The rapid progress of the Internet has exposed networks to an increasednumber of threats. Intrusion detection technology can effectively protect networksecurity against malicious attacks. In this paper, we propose a R...The rapid progress of the Internet has exposed networks to an increasednumber of threats. Intrusion detection technology can effectively protect networksecurity against malicious attacks. In this paper, we propose a ReliefF-P-NaiveBayes and softmax regression (RP-NBSR) model based on machine learningfor network attack detection to improve the false detection rate and F1 score ofunknown intrusion behavior. In the proposed model, the Pearson correlation coef-ficient is introduced to compensate for deficiencies in correlation analysis betweenfeatures by the ReliefF feature selection algorithm, and a ReliefF-Pearson correlation coefficient (ReliefF-P) algorithm is proposed. Then, the Relief-P algorithm isused to preprocess the UNSW-NB15 dataset to remove irrelevant features andobtain a new feature subset. Finally, naïve Bayes and softmax regression (NBSR)classifier is constructed by cascading the naïve Bayes classifier and softmaxregression classifier, and an attack detection model based on RP-NBSR is established. The experimental results on the UNSW-NB15 dataset show that the attackdetection model based on RP-NBSR has a lower false detection rate and higherF1 score than other detection models.展开更多
Trajectory privacy protection schemes based on suppression strategies rarely take geospatial constraints into account,which is made more likely for an attacker to determine the user’s true sensitive location and traj...Trajectory privacy protection schemes based on suppression strategies rarely take geospatial constraints into account,which is made more likely for an attacker to determine the user’s true sensitive location and trajectory.To solve this problem,this paper presents a privacy budget allocation method based on privacy security level(PSL).Firstly,in a custom map,the idea of P-series is contributed to allocate a given total privacy budget reasonably to the initially sensitive locations.Then,the size of privacy security level for sensitive locations is dynamically adjusted by comparing it with the customized initial level threshold parameterµ.Finally,the privacy budget of the initial sensitive location is allocated to its neighbors based on the relationship between distance and degree between nodes.By comparing the PSL algorithm with the traditional allocation methods,the results show that it is more flexible to allocate a privacy budget without compromising location privacy under the same preset conditions.展开更多
基金supported by the National Natural Science Foundation of China(61300216,Wang,H,www.nsfc.gov.cn).
文摘The rapid progress of the Internet has exposed networks to an increasednumber of threats. Intrusion detection technology can effectively protect networksecurity against malicious attacks. In this paper, we propose a ReliefF-P-NaiveBayes and softmax regression (RP-NBSR) model based on machine learningfor network attack detection to improve the false detection rate and F1 score ofunknown intrusion behavior. In the proposed model, the Pearson correlation coef-ficient is introduced to compensate for deficiencies in correlation analysis betweenfeatures by the ReliefF feature selection algorithm, and a ReliefF-Pearson correlation coefficient (ReliefF-P) algorithm is proposed. Then, the Relief-P algorithm isused to preprocess the UNSW-NB15 dataset to remove irrelevant features andobtain a new feature subset. Finally, naïve Bayes and softmax regression (NBSR)classifier is constructed by cascading the naïve Bayes classifier and softmaxregression classifier, and an attack detection model based on RP-NBSR is established. The experimental results on the UNSW-NB15 dataset show that the attackdetection model based on RP-NBSR has a lower false detection rate and higherF1 score than other detection models.
基金the National Natural Science Foundation of China.61300216Doctoral Scientific Fund of Henan Polytechnic University.B2022-16+1 种基金Doctoral Scientific Fund of Henan Polytechnic University.B2020-32Youth Fund of Henan Polytechnic University.Q2014-05。
文摘Trajectory privacy protection schemes based on suppression strategies rarely take geospatial constraints into account,which is made more likely for an attacker to determine the user’s true sensitive location and trajectory.To solve this problem,this paper presents a privacy budget allocation method based on privacy security level(PSL).Firstly,in a custom map,the idea of P-series is contributed to allocate a given total privacy budget reasonably to the initially sensitive locations.Then,the size of privacy security level for sensitive locations is dynamically adjusted by comparing it with the customized initial level threshold parameterµ.Finally,the privacy budget of the initial sensitive location is allocated to its neighbors based on the relationship between distance and degree between nodes.By comparing the PSL algorithm with the traditional allocation methods,the results show that it is more flexible to allocate a privacy budget without compromising location privacy under the same preset conditions.
