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Over the past decade, numerous systems have been proposed to detect and subsequently prevent or mitigate security vulnerabilities. However, many existing intrusion or anomaly detection solutions are limited to a subset of the traffic due to scalability issues, hence failing to operate at line-rate on large, high-speed datacentre networks. In this paper, we present a two-level solution for anomaly...
Distributed Denial-of-Service (DDoS) attacks continue to constitute a pernicious threat to the delivery of services within the Internet domain. These attacks harness the power of thousands, and sometimes tens or hundreds of thousands of compromised computers to attack web-services and online trading sites, resulting in significant down-time and financial loss. The problem of detecting DDoS attacks...
Anomalies in communication network traffic caused by malware or denial-of-service attacks manifest themselves in structural changes in the covariance matrix of traffic features. Real-time detection of anomalies in high-dimensional data demands a very efficient algorithm to identify these changes in a compact low-dimensional representation. This paper presents an efficient algorithm for the rapid detection...
Botnets have become one of the major tools used by attackers to perform various malicious activities on the Internet, such as launching distributed denial of service attacks, sending spam, leaking personal information, and so on. In this paper, we present BotCatch, a behavior-based botnet detection system that considers multiple coordinated group activities in the monitored network to identify bot-infected...
The identification of Internet applications is important for ISPs and network administrators to protect the network from unwanted traffic and prioritize some major applications. Statistical methods are widely used since they allow to classify applications according to their statistical signatures. They combine the statistical analysis of flow parameters, such as packet size and inter-packet time,...
Malicious web pages are an emerging security concern on the Internet due to their popularity and their potential serious impacts. Detecting and analyzing them is very costly because of their qualities and complexities. There has been some research approaches carried out in order to detect them. The approaches can be classified into two main groups based on their used analysis features: static feature...
Nowadays, there is more and more network education public sentiment information on internet and it has produced a significant impact on education. More and more hide information which is useful can be mined; therefore, this paper constructs a model about the application of the text mining technology in monitoring the network education public sentiment, according to the characteristics of the network...
This paper constructed a web site supervision system to analyze the sentiment orientation of the articles (document, text) on the web sites in intranet. A scheme based on Vector Space Model (VSM) is put forward for identifying the sentiment orientation of document. Considering the feature of a document is relative to the term frequencies appears in the document and corpus, we extract the feature of...
In this paper, we propose an efficient approach for real-time eye-gaze detection from images acquired from a web camera. The measured data is sufficient to describe the eye movement, because the web camera is stationary with respect to the head. First, the image is binarized with a dynamic threshold. Then geometry features of the eye image are extracted from binary image. Next using estimation method...
Thanks to Rich Internet Applications (RIAs) with their enhanced interactivity, responsiveness and dynamicity, the user experience in the Web 2.0 is becoming more and more appealing and user-friendly. The dynamic nature of RIAs and the heterogeneous technologies, frameworks, communication models used for implementing them negatively affect their analyzability and understandability. Consequently, specific...
In large-scale compute cloud systems, component failures become norms instead of exceptions. Failure occurrence as well as its impact on system performance and operation costs are becoming an increasingly important concern to system designers and administrators. When a system fails to function properly, health-related data are valuable for troubleshooting. However, it is challenging to effectively...
In this study, a vision monitoring system that is applicable to the maintenance of data centers was developed and applied to data center status extraction. The vision monitoring system, which is intended to complement system monitoring tools, such as IPMI and Nagios, has the additional benefit of enabling continuous monitoring of the external status of data centers. This system, which is based on...
Tracking the spread of an epidemic disease like seasonal or pandemic influenza is an important task that can reduce its impact and help authorities plan their response. In particular, early detection and geolocation of an outbreak are important aspects of this monitoring activity. Various methods are routinely employed for this monitoring, such as counting the consultation rates of general practitioners...
As the Web continues to grow, the pornographic texts in varied forms run rampant on Internet, despite repeated prohibitionsm. It severely does harms to the development of people's mental health and the stability of sociality. In this paper, We propose a novel and effective feature weight-value calculating method named FEFP for such objectionable information, and design a scheme to auto-build system...
This paper describes the development and testing of a prototype home ECG (electrocardiograph) system that obtains patient's ECG signal and processes it to determine related parameters using LabVIEW software. These parameters include heart rate, PR-interval, QT-interval, ST-interval, P-wave and T-wave durations. The recorded ECG signal and related parameters are transmitted over the Internet to the...
Malicious software is rampant on the Internet and costs billions of dollars each year. Safe and thorough analysis of malware is key to protecting vulnerable systems and cleaning those that have already been infected. Most current state-of-the-art analysis platforms run alongside the malware, increasing their detectability. This reduces the value of analysis because some malware is known to behave...
Some network anomalous events caused by same reason (e.g., DDoS, link failure) tend to present similar unusual change on multiple traffic observations, and this part of traffic usually exhibits anomalous features either on time or frequency domain. Motivated by this fact, this paper introduces a multidimensional traffic anomaly detection method based on independent component analysis (ICA). Considering...
The randomness of the network behaviors poses serious challenges for discovering the abnormal patterns in network traffic flows. This paper presents a method based on blind source separation approach for detecting abnormal traffic flows. It decomposes the network traffic into two components: the routine pattern and the abnormal pattern. The scale-space filter with adaptive scale is applied to filter...
Botnet detection has attracted lots of attention. But few works have considered online detection. In this paper, we propose a novel approach that can monitor the botnet activities in real time. We define the concept of ldquofeature streamrdquo to describe raw network traffics. If some feature streams show high similarities, the corresponding host will be regarded as suspicious bots which will be added...
With the rising popularity of the Internet, the resulting increase in the number of available vulnerable machines, and the elevated sophistication of the malicious code itself, the detection and prevention of unknown malicious codes meet great challenges. Traditional anti-virus scanner employs static features to detect malicious executable codes and is hard to detect the unknown malicious codes effectively...
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