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DBSCAN is one of powerful density-based clustering algorithms for detecting outliers, but there are some difficulties in finding its parameters (epsilon and minpts). Currently, there is also no way to use DBSCAN with different parameters for different cluster when it is applied to anomaly detection when network traffic includes multiple traffic types with different characteristics. In this paper,...
Clinical electroencephalography (EEG) is routinely used to monitor brain function in critically ill patients, and specific EEG waveforms are recognized by clinicians as signatures of abnormal brain. These pathologic EEG waveforms, once detected, often necessitate accute clinincal interventions, but these events are typically rare, highly variable between patients, and often hard to separate from background,...
Fraud is increasing with the extensive use of internet and the increase of online transactions. More advanced solutions are desired to protect financial service companies and credit card holders from constantly evolving online fraud attacks. The main objective of this paper is to construct an efficient fraud detection system which is adaptive to the behavior changes by combining classification and...
Security of computers and the networks that connect them is increasingly becoming of great significance. As an effect, building effective intrusion detection models with good accuracy and real-time performance are essential. In this paper we propose a new data mining based technique for intrusion detection using Cost-sensitive classification and Support Vector Machines. We introduced an algorithm...
This paper explores online learning and batch algorithms for detecting malicious Web sites (those involved in criminal scams) using lexical and host-based features of the associated URLs. A data set has been built including malicious and benign URLs, and data mining system Weka has been used as an aid to classify the existent URLs and new coming URLs and evaluate the classification algorithms. A real-time...
It is estimated that over 8 million cell phones are lost or stolen each year [7]; often the loss of a cell phone means the loss of personal data, time and enormous aggravation. In this paper we present machine-learning based algorithms by which a cell phone can discern that it may be lost, and take steps to enhance its chances of being successfully recovered. We use data collected from the Reality...
This paper presents a neural-network-based active learning procedure for computer network intrusion detection. Applying data mining and machine learning techniques to network intrusion detection often faces the problem of very large training dataset size. For example, the training dataset commonly used for the DARPA KDD-1999 offline intrusion detection project contained approximately five hundred...
Fuzzy classifiers and fuzzy rules are powerful tools in data mining and knowledge discovery. In this work, intrusion detection is approached as a data mining task and genetic programming is deployed to evolve fuzzy classifiers for detection of intrusion and security problems. We train the fuzzy classifier on a data set modeled as a fuzzy information retrieval collection and investigate its ability...
Aim at the low accuracy of intrusion detection system, to analysis the Bayesian classification algorithm and give some improvements, with the experimental data of kddcup99, in order to find a reasonable data pre-processing methods and more effective classification algorithm to improve the accuracy of intrusion detection system.
Multi-documents summarization is an important research area of NLP. Most methods or techniques of multi-document summarization either consider the documents collection as single-topic or treat every sentence as single-topic only, but lack of a systematic analysis of the subtopic semantics hiding inside the documents. This paper presents a Subtopic-based Multi-documents Summarization (SubTMS) method...
A bug-tracking system such as Bugzilla contains bug reports (BRs) collected from various sources such as development teams, testing teams, and end users. When bug reporters submit bug reports to a bug-tracking system, the bug reporters need to label the bug reports as security bug reports (SBRs) or not, to indicate whether the involved bugs are security problems. These SBRs generally deserve higher...
For solving the problem of less information getting about unknown intrusions in anomaly detection, a model based on hybrid SVM/SOM is proposed. Firstly, C-SVM is used to find out the anomalous connections, and then, a packet filtering scheme is used to remove the known intrusions, which is performed by one-class SVM, after that, the identified unknown intrusions are projected onto the output grid...
With the rapid development of the Internet services and the fast increasing of intrusion problems, the traditional intrusion detection methods cannot work well with the more and more complicated intrusions. So introducing machine learning into intrusion detection systems to improve the performance has become one of the major concerns in the research of intrusion detection. Intrusion detection systems...
As the network dramatically extended, security considered as major issue in networks. Internet attacks are increasing, and there have been various attack methods, consequently. Intrusion detection systems have been used along with the data mining techniques to detect intrusions. In this work we aim to use data mining techniques including classification tree and support vector machines for intrusion...
This paper proposes Modified Ant Miner algorithm for intrusion detection. Ant Miner and its descendant have produced good result on many classification problems. Data mining technique is still relatively unexplored area for intrusion detection. In this paper, modification has been suggested in basic ant miner algorithm to improve accuracy and training time of algorithm. The KDD Cup 99 intrusion data...
In order to detect, identity and hold up network attacks, a network intrusion detection system based on rough set theory and multiclass linear support vector machine (linear SVM) is in this article. The system makes the most of rough set theory and linear SVM to reduce the redundancies of data sets and improve the detection rate of EDS. The simulation experiment shows this approach has higher ratio...
In this paper we describe an experience resulting from the collaboration among data mining researchers, domain experts of the Italian revenue agency, and IT professionals, aimed at detecting fraudulent VAT credit claims. The outcome is an auditing methodology based on a rule-based system, which is capable of trading among conflicting issues, such as maximizing audit benefits, minimizing false positive...
Detection of execution anomalies is very important for the maintenance, development, and performance refinement of large scale distributed systems. Execution anomalies include both work flow errors and low performance problems. People often use system logs produced by distributed systems for troubleshooting and problem diagnosis. However, manually inspecting system logs to detect anomalies is unfeasible...
Feature selection, structure design and weight training are considered as three key tasks for the application of neural network. Traditional leaning algorithms of neural network, which only optimize one or two aspects of these three tasks, neglect the fact that these three tasks are interdependent and make a united contribution to the performance of neural network. In order to model normal behaviors...
With increasing connectivity between computers, the need to keep networks secure becomes more and more vital. Intrusion detection systems have become an essential component of network security to supplement existing defenses. This paper proposes a novel intrusion detection system, which combines the supervised classifiers and unsupervised clustering to detect intrusions. Decision tree, naive Bayes...
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