Framework For Traffic Monitoring Research Articles

An Efficient and Privacy-Preserving Information Reporting Framework for Traffic Monitoring in Vehicular Networks

Traffic monitoring services show great potential for improving traffic efficiency in vehicular networks. With the assistance of 5G innovation, vehicles can report information such as driving misbehavior and traffic status to the traffic monitoring server which then returns feedback to the network to enhance the efficiency of traffic management. However, some challenging issues in information reporting need to be addressed first. Trivial reported information significantly increases the workload of the server. Moreover, a server is difficult to distinguish the truth of reported messages. As a result, the server is incapable of returning immediate feedback to the network, thereby reducing the quality of traffic services. Meanwhile, uploading location-based information to the server exposes vehicles' privacy. Although some existing techniques such as trust management benefit information reporting, a complete framework is still missing. In this paper, we propose an information reporting framework for solving both efficiency and privacy issues. Specifically, messages are probabilistically reported to the server depending on their importance. Furthermore, the DAG-based blockchain ensures that messages reported for a specific event are all gathered in the same ledger. We also present a scheme based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">geo-indistinguishability</i> to protect the location privacy of vehicles. Extensive experiments demonstrate that the proposed framework preserves the location privacy of vehicles and achieves efficiency with acceptable overheads.

Federated Deep Reinforcement Learning for Traffic Monitoring in SDN-Based IoT Networks

This paper proposes a novel traffic monitoring framework, namely, DeepMonitor, for SDN-based IoT networks to provide fine-grained traffic analysis capability for different IoT traffic types at the network edges. Specifically, we first develop an intelligent flow rule match-field control system, called DeepMonitor agent, for SDN-based IoT edge nodes, taking different granularity-level requirements and their maximum flow-table capacity into consideration. We then formulate the control optimization problem for each edge node employing the Markov decision process (MDP). Next, we develop a double deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -network (DDQN) algorithm to quickly achieve the optimal flow rule match-field policy. Moreover, we propose a federated DDQN-based traffic monitoring mechanism to significantly improve the learning performance of the edge nodes. The results obtained through extensive emulations show that by applying the DeepMonitor, the flow-table overflow problem at the edge nodes can be completely bypassed. The average number of match-fields in a flow rule achieved by DeepMonitor is increased by approximately 37% (for medium and diverse granularity-level requirements) and 41.9% (for high granularity-level requirement) compared to that of an existing solution, i.e., FlowStat. Finally, by adopting DeepMonitor, the DDoS attack detection performance of an intrusion detection system can be enhanced by up to 22.83% compared with that of FlowStat.

Employing Real-Time Object Detection for Traffic Monitoring

The population rate in India is increasing at a fast pace. Also, this increasing number is the main driver of various issues, one of which is rise in traffic in the past few decades, prompting to inadequately managed traffic checking thus leading to imprudent drivers. Controlling such a high density of traffic, halting the defaulters and fining each one of them would end up being a very challenging piece of work, given that such an activity has to be performed with exactness is difficult. It would require an enormous number of personnel, who can instead be deployed for better and more demanding undertakings, to reach a high precision. When we discuss precision and speed in connection with unpredictability, we can place some confidence in machines and for undertakings of higher nature which require recognition of objects, highlight extraction and prediction, we can set ourselves towards deep learning. This paper proposes a brilliant traffic monitoring framework, intended to call attention to the defaulters, and fine them as per the standard they break. The TensorFlow library, later integrated with the Keras library, provides multiple types of layers of neural networks for differently demanding tasks. Our model utilizes the TensorFlow library for deep learning and the OpenCV library for the vision. In order to perform object detection, image cropping and return the bounding boxes, our model employs the YOLOv4 algorithm. When the step of sifting the regions of interest is done, these regions are now sent to the different classification models in order to be used for performing classification between the defaulters and the non-defaulters. LeNet and AlexNet have been utilized as they give a reliable performance in the grouping of small images of low resolution as they were initially worked upon to classify penmanship styles. The LeNet has been used to group the bikers not wearing head protectors from bikers wearing head protectors and the AlexNet has been used to distinguish the drivers with the safety belts on from drivers without the safety belts on. The model that arranges bikers wearing or not wearing helmets utilizes the LeNet classifier while the model that groups the car drivers with their safety belts on from the car drivers without the safety belts on employs the AlexNet architecture as it is a marginally more precise model as mentioned above and results in higher classification accuracy of the lower quality car driver pictures. Our model has been trained utilizing Mini-Batch Gradient Descent using the Adam optimizer with a batch size of 10 with the division of the dataset using five-fold method where the training set constitutes 80% of the dataset while the testing set constitutes rest of the 20% of the dataset. The dataset for training the LeNet architecture constitutes 300 images of bikers with and without helmets in a ratio of 1:1 while the dataset that has been used to train the AlexNet classifier consists of 300 images of drivers with and without their seatbelts on also in a ratio of 1:1. The AlexNet shows an accuracy of 93.6% while the LeNet design gives an accuracy of 94.7%. The scripts which utilize the model to recognize the objects and provide class of the objects that are a necessity in building a traffic monitoring system such as helmet on a motorist’s head or a safety belt for a car driver have been written in python. The recognized image category is extracted and stored. This system can successfully provide information about detected entities and thus ensure proper traffic maintenance. Therefore, the developed system provides aid in proper traffic management and giving its part in order to lead us towards becoming a more technologically developed society using real-time object detection and identification technology.

Multi agent based Framework for Traffic monitoring in VANET

For last many years studies have been carried out to inculcate technical advancement in the field of VANET traffic monitoring systems. Rapidly growing population and number of vehicles have increased the probability of network congestion, traffic jams and road accidents. To overcome these hazardous situations, series of technologies have been implemented on vehicle infrastructure in recent years. Vehicles are equipped with intelligent on board equipments and communication is facilitated between vehicle and roadside units to enhance road safety. In this paper multi agent base framework is proposed to increase coordination and synchronization among vehicles in VANETs.

An Improved Framework for Incident Handling

ABSTRACT Information is regarded as a resource with the highest organizational value. Every organization needs a secured way to transmit information over the network. When the network grows, the security becomes a major concern. As a result, a traffic monitoring frame work is required to understand the security risks, to handle the security breaches, to design the security policy, and to provide an effective business continuity plan in case of cyber disasters. In this paper, an Internet traffic monitoring framework has been proposed which handles incidents in a better way and consists of three interdependent layers. Layer 1 comprises the stakeholders involved in implementing the framework. Layer 2 is the core layer which provides the mechanism for its implementation, and Layer 3 shows the outcomes of the Internet Traffic Monitoring Framework. The proposed framework has been designed to ease the work of the Internet Service Provider and to provide a reliable and systematic way of incident handling by monitoring the Internet traffic to combat cyber crimes, cyber terrorism, and cyber disasters in the Republic of Mauritius. This framework also provides a foundation for the overall security management at Computer Emergency Response Team (CERT), Mauritius.

Data-Provenance Verification For Secure Hosts

Malicious software typically resides stealthily on a user's computer and interacts with the user's computing resources. Our goal in this work is to improve the trustworthiness of a host and its system data. Specifically, we provide a new mechanism that ensures the correct origin or provenance of critical system information and prevents adversaries from utilizing host resources. We define data-provenance integrity as the security property stating that the source where a piece of data is generated cannot be spoofed or tampered with. We describe a cryptographic provenance verification approach for ensuring system properties and system-data integrity at kernel-level. Its two concrete applications are demonstrated in the keystroke integrity verification and malicious traffic detection. Specifically, we first design and implement an efficient cryptographic protocol that enforces keystroke integrity by utilizing on-chip Trusted Computing Platform (TPM). The protocol prevents the forgery of fake key events by malware under reasonable assumptions. Then, we demonstrate our provenance verification approach by realizing a lightweight framework for restricting outbound malware traffic. This traffic-monitoring framework helps identify network activities of stealthy malware, and lends itself to a powerful personal firewall for examining all outbound traffic of a host that cannot be bypassed.