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This study presents a malware classification system designed to classify malicious processes at run-time on production hosts. The system monitors process-level system call activity and uses information extracted from system call traces as inputs to the classifier. The system is advantageous because it does not require the use of specialized analysis environments. Instead, a ‘lightweight’ service application...
Classifying instances in evolving data stream is a challenging task because of its properties, e.g., infinite length, concept drift, and concept evolution. Most of the currently available approaches to classify stream data instances divide the stream data into fixed size chunks to fit the data in memory and process the fixed size chunk one after another. However, this may lead to failure of capturing...
Feature subset selection is a pattern recognition problem which is usually viewed as a data mining enhancement technique. By viewing the imprecise feature values as fuzzy sets, the information it contains would not be lost compared with the traditional methods. Optimal fuzzy-valued feature subset selection (OFFSS) is a technique for fuzzy-valued feature subset selection. The core of OFFSS is the heuristic...
“Gain-Based Separation” is a novel heuristic that modifies the standard multiclass decision tree learning algorithm to produce forests that can describe an example or object with multiple classifications. When the information gain at a node would be higher if all examples of a particular classification were removed, those examples are reserved for another tree. In this way, the algorithm performs...
In this paper, an algorithm of learning computational verb decision trees (verb trees, for short) from training examples base on impact factors, which are calculated by using computational verb similarities, is presented. Some examples are used to show the creation of verb decision tree and the usefulness of verb decision trees. Examples are used to show that verb decision trees are powerful tools...
Early ID3, C4.5, CART and the other decision tree algorithms are no longer met the situation of massive data analysis for the time being. Those algorithms has the same limitations that they can not handle the updated data sets dynamically and the decision tree generated by these algorithms need to be purned. These weaknesses limit the use of the above-mentioned algorithms. So a novel parallel decision...
A fast pruning algorithm for an Efficient Adaptive Fuzzy Neural Network (EAFNN) is presented in this paper. An EAFNN is a Takagi-Sugeno-Kang (TSK) type fuzzy model which is functionally equivalent to the Ellipsoidal Basis Function (EBF) neural network. An EAFNN uses the combined pruning algorithm where both Error Reduction Ratio (ERR) method and a modified Optimal Brain Surgeon (OBS) technology are...
Fuzzy decision tree is generally considered as an extension of crisp decision tree. The algorithms used in fuzzy decision tree induction are often the extended form of those used in crisp decision tree induction. In this paper, the problem is considered from the converse way and a new method is proposed to induce crisp decision tree. One fuzzy decision tree induction algorithm based on classification...
Hyper-heuristics are increasingly used in function and combinatorial optimization. Rather than attempt to solve a problem using a fixed heuristic, a hyper-heuristic approach attempts to find a combination of heuristics that solve a problem (and in turn may be directly suitable for a class of problem instances). Hyper-heuristics have been little explored in data mining. Here we apply a hyper-heuristic...
Decision tree is one kind of inductive learning algorithms that offers an efficient and practical method for generalizing classification rules from previous concrete cases that already solved by domain experts. It is considered attractive for many real life applications, mostly due to its interpretability. Recently, many researches have been reported to endow decision trees with incremental learning...
Recently, traffic classification becomes more and more important for network management and measurement tasks. In this paper, we make a first step towards dynamic online traffic classification using data stream mining method. Two main contributions are as follows. Firstly, we propose a novel integrated dynamic online traffic classification framework, called DSTC (data stream based traffic classification)...
As designing practical algorithms of learning from examples, one has to deal with some optimization problems. The major optimization problems are: the smallest feature subset selection, the smallest decision tree induction, and the smallest k-DNF induction. In this paper, we show that all these optimization problems listed as above are NP-hard, and we present new greedy algorithms for solving these...
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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