A growing range of data sets have been created in recent years; these are used by platforms and software applications and kept in remote access repositories. Datasets are therefore more susceptible to harmful attacks....
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ISBN:
(数字)9798350365856
ISBN:
(纸本)9798350365863
A growing range of data sets have been created in recent years; these are used by platforms and software applications and kept in remote access repositories. Datasets are therefore more susceptible to harmful attacks. As a result, network security in data transmission is becoming a more important area of study. One well-known method for safeguarding computer systems is the deployment of intrusion detection systems. This study proposes an artificial intelligence based method for data analysis-based anomaly detection. Methods based on machine learning and rules are mixed together. The right rules are created via a genetic algorithm. Relevant features are extracted using principal component analysis with the goal of enhancing performance. The KDD Cup 1999 dataset is used to empirically validate the suggested procedure, satisfying the criterion of using appropriate data. Using the well-known benchmark dataset, the suggested approach is used to identify and examine four different kinds of attacks: Neptune, Ipsweep, Pod, and Teardrop. During the machine learning phase, the data is categorized into categories of attacks and normal behavior after the features set during the training phase are tested. For the purpose of data analysis, the input data is divided into training and testing sets for an artificial neural network. The first 80% of the data are used to train the neural network, and the remaining 20% are used for testing. The estimated accuracy improves with the number of epochs and is higher for training data and lower for validation test data, according to experimental results. Consequently, the trained model can be retained and used to detect discrepancies. The learnt model is used to the system to compute new input parameters that are not used during training or validation.
Technology advancements have transformed medical science and practice, leading to the vast gathering of a wide range of medical data. Medical researchers use artificial intelligence techniques extensively because they...
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ISBN:
(数字)9798350349832
ISBN:
(纸本)9798350349849
Technology advancements have transformed medical science and practice, leading to the vast gathering of a wide range of medical data. Medical researchers use artificial intelligence techniques extensively because they enable the identification and creation of models of complicated datasets and the interactions between them. This, in turn, enables the successful prediction of future outcomes associated with a specific sickness type. An artificial intelligence-based approach to healthcare data analytics is presented, which leverages data to build a desired model and solve a particular issue. The suggested approach for healthcare data analytics uses a random forest and feedforward artificial neural network with two hidden layers as its basis to get the best model.
In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protoc...
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In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.
This paper develops a formal string diagram language for monoidal closed categories. Previous work has shown that string diagrams for freely generated symmetric monoidal categories can be viewed as hypergraphs with in...
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Fairness concern behavior, a well-known cognitive bias, refers to a person’s attitude of dissatisfaction for unequal pay-offs in someone’s favor. Against environmental pollution, many firms are focused on green manu...
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This paper describes the development of an IoT device prototype for measuring biogas release parameters. Through the device, measurements are made, and statistics are accumulated based on which calculations are perfor...
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The report proposes a dynamic routing method for Self-healing Networks (ShN). The method takes into account the specific features of ShN. During routing, the flow of service information is reduced. The search for the ...
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the report presents the results of a comparative analysis of methods for parameterization of cognitive models based on the use of fuzzy set theory and antonym logic. Reasoned expediency of using methods of the logic o...
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The subject of the study is methods of balancing raw data. The purpose of the article is to improve the quality of intrusion detection in computer networks by using class balancing methods. Task: to investigate method...
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The development of multi-agent technology for assessing the availability of information at the software development initial stages is an actual task, the solution of which is the purpose of this study. The paper prese...
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