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Cybernet Model:A New Deep Learning Model for Cyber DDoS Attacks Detection and Recognition

Computers, Materials & Continua 2024年第1期78卷 1275-1295页

作者： Azar Abid Salih maiwan bahjat abdulrazaq Department of Information Technology Technical College of DuhokDuhok Polytechnic UniversityDuhokIraq Department of Computer Science Faculty of ScienceUniversity of ZakhoDuhokIraq

Cyberspace is extremely dynamic,with new attacks arising *** cybersecurity controls is vital for network *** Learning(DL)models find widespread use across various fields,with cybersecurity being one of the most crucial due to their rapid cyberattack detection capabilities on networks and *** capabilities of DL in feature learning and analyzing extensive data volumes lead to the recognition of network traffic *** study presents novel lightweight DL models,known as Cybernet models,for the detection and recognition of various cyber Distributed Denial of Service(DDoS)*** models were constructed to have a reasonable number of learnable parameters,i.e.,less than 225,000,hence the name“lightweight.”This not only helps reduce the number of computations required but also results in faster training and inference ***,these models were designed to extract features in parallel from 1D Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM),which makes them unique compared to earlier existing architectures and results in better performance *** validate their robustness and effectiveness,they were tested on the CIC-DDoS2019 dataset,which is an imbalanced and large dataset that contains different types of DDoS *** results revealed that bothmodels yielded promising results,with 99.99% for the detectionmodel and 99.76% for the recognition model in terms of accuracy,precision,recall,and F1 ***,they outperformed the existing state-of-the-art models proposed for the same ***,the proposed models can be used in cyber security research domains to successfully identify different types of attacks with a high detection and recognition rate.

关键词： Deep learning CNN LSTM Cybernet model DDoS recognition

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KurdSet: A Kurdish Handwritten Characters Recognition Dataset Using Convolutional Neural Network

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Computers, Materials & Continua 2024年第4期79卷 429-448页

作者： Sardar Hasen Ali maiwan bahjat Abdulrazzaq Computer Science Department University of ZakhoDuhokIraq

Handwritten character recognition(HCR)involves identifying characters in images,documents,and various sources such as forms surveys,questionnaires,and signatures,and transforming them into a machine-readable format for subsequent *** recognizing complex and intricately shaped handwritten characters remains a significant *** use of convolutional neural network(CNN)in recent developments has notably advanced HCR,leveraging the ability to extract discriminative features from extensive sets of raw *** of the absence of pre-existing datasets in the Kurdish language,we created a Kurdish handwritten dataset called(KurdSet).The dataset consists of Kurdish characters,digits,texts,and *** dataset consists of 1560 participants and contains 45,240 *** this study,we chose characters only from our *** utilized a Kurdish dataset for handwritten character *** study also utilizes various models,including InceptionV3,Xception,DenseNet121,and a *** show the performance of the KurdSet dataset,we compared it to Arabic handwritten character recognition dataset(AHCD).We applied the models to both datasets to show the performance of our ***,the performance of the models is evaluated using test accuracy,which measures the percentage of correctly classified characters in the evaluation *** models performed well in the training phase,DenseNet121 exhibited the highest accuracy among the models,achieving a high accuracy of 99.80%on the Kurdish *** Xception model achieved 98.66%using the Arabic dataset.

关键词： CNN models Kurdish handwritten recognition KurdSet dataset Arabic handwritten recognition DenseNet121 model InceptionV3 model Xception model

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