intelligence algorithms is used for feature selection. In this study, a binary version of Emperor Penguin Optimizer (EPO) is used to select more appropriate features. The proposed method uses the selected features for learning and classification in the spam detection process. Experiments in the MATLAB environment on the Spambase dataset show that with the increase in population the error in spam detection in Emails will decrease about 14.61% and with the increase in feature space, it will decrease about 43.85% in the best situation. Experiments show that the proposed method has less error in detecting spam compare to other methods, multilayer artificial neural network, recursive neural network, support vector machine, Bayesian network, and whale optimization algorithm. Experiments show that the error of spam detection in the proposed approach is about 23.57% less than the whale optimization algorithm. Empirical results, obtained through simulations on the Spambase dataset, show our approach outperforms the other existing methods on precision value.
Computer Networks and Distributed Systems
Fatemeh Davami; Sahar Adabi; Ali Rezaee; Amir Masoud Rahamni
Volume 7, Issue 2 , May 2021, Pages 126-136
Abstract
In the last ten years, the Cloud data centers have been manifested as the crucial computing architectures to enable extreme data workflows. Due to the complicatedness and diverse kinds of computational resources like Fog nodes and Cloud servers, workflow scheduling has been proposed to be the main challenge ...
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In the last ten years, the Cloud data centers have been manifested as the crucial computing architectures to enable extreme data workflows. Due to the complicatedness and diverse kinds of computational resources like Fog nodes and Cloud servers, workflow scheduling has been proposed to be the main challenge in Cloud and or Fog computing environments. For resolving this issue, the present study offers a scheduling algorithm according to the critical path extraction, referred to as the Critical Path Extraction Algorithm (CPEA). In fact, it is one of the new multi-criteria decision-making algorithms to extract the critical paths of multiple workflows because it is of high importance to find the critical path in the creation and control of the scheduling. Moreover, an extensive software simulation investigation has been performed to compare this new algorithm in the real work-loads and recent algorithm. We compare our algorithm with the GRP-HEFT algorithm. The experimental results confirm the proposed algorithm's superiority in fulfilling make-span and waiting time and that workflow scheduling based on CPEA further improves the workflow make-span and waiting time.
Pattern Analysis and Intelligent Systems
reza molaee fard
Volume 7, Issue 2 , May 2021, Pages 137-146
Abstract
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 ...
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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.
Pattern Analysis and Intelligent Systems
Razieh Asgarnezhad; Karrar Ali Mohsin Alhameedawi
Volume 7, Issue 2 , May 2021, Pages 147-156
Abstract
Due to the importance of automatic identification of brain conditions, many researchers concentrate on Epilepsy disorder to aim to the detecting of eye states and classification systems. Eye state recognition has a vital role in biomedical informatics such as controlling smart home devices, driving detection, ...
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Due to the importance of automatic identification of brain conditions, many researchers concentrate on Epilepsy disorder to aim to the detecting of eye states and classification systems. Eye state recognition has a vital role in biomedical informatics such as controlling smart home devices, driving detection, etc. This issue is known as electroencephalogram signals. There are many works in this context in which traditional techniques and manually extracted features are used. The extraction of effective features and the selection of proper classifiers are challenging issues. In this study, a classification system named PEML-E was proposed in which a different pre-processing stage is used. The ensemble methods in the classification stage are compared to the base classifiers and the most important works in this context. To evaluate, a freely available public EEG eye state dataset from UCI is applied. The highest accuracy, precision, recall, F1, specificity, and sensitivity are obtained 95.88, 95.39, 96.25, 96.18, 96.25, and 95.44%, respectively.