After the Stuxnet security event in Iran, the security issues on industrial Internet are very serious. Besides, there are many flaws existing in the modern traffic modelling approaches to the industrial field network. Aiming at these problems, the traffic characteristic map-based intrusion detection model for industrial Internet was proposed. Firstly, information entropy method was adopted to s...

Advanced Driver Assistance Systems rely on automated traffic sign recognition. Today, Deep Learning methods outperform other approaches in terms of accuracy and processing time; however, they require vast well-curated data sets for training. In this paper, we present the Austrian Highway Traffic Sign Data Set (ATSD), a comprehensive annotated set images almost all signs highways 2014, correspon...

This paper presents a color space based algorithm for traffic signal light detection for the modules used in Advanced Driver Assistance Systems (ADAS). The autonomous vehicle has been a topic for the discussions among the computer science engineers for several years. Traffic Signal Light Detection algorithm is based on color space theory which efficiently detects the color of traffic light duri...

Network-based intrusions have become a serious treat to the users of the Internet. To help cover their tracks, attackers launch attacks from a series of previously compromised systems called stepping stones. Timing correlations on incoming and outgoing packets can lead to detection of the stepping stone and can be used to trace the attacker through each link. Existing approaches, however, delib...

Currently, flow-level anomaly detection systems get widely deployed in ISP networks to provide fast detection in case of large-scale anomalies such as worms, denial-of-service attacks, or flash crowds. Unfortunately, benchmark evaluation traces which would allow for systematically evaluating these anomaly detection systems are not available to neither research nor industry. In this paper, we id...

Automatic incident detection and characterization is urgently require in the development of advanced technologies used for reducing non-recurrent traffic congestion on urban traffic. This paper presents a new method using data mining to identify automatically freeway incidents. As a component of a real-time traffic adaptive control system for signal control, the algorithm feeds an incident repo...

With the rapid development of the Internet and the continuous expanding of the data network, little potential anomaly can seriously affect the normal operation of the network, and even lead to huge economic losses. In order to be more accurate and efficient in the traffic detection, in this paper, we propose an N-ARMA based traffic anomaly detection model. We also conduct extensive experiments ...

Robust and reliable traffic surveillance system is an urgent need to improve traffic control and management. Vehicle flow detection appears to be an important part in surveillance system. The traffic flow shows the traffic state in fixed time interval and helps to manage and control especially when there’s a traffic jam. In this paper, we propose a traffic surveillance system for vehicle counti...

Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation I...

Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or prohibit certain actions. A fast real-time and robust automatic traffic sign detection and recognition can support and disburden the driver and significantly increase driving safety and comfort. Automatic recognition of traffic signs is also important for automated intelligent driving vehicle or driv...