This project explored a new approach in detecting vehicles on a roadway by making a roadway section itself a traffic flow detector. Sections of a given roadway are paved with carbon-nanotube (CNT)/cement composites; the piezoresitive property of carbon nanotubes enables the composite to detect the traffic flow. Meanwhile, CNTs can also work as the reinforcement elements to improve the strength ...

Detection of traffic congestion is an important issue both for the transportation research community and everyday life of the motorists. Remote sensing sensors installed on aircrafts or satellites enable information collection for various traffic applications over large areas. Optical systems are already in use but are limited due to their daylight operation and cloud-free conditions requiremen...

We propose a router-based technique to mitigate the stealthy reduction of quality (RoQ) attacks at the routers in the Internet. The RoQ attacks have been shown to impair the QoS sensitive VoIP and the TCP traffic in the Internet. It is difficult to detect these attacks because of their low average rates. We also show that our generalized approach can detect these attacks even if they employ the...

This paper describes a novel domain-aware anomaly detection system for in-car CAN bus network traffic. Through inspection of real CAN bus communication, we were able to split the messages into fields and identify the field types, without any prior knowledge of the message formats. We discovered the presence of Constant fields, Multi-Value fields and Counter or Sensor fields. Next we developed a...

Road sign detection and tracking is one of the major tasks in a visual driver-assistance system. This paper describes an approach to detecting and tracking road signs appearing in complex traffic scenes. In the detecting phase, two neural networks are developed to extract color and shape features of traffic signs, respectively, from scenes images. A process characterized by fuzzy-set discipline...

We present a payload-based anomaly detector, we call PAYL, for intrusion detection. PAYL models the normal application payload of network traffic in a fully automatic, unsupervised and very effecient fashion. We first compute during a training phase a profile byte frequency distribution and their standard deviation of the application payload flowing to a single host and port. We then use Mahala...

This work is focused on the application of high order traffic flow theory to the problem of traffic incident detection, and for modeling multiclass traffic flow composed of different vehicle types. For incident detection applications, a class of generic second order traffic flow models (GOSM) is applied to detect traffic incidents in real time by posing the problem as a hybrid state estimation ...

In this paper, the problem of measuring normality in HTTP traffic for the purpose of anomaly-based network intrusion detection is addressed. The work carried out is expressed in two steps: first, some statistical analysis of both normal and hostile traffic is presented. The experimental results of this study reveal that certain features extracted from HTTP requests can be used to distinguish an...