The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber threats.Among the myriad of potential...The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber threats.Among the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic.As IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT serves.In this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS attacks.The CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)methods.The proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in real-time.This involvesemploying the best-performing ML algorithmfor detection and the integration of warning mechanisms.We believe this approach will significantly enhance the field of security research and continue to refine it based on future insights and developments.展开更多
Background Performance metrics currently focus on the measurement of the application of guideline-indi- cated medications without considering the appropriate dosing of these drugs.
How a property tycoon has ditched profits for helping the elderly and realizing a childhood dream Ever since her teenage years,Wen Xiujie has been full of dreams and few earthly desires. In mid-1980s,when Chinese peop...How a property tycoon has ditched profits for helping the elderly and realizing a childhood dream Ever since her teenage years,Wen Xiujie has been full of dreams and few earthly desires. In mid-1980s,when Chinese people invented the term"10,000-yuan house- holds"to describe a well-off family with a new fortune from private businesses, 10,000 yuan(＄6,667 back then)was the weekly pocket money given to Wen, who was a middle school student.As heiress to the family property devel- opment company her father展开更多
文摘The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber threats.Among the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic.As IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT serves.In this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS attacks.The CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)methods.The proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in real-time.This involvesemploying the best-performing ML algorithmfor detection and the integration of warning mechanisms.We believe this approach will significantly enhance the field of security research and continue to refine it based on future insights and developments.
文摘Background Performance metrics currently focus on the measurement of the application of guideline-indi- cated medications without considering the appropriate dosing of these drugs.
文摘How a property tycoon has ditched profits for helping the elderly and realizing a childhood dream Ever since her teenage years,Wen Xiujie has been full of dreams and few earthly desires. In mid-1980s,when Chinese people invented the term"10,000-yuan house- holds"to describe a well-off family with a new fortune from private businesses, 10,000 yuan(＄6,667 back then)was the weekly pocket money given to Wen, who was a middle school student.As heiress to the family property devel- opment company her father
