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Machine Learning with Dimensionality Reduction for DDoS Attack Detection

Computers, Materials & Continua 2022年第8期72卷 2665-2682页

作者： Shaveta Gupta dinesh grover Ahmad Ali AlZubi Nimit Sachdeva Mirza Waqar Baig Jimmy Singla IK Gujral Punjab Technical University Jalandhar144603India Department.of Electrical Engineering and Computer Science Punjab Agriculture UniversityLudhiana141004India Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Vunsol Private Limited Mohali160055India Department of Electrical Engineering FAST National UniversityCFD CampusFaisalabad44000Pakistan School of Computer Science and Engineering Lovely Professional UniversityPunjab144001India

With the advancement of internet,there is also a rise in cybercrimes and digital ***(Distributed Denial of Service)attack is the most dominant weapon to breach the vulnerabilities of internet and pose a significant threat in the digital *** cyber-attacks are generated deliberately and consciously by the hacker to overwhelm the target with heavy traffic that genuine users are unable to use the target *** a result,targeted services are inaccessible by the legitimate *** prevent these attacks,researchers are making use of advanced Machine Learning classifiers which can accurately detect the DDoS ***,the challenge in using these techniques is the limitations on capacity for the volume of data and the required processing *** this research work,we propose the framework of reducing the dimensions of the data by selecting the most important features which contribute to the predictive *** show that the‘lite’model trained on reduced dataset not only saves the computational power,but also improves the predictive *** show that dimensionality reduction can improve both effectiveness(recall)and efficiency(precision)of the model as compared to the model trained on‘full’dataset.

关键词： DDoS(Distributed denial of service) internet ML(machine learning) accuracy

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