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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Design and implementation of an embedded interactive timing system for medical treatment of patients with learning and attention issues 28

Design and implementation of an embedded interactive timing ...

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28th International Scientific Conference Electronics, ET 2019 - Proceedings

作者： Angelova, Maria A. Velchev, Yuliyan S. Dimitrov, Kalin L. Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies 8 Kliment Ohridski blvd Sofia1000 Bulgaria Department of Radio Communications and Video Technologies Faculty of Telecommunications Technical University of Sofia 8 Kliment Ohridski blvd Sofia1000 Bulgaria

ISBN: (纸本)9781728125749

This article describes the design and implementation of an Embedded Interactive Timing System. Such systems are used in the therapy of children who have learning problems. The matching of different beats can help the brain work more efficiently. A methodology and architecture of an interactive timing is proposed. Open-source software environment and microcontroller is used for the practical realization of the system. Some results of the program functioning are shown. © 2019 IEEE.

关键词： Microcontrollers

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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

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computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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The technique for computer systems Trojan diagnosis in the monitor mode

The technique for computer systems Trojan diagnosis in the m...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications

作者： Segiy Lysenko Oleg Savenko System programming Department Khmelnitsky National University Khmelnitskiy Ukraine Computer Systems and Programming Faculty Lecturer of System programming Department Khmelnitsky National University Khmelnitskiy Ukraine

New technique for computer system Trojan diagnosis in monitor mode which uses fuzzy logic and allows improving reliability and efficiency is developed.

关键词： Trojan horses computers Monitoring Software Computational modeling Software reliability

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Neuromorphic Neurons and Networks for Artificial Intelligence Built Using Temporal Space Calculations 32

Neuromorphic Neurons and Networks for Artificial Intelligenc...

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32nd International Scientific Conference Electronics, ET 2023

作者： Trendafilov, Ianislav Gancheva, Veska Neuromorphica Ltd. 8 Kliment Ohridski blvd. Sofia1335 Bulgaria Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350302004

We deigned a spiking neural network that computes network weights in the temporal dimension. Such a network can be used for artificial intelligence and deep learning. We demonstrate circuits implementing blocks for building such a network and then a training model. This enables creation of efficient Hessenstein-Reichardt detectors observed in motion detection networks in the nature. The proposed network allows reconfiguration across network layers algorithmically. © 2023 IEEE.

关键词： Neural networks

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Good error correcting output codes for adaptive multiclass learning

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4th International Workshop on Multiple Classifier systems, MCS 2003

作者： Tapia, Elizabeth González, José Carlos García-Villalba, Javier Department of Electrical Engineering National Univ. of Rosario Argentina Department of Telematics Engineering Technical Univ. of Madrid Spain Department of Computer Systems and Programming Complutense Univ. of Madrid Spain

ISBN: (纸本)3540403698

In recent work, we introduced a generalization of ECOC learning under the theory of recursive error correcting codes. We named it RECOC (Recursive ECOC) learning. If long output codewords are allowed, as in the case of problems involving a large number of classes, standard recursive codes such as LDPC or Turbo can be used. However, if the number of classes is moderated, neither good LDPC nor Turbo codes might exist due to the small block lengths involved. In this paper, RECOC learning based on the recently introduced ensemble of Product Accumulated codes is analyzed. Due to their native data storage oriented design, smaller block lengths are allowed. In addition, because of their simplicity, all key concepts regarding RECOC learning can be easily explained. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Digital storage

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Software Anomaly Detection Method Based on Artificial Neural Network

Software Anomaly Detection Method Based on Artificial Neural...

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IEEE International Conference on e-Business Engineering (ICEBE)

作者： Veska Gancheva Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

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.

关键词： Training Accuracy Predictive models Network security Feature extraction Data structures Data models Software Anomaly detection Principal component analysis

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Traffic and road sign recognition using deep convolutional neural network

Traffic and road sign recognition using deep convolutional n...

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2021 International Conference on Information Science and Communications Technologies, ICISCT 2021

作者： Kuchkorov, Temurbek Khamzaev, Jamshid Allamuratova, Zamira Ochilov, Temur Tashkent University of Information Technologies Nmaed after Muhammad Al-Khwarizmi Department of Computer Systems Tashkent Uzbekistan Tashkent University of Information Technologies Nmaed after Muhammad Al-Khwarizmi Joint Faculty TUIT-BSUIR Department of Information-computer Technologies and Programming Tashkent Uzbekistan

ISBN: (纸本)9781665432580

Automatic road sign recognition is one of the most important steps to help a driver prevent accidents. In this paper, the deep convolutional neural network (Deep CNN) was used for the autonomous traffic and road signs detection and recognition system. The proposed system works in real-time recognition of the image of road signs. The contribution of this article is that it consists of a newly developed datasets for 42 different road signs using combination of existing datasets and collected local road signs. The images were taken from different angles and included other parameters and conditions. More than 30K images were collected to create the dataset, which is named Uzbekistan traffic and road signs (UZ-TRS-2021). The CNN architecture was used with various parameters to achieve the best recognition speed. Experimental results show that the proposed CNN architecture achieved 98% accuracy. © 2021 IEEE.

关键词： Convolutional neural networks

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Healthcare Data Analytics Based on Machine Learning

Healthcare Data Analytics Based on Machine Learning

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Communications, Computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Veska Gancheva Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

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.

关键词： Analytical models Data analysis Computational modeling Medical services Artificial neural networks Learning (artificial intelligence) Data models Telecommunication computing Informatics Random forests

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Medical Software User Interfaces, Stroke MD application design

Medical Software User Interfaces, Stroke MD application desi...

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E-Health and Bioengineering Conference

作者： Elena Zamsa Programming Systems department Institute of Mathematics and Computer Science IMI Chisinau Republic of Moldova

ISBN: (纸本)9781467375467

The article presents the design of an application interface for associated medical data visualization and management for neurologists in a stroke clustering and prediction system called Stroke MD. The goal of the system is to facilitate efficient visual data introduction and knowledge extraction based on a predictive model implementation. Aspects such as quality of the visualized data, type of the viewed data (risk factor groups, clustered groups, data alerts, conclusions etc.) and efficient user-software interaction directly influenced decisions about system design, in particular the design of the doctor's/patient's user interface sides, input methods and user-software interaction techniques, ending in to a hybrid setup of interface.

关键词： e-health medical software user interfaces stroke risk groups management systems

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