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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,...
The cheap and low-quality sensor devices are usually used for event detection in Internet of Things (IoT), and they put limitations on power, memories and computing capabilities. Those limitations need to be considered while designing our outlier detection algorithm. In this paper, we try to present an adaptive online outlier detection algorithm to handle data measurements for event detection. Namely,...
The integrators are facing the problems of so many decision attributes and few data samples for decision-making analysis when evaluating the partners of collaboration and innovation in complex products and systems(CoPS). Firstly, this paper created a trust evaluation model of collaborative partners in CoPS. Secondly, followed by the application of RS attribute reduction as a data pre-processing removes...
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...
Against the low efficiency of training on large-scale SVM, a reduction approach is proposed. This paper presents a new samples reduction method, called bistratal reduction method (BRM). BRM has two levels. The first level is coarse-grained reduction. It deletes the redundant clusters with KDC reduction. The second level is fine-grained reduction. It picks out the support vectors from the clusters...
Distributed denial of service (DDoS) attacks is one of the major threats to the current Internet. After analyzing the characteristics of DDoS attacks and the existing approaches to detect DDoS attacks, a novel detection method based on conditional entropy is proposed in this paper. First, a group of statistical features based on conditional entropy is defined, which is named Traffic Feature Conditional...
The success of any Intrusion Detection Systems (IDSs) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with irrelevant and redundant features. How to choose the effective and key features is very important topic for an intrusion detection problem. Support vector machine (SVM) has been employed to provide potential solutions for the IDSs...
In order to solve the problem that algorithm SVM (Support Vector Machine) is very slowly for intrusion detection systems, a novel algorithm based on SVM divided up by clusters was proposed. In the method, Training set is divided into many subsets by clustering algorithm, and these subsets are classified by the decision function SVM. Detection Experiments with the algorithm on intrusion detection data...
We propose a semi supervised classifier for intrusion detection. In our approach, we classify the data entering the computer network. To achieve this, we start with two broad classes of data namely, malicious data and good data. We use Support vector machine based classifier with spherical decision boundaries to classify a chosen subset of malicious data taken as training samples. In the Intrusion...
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 development and popularization of computer network, network security problems increasingly bring into prominence. Intrusion detection technique can effectively enlarge the scope of protection on network and system. An intrusion detection method based on support vector machine (SVM) is studied. Aiming at the shortcoming of SVM on detecting precision, an intrusion detection model based on improved...
In this paper, we investigate imposture using synthetic speech. Although this problem was first examined over a decade ago, dramatic improvements in both speaker verification (SV) and speech synthesis have renewed interest in this problem. We use a HMM-based speech synthesizer which creates synthetic speech for a targeted speaker through adaptation of a background model. We use two SV systems: standard...
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...
Nowadays, challenged by malicious use of network and intentional attacks on personal computer system, intrusion detection system has become an indispensible and infrastructural mechanism for securing critical resource and information. Most current intrusion detection systems focus on hybrid supervised and unsupervised machine learning technologies. The related work has demonstrated that they can get...
This paper proposed a new algorithm of multi-category SVM incremental learning by analyzing the distribution characteristics of the intrusion detection data. Samples used in learning were selected by measuring the distance between samples and their class-centers, and they are just those samples which will most possibly be the SVs in incremental learning. By several binary-class hyper-planes, the zones...
It is important that the training time of the Support Vector Machine (SVM) is shortened and storage space requirement is reduced for high-speed and large-scale network. An intrusion detection method based on parallel SVM is proposed and a detection model system is constructed in this paper. First, original training dataset gained from network is divided into three subsets according to the network...
In order to share the knowledge of intrusion among distributed hosts and make the intrusion detect packages more efficient and reliable, a framework of distributed incremental intrusion detection based on SVM is proposed in the study. In this framework, the locate SVM detects the local attacks and take charge of collecting the new typical samples. A center SVM summarizes the distributed samples and...
Intrusion detection technology is a key research direction in information technology. For intrusion detection method based support vector machine (SVM), there is a big obstacle that the amount of audit data for modeling is very large even for a small network scale, so it's impractical to directly train SVM using original training datasets. Selecting important features from input dataset leads to a...
Intrusion detection is still a crucial issue for network security. Support vector machine (SVM) has been successfully applied in intrusion detection systems. However, for further improvement in performance, data dimension reduction should have drawn special attention. This paper proposes a scheme using popular non-linear dimension reduction tool Isomap and one-class support vector machine to detect...
When collecting network connection information, we can not obtain a complete data set at once, which result in SVM training insufficiently and high error rate of prediction. To solve this problem, this paper proposes a new method that combines support vector machine with clustering algorithm, based on analyzing the relation between boundary support vectors and KKT condition. In the method, firstly,...
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