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This paper tackles the problem of testing production systems, i.e. systems that run in industrial environments, and that are distributed over several devices and sensors. Usually, such systems lack of models, or are expressed with models that are not up to date. Without any model, the testing process is often done by hand, and tends to be an heavy and tedious task. This paper contributes to this...
Magnetic Resonance Imaging (MRI) has become an important tool for doctors to diagnose liver cancer for decays. The survival rate of liver cancer patients can be significantly improved by an early diagnosis. In this paper, we present a computer aided kernel based support vector machine (SVM) algorithm for diagnosing liver cancer in early stage by applying our proposed method to the patients' magnetic...
Microarchitectural information regarding various aspects of instruction execution can help processor-level stimuli generators more easily reach verification goals. While many such aspects are based on common microarchitectural concepts, their specific manifestations are highly design-specific. We propose using an automatic method for acquiring such microarchitectural knowledge and integrating it into...
To deal with any possible cases for training anti-spam machine learning models, it is crucial to design a safe way to shrink the size of training sample set via reducing redundancies with minimal information loss for classification as well as make distribution of samples balanced. Presently, there is no such solution to do so. In this paper, we propose a safe approach to address these problems and...
Intrusion detection systems (IDS) protect computer systems by providing alerts which might be caused by malicious attacks. Learning methods were introduced into intrusion detection to automatically improve the performance using history data. Yet high quality data requires heavy labor of experts or expensive monitoring process. Meanwhile, IDS should minimize a nonuniform cost of the misclassification...
The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of...
Signature-based anti-viruses are very accurate, but are limited in detecting new malicious code. Dozens of new malicious codes are created every day, and the rate is expected to increase in coming years. To extend the generalization to detect unknown malicious code, heuristic methods are used; however, these are not successful enough. Recently, classification algorithms were used successfully for...
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