针对复杂网络群落划分的准确性差和时间复杂度高的问题,设计了一种基于修正Jaccard贴近度和群落合并的用于非堆叠群落的划分算法IJCD(Improved Jaccard community detection)。该算法针对Jaccard贴近度的计算结果中存在距离不同但贴近...针对复杂网络群落划分的准确性差和时间复杂度高的问题,设计了一种基于修正Jaccard贴近度和群落合并的用于非堆叠群落的划分算法IJCD(Improved Jaccard community detection)。该算法针对Jaccard贴近度的计算结果中存在距离不同但贴近度可能相同的情况,引入了改进的Jaccard贴近度算法计算节点之间的贴近度,选择多个贴近节点在一个群落而不是最贴近的两个节点,从而得到初始群落,再进行群落合并。计算所得的初始群落的准确率较高且群落个数较少,提高了整个算法的效率。最后,采用了几种经典的算法对网络进行群落划分,在选取的几个真实网络和计算机生成网络上的实验结果表明:IJCD算法能够有效地对群落进行划分,并且有较高的准确度和较低的时间复杂度。展开更多
Machine Learning(ML)systems often involve a re-training process to make better predictions and classifications.This re-training process creates a loophole and poses a security threat for ML systems.Adversaries leverag...Machine Learning(ML)systems often involve a re-training process to make better predictions and classifications.This re-training process creates a loophole and poses a security threat for ML systems.Adversaries leverage this loophole and design data poisoning attacks against ML systems.Data poisoning attacks are a type of attack in which an adversary manipulates the training dataset to degrade the ML system’s performance.Data poisoning attacks are challenging to detect,and even more difficult to respond to,particularly in the Internet of Things(IoT)environment.To address this problem,we proposed DISTINIT,the first proactive data poisoning attack detection framework using distancemeasures.We found that Jaccard Distance(JD)can be used in the DISTINIT(among other distance measures)and we finally improved the JD to attain an Optimized JD(OJD)with lower time and space complexity.Our security analysis shows that the DISTINIT is secure against data poisoning attacks by considering key features of adversarial attacks.We conclude that the proposed OJD-based DISTINIT is effective and efficient against data poisoning attacks where in-time detection is critical for IoT applications with large volumes of streaming data.展开更多
文摘针对复杂网络群落划分的准确性差和时间复杂度高的问题,设计了一种基于修正Jaccard贴近度和群落合并的用于非堆叠群落的划分算法IJCD(Improved Jaccard community detection)。该算法针对Jaccard贴近度的计算结果中存在距离不同但贴近度可能相同的情况,引入了改进的Jaccard贴近度算法计算节点之间的贴近度,选择多个贴近节点在一个群落而不是最贴近的两个节点,从而得到初始群落,再进行群落合并。计算所得的初始群落的准确率较高且群落个数较少,提高了整个算法的效率。最后,采用了几种经典的算法对网络进行群落划分,在选取的几个真实网络和计算机生成网络上的实验结果表明:IJCD算法能够有效地对群落进行划分,并且有较高的准确度和较低的时间复杂度。
基金This work was supported by a National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)under Grant 2020R1A2B5B01002145.
文摘Machine Learning(ML)systems often involve a re-training process to make better predictions and classifications.This re-training process creates a loophole and poses a security threat for ML systems.Adversaries leverage this loophole and design data poisoning attacks against ML systems.Data poisoning attacks are a type of attack in which an adversary manipulates the training dataset to degrade the ML system’s performance.Data poisoning attacks are challenging to detect,and even more difficult to respond to,particularly in the Internet of Things(IoT)environment.To address this problem,we proposed DISTINIT,the first proactive data poisoning attack detection framework using distancemeasures.We found that Jaccard Distance(JD)can be used in the DISTINIT(among other distance measures)and we finally improved the JD to attain an Optimized JD(OJD)with lower time and space complexity.Our security analysis shows that the DISTINIT is secure against data poisoning attacks by considering key features of adversarial attacks.We conclude that the proposed OJD-based DISTINIT is effective and efficient against data poisoning attacks where in-time detection is critical for IoT applications with large volumes of streaming data.
