As the Internet offers increased connectivity between human beings, it has fallen prey to malicious users who exploit its resources to gain illegal access to critical information. In an effort to protect computer netw...As the Internet offers increased connectivity between human beings, it has fallen prey to malicious users who exploit its resources to gain illegal access to critical information. In an effort to protect computer networks from external attacks, two common types of Intrusion Detection Systems (IDSs) are often deployed. The first type is signature-based IDSs which can detect intrusions efficiently by scanning network packets and comparing them with human-generated signatures describing previously-observed attacks. The second type is anomaly-based IDSs able to detect new attacks through modeling normal network traffic without the need for a human expert. Despite this advantage, anomaly-based IDSs are limited by a high false-alarm rate and difficulty detecting network attacks attempting to blend in with normal traffic. In this study, we propose a StreamPreDeCon anomaly-based IDS. StreamPreDeCon is an extension of the preference subspace clustering algorithm PreDeCon designed to resolve some of the challenges associated with anomalous packet detection. Using network packets extracted from the first week of the DARPA '99 intrusion detection evaluation dataset combined with Generic Http, Shellcode and CLET attacks, our IDS achieved 94.4% sensitivity and 0.726% false positives in a best case scenario. To measure the overall effectiveness of the IDS, the average sensitivity and false positive rates were calculated for both the maximum sensitivity and the minimum false positive rate. With the maximum sensitivity, the IDS had 80% sensitivity and 9% false positives on average. The IDS also averaged 63% sensitivity with a 0.4% false positive rate when the minimal number of false positives is needed. These rates are an improvement on results found in a previous study as the sensitivity rate in general increased while the false positive rate decreased.展开更多
The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for id...The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for identifying sentiment in OSNs such as communication pattern mining and classification based on emoticon and parts of speech, the majority of them utilize a suboptimal batch mode learning approach when analyzing a large amount of real time data. As an alternative we present a stream algorithm using Modified Balanced Winnow for sentiment analysis on OSNs. Tested on three real-world network datasets, the performance of our sentiment predictions is close to that of batch learning with the ability to detect important features dynamically for sentiment analysis in data streams. These top features reveal key words important to the analysis of sentiment.展开更多
随着高清视频和实时流媒体业务的快速增长,智能数据中心面临传输稳定性与服务质量(Quality of Service,QoS)保障的双重挑战。基于此,研究软件定义广域网络(Software Defined Wide Area Network,SD-WAN)在视频流传输中的路径调度优化、Qo...随着高清视频和实时流媒体业务的快速增长,智能数据中心面临传输稳定性与服务质量(Quality of Service,QoS)保障的双重挑战。基于此,研究软件定义广域网络(Software Defined Wide Area Network,SD-WAN)在视频流传输中的路径调度优化、QoS保障机制与安全策略设计,分析其在高清视频分发、视频会议保障、边缘协同处理与远程监控管理中的应用效果,探讨SD-WAN在构建高效、安全、智能的视频网络传输体系中的技术路径。展开更多
流数据作为大数据的重要形式广泛存在于实际问题中,由于流数据中数据分布变化产生概念漂移,容易导致模型的泛化性能下降,且在实际应用问题中,数据标记成本较高,难以获得强监督的信息.针对以上问题,本文提出一种基于在线深度神经网络的...流数据作为大数据的重要形式广泛存在于实际问题中,由于流数据中数据分布变化产生概念漂移,容易导致模型的泛化性能下降,且在实际应用问题中,数据标记成本较高,难以获得强监督的信息.针对以上问题,本文提出一种基于在线深度神经网络的弱监督概念漂移检测(Weakly supervised conceptual drift detection method based on online deep neural network,WSCDD)方法.该方法设计了一种在线深度神经网络模型,采用Hedge反向传播方法在线学习网络深度,并通过设计Dropout层在模型预测时引入随机性,利用蒙特卡罗方法量化深度神经网络模型的预测不确定性,通过自适应滑动窗口技术检测弱监督环境下概念漂移的发生,并使模型适应新的概念.实验结果表明,该方法可以准确检测数据流中概念漂移的发生,在漂移发生后能够快速收敛到新的数据分布,提高了学习模型的泛化性能.展开更多
文摘As the Internet offers increased connectivity between human beings, it has fallen prey to malicious users who exploit its resources to gain illegal access to critical information. In an effort to protect computer networks from external attacks, two common types of Intrusion Detection Systems (IDSs) are often deployed. The first type is signature-based IDSs which can detect intrusions efficiently by scanning network packets and comparing them with human-generated signatures describing previously-observed attacks. The second type is anomaly-based IDSs able to detect new attacks through modeling normal network traffic without the need for a human expert. Despite this advantage, anomaly-based IDSs are limited by a high false-alarm rate and difficulty detecting network attacks attempting to blend in with normal traffic. In this study, we propose a StreamPreDeCon anomaly-based IDS. StreamPreDeCon is an extension of the preference subspace clustering algorithm PreDeCon designed to resolve some of the challenges associated with anomalous packet detection. Using network packets extracted from the first week of the DARPA '99 intrusion detection evaluation dataset combined with Generic Http, Shellcode and CLET attacks, our IDS achieved 94.4% sensitivity and 0.726% false positives in a best case scenario. To measure the overall effectiveness of the IDS, the average sensitivity and false positive rates were calculated for both the maximum sensitivity and the minimum false positive rate. With the maximum sensitivity, the IDS had 80% sensitivity and 9% false positives on average. The IDS also averaged 63% sensitivity with a 0.4% false positive rate when the minimal number of false positives is needed. These rates are an improvement on results found in a previous study as the sensitivity rate in general increased while the false positive rate decreased.
文摘The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for identifying sentiment in OSNs such as communication pattern mining and classification based on emoticon and parts of speech, the majority of them utilize a suboptimal batch mode learning approach when analyzing a large amount of real time data. As an alternative we present a stream algorithm using Modified Balanced Winnow for sentiment analysis on OSNs. Tested on three real-world network datasets, the performance of our sentiment predictions is close to that of batch learning with the ability to detect important features dynamically for sentiment analysis in data streams. These top features reveal key words important to the analysis of sentiment.
文摘随着高清视频和实时流媒体业务的快速增长,智能数据中心面临传输稳定性与服务质量(Quality of Service,QoS)保障的双重挑战。基于此,研究软件定义广域网络(Software Defined Wide Area Network,SD-WAN)在视频流传输中的路径调度优化、QoS保障机制与安全策略设计,分析其在高清视频分发、视频会议保障、边缘协同处理与远程监控管理中的应用效果,探讨SD-WAN在构建高效、安全、智能的视频网络传输体系中的技术路径。
文摘流数据作为大数据的重要形式广泛存在于实际问题中,由于流数据中数据分布变化产生概念漂移,容易导致模型的泛化性能下降,且在实际应用问题中,数据标记成本较高,难以获得强监督的信息.针对以上问题,本文提出一种基于在线深度神经网络的弱监督概念漂移检测(Weakly supervised conceptual drift detection method based on online deep neural network,WSCDD)方法.该方法设计了一种在线深度神经网络模型,采用Hedge反向传播方法在线学习网络深度,并通过设计Dropout层在模型预测时引入随机性,利用蒙特卡罗方法量化深度神经网络模型的预测不确定性,通过自适应滑动窗口技术检测弱监督环境下概念漂移的发生,并使模型适应新的概念.实验结果表明,该方法可以准确检测数据流中概念漂移的发生,在漂移发生后能够快速收敛到新的数据分布,提高了学习模型的泛化性能.
