Lossless data hiding can restore the original status of cover media after embedded secret data are extracted. In 2010, Wang et al. proposed a lossless data hiding scheme which hides secret data in vector quantization ...Lossless data hiding can restore the original status of cover media after embedded secret data are extracted. In 2010, Wang et al. proposed a lossless data hiding scheme which hides secret data in vector quantization （VQ） indices, but the encoding strategies adopted by their scheme expand the final codestream. This paper designs four embedding and encoding strategies to improve Wang et aL＇s scheme. The experiment result of the proposed scheme compared with that of the Wang et aL＇s scheme reduces the bit rates of the final codestream by 4.6% and raises the payload by 1.09% on average.展开更多
In recent years, significant research has been devoted to the development of Intrusion Detection Systems (IDS) able to detect anomalous computer network traffic indicative of malicious activity. While signature-based ...In recent years, significant research has been devoted to the development of Intrusion Detection Systems (IDS) able to detect anomalous computer network traffic indicative of malicious activity. While signature-based IDS have proven effective in discovering known attacks, anomaly-based IDS hold the even greater promise of being able to automatically detect previously undocumented threats. Traditional IDS are generally trained in batch mode, and therefore cannot adapt to evolving network data streams in real time. To resolve this limitation, data stream mining techniques can be utilized to create a new type of IDS able to dynamically model a stream of network traffic. In this paper, we present two methods for anomalous network packet detection based on the data stream mining paradigm. The first of these is an adapted version of the DenStream algorithm for stream clustering specifically tailored to evaluate network traffic. In this algorithm, individual packets are treated as points and are flagged as normal or abnormal based on their belonging to either normal or outlier clusters. The second algorithm utilizes a histogram to create a model of the evolving network traffic to which incoming traffic can be compared using Pearson correlation. Both of these algorithms were tested using the first week of data from the DARPA ’99 dataset with Generic HTTP, Shell-code and Polymorphic attacks inserted. We were able to achieve reasonably high detection rates with moderately low false positive percentages for different types of attacks, though detection rates varied between the two algorithms. Overall, the histogram-based detection algorithm achieved slightly superior results, but required more parameters than the clustering-based algorithm. As a result of its fewer parameter requirements, the clustering approach can be more easily generalized to different types of network traffic streams.展开更多
基金supported by the National Science Council,Taiwan under Grant No.NSC 99-2221-E-324-040-MY2
文摘Lossless data hiding can restore the original status of cover media after embedded secret data are extracted. In 2010, Wang et al. proposed a lossless data hiding scheme which hides secret data in vector quantization （VQ） indices, but the encoding strategies adopted by their scheme expand the final codestream. This paper designs four embedding and encoding strategies to improve Wang et aL＇s scheme. The experiment result of the proposed scheme compared with that of the Wang et aL＇s scheme reduces the bit rates of the final codestream by 4.6% and raises the payload by 1.09% on average.
文摘In recent years, significant research has been devoted to the development of Intrusion Detection Systems (IDS) able to detect anomalous computer network traffic indicative of malicious activity. While signature-based IDS have proven effective in discovering known attacks, anomaly-based IDS hold the even greater promise of being able to automatically detect previously undocumented threats. Traditional IDS are generally trained in batch mode, and therefore cannot adapt to evolving network data streams in real time. To resolve this limitation, data stream mining techniques can be utilized to create a new type of IDS able to dynamically model a stream of network traffic. In this paper, we present two methods for anomalous network packet detection based on the data stream mining paradigm. The first of these is an adapted version of the DenStream algorithm for stream clustering specifically tailored to evaluate network traffic. In this algorithm, individual packets are treated as points and are flagged as normal or abnormal based on their belonging to either normal or outlier clusters. The second algorithm utilizes a histogram to create a model of the evolving network traffic to which incoming traffic can be compared using Pearson correlation. Both of these algorithms were tested using the first week of data from the DARPA ’99 dataset with Generic HTTP, Shell-code and Polymorphic attacks inserted. We were able to achieve reasonably high detection rates with moderately low false positive percentages for different types of attacks, though detection rates varied between the two algorithms. Overall, the histogram-based detection algorithm achieved slightly superior results, but required more parameters than the clustering-based algorithm. As a result of its fewer parameter requirements, the clustering approach can be more easily generalized to different types of network traffic streams.
