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
To relieve the negative effect brought by the intricate wireless network environment and unstable user behavior in layered mobile peer-to peer（P2P） streaming service, an evolved layered P2P （E-LP2P） data schedulin...To relieve the negative effect brought by the intricate wireless network environment and unstable user behavior in layered mobile peer-to peer（P2P） streaming service, an evolved layered P2P （E-LP2P） data scheduling scheme in the process of service delivery is introduced in this paper. The data in base layer is scheduled according to its importance in streaming play to guarantee the basic play of streaming. The data in enhancement layer is scheduled according to the characters of streaming data, including its position and amount in server peer set in a multiple tied way towards the data in enhancement layer. To cope with the layer jitter caused by the fluctuation of bandwidth, jitter prevent mechanism is used to adjust the highest layer dynamically during the process of data scheduling. Simulation results show that the E-LP2P can provide good quality of service（QoS） performance in terms of throughput, layer delivery ratio, server load and useless packet ratio.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
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.
基金supported by the National Natural Science Foundation of China (60902047)the Fundamental Research Funds for the Central Universities (BUPT 2009RC0120)
文摘To relieve the negative effect brought by the intricate wireless network environment and unstable user behavior in layered mobile peer-to peer（P2P） streaming service, an evolved layered P2P （E-LP2P） data scheduling scheme in the process of service delivery is introduced in this paper. The data in base layer is scheduled according to its importance in streaming play to guarantee the basic play of streaming. The data in enhancement layer is scheduled according to the characters of streaming data, including its position and amount in server peer set in a multiple tied way towards the data in enhancement layer. To cope with the layer jitter caused by the fluctuation of bandwidth, jitter prevent mechanism is used to adjust the highest layer dynamically during the process of data scheduling. Simulation results show that the E-LP2P can provide good quality of service（QoS） performance in terms of throughput, layer delivery ratio, server load and useless packet ratio.
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。
