Fast and accurate network traffic identification is becoming essential for network management, high quality of service control and early detection of network traffic abnormalities. Techniques based on statistical features of packet flows have recently become popular for network classification due to the limitations of traditional port and payload based methods. In this paper, we propose a metho...

The objective of this work describes a method for Traffic sign detection and recognition from the traffic panel board(signage). It detect the traffic signs especially for Indian conditions. Images are acquired through the camera and it is invariant to size then it is scaled. It consist of the following steps, first, it detect the traffic sign, if it has sufficient contrast from the background t...

Clustering, as a kind of data mining methods, with the characteristic of no supervising, quick modeling is widely used in intrusion detection. However, most of the traditional clustering algorithms use a single data point as a calculating unit, and the drawback exists in time wasting to calculate one data point after another when clustering, meanwhile, a single local change of data will signifi...

Integrated Network-Based Ohio University Network Detective Service (INBOUNDS) is a network based intrusion detection system being developed at Ohio University. The Anomalous Network-Traffic Detection with Self Organizing Maps (ANDSOM) module for INBOUNDS detects anomalous network traffic based on the Self-Organizing Map algorithm. Each network connection is characterized by six parameters and s...

As a kind of stream data mining method, stream clustering has great potentiality in areas such as network traffic analysis, intrusion detection, etc. This paper proposes a novel grid-based clustering algorithm for stream data, which has both advantages of grid mapping and DBSCAN algorithm. The algorithm adopts the two-phase model and in the online phase, it maps stream data into a grid and the ...

Vision-based object detection is one of the fundamental functions in numerous traffic scene applications such as self-driving vehicle systems and advance driver assistance systems (ADAS). However, it is also a challenging task due to the diversity of traffic scene and the storage, power and computing source limitations of the platforms for traffic scene applications. This paper presents a gener...

The main aim in network anomaly detection is effectively spotting hostile events within the traffic pattern associated to network operations, by distinguishing them from normal activities. This can be only accomplished by acquiring the a-priori knowledge about any kind of hostile behavior that can potentially affect the network (that is quite impossible for practical reasons) or, more easily, b...

The deployment and use of Anomaly Detection (AD) sensors often requires the intervention of a human expert to manually calibrate and optimize their performance. Depending on the site and the type of traffic it receives, the operators might have to provide recent and sanitized training data sets, the characteristics of expected traffic (i.e. outlier ratio), and exceptions or even expected future...