Video surveillance units are usually the first element of a security system. While they are the most intuitive to understand and can be programmed for many tasks, they also have many vulnerabilities, such as sensitivity to light levels and large computational requirements. The article presents an application of computer vision methods to traffic flow monitoring and road traffic analysis. The application is utilizing image-processing methods using OpenCV library designed and modified to the needs of road traffic analysis. This method gives functional capabilities of the system to monitor the road, to initiate automated vehicle tracking, to measure the average speed. This system is based on stationary video cameras and an onboard arm processor running on android or linux processing the video and sending the processed data to a central server connected to wide area network or through GPRS. This software was made to save users time so that they don’t get stuck in traffic jams and avoid them beforehand as they have prior knowledge about the traffic conditions on the alternate routes, so that users could choose the route with least traffic and hence shall reach his/her destination hassle free. What users need to do is, subscribe to the routes (from their android phone) in a particular area, our software will tell the user about the traffic density in each of the alternate routes available in that area and suggest the best route with least traffic to the user. Therefore by using our software from any android device, the user will enjoy a hassle free ride towards his destination.

A practical, community-driven checklist for pentesting MCP servers. Covers traffic analysis, tool-call behavior, namespace abuse, auth flows, and remote server risks. Maintained by Appsecco and licensed for remixing.

This repository contains software for multi-agent simulation model of mixed traffic flow of connected (HVs) and automated vehicles (AVs) in Python using pygame, matplotlib, numpy, scipy and seaborn libraries. The software is capable of simulating many different cases of traffic flow and creates data files and figures for the purpose of analysis.

Intrusion Detection Systems (IDSs) and Intrusion Prevention Systems (IPSs) are the most important defense tools against the sophisticated and ever-growing network attacks. Due to the lack of reliable test and validation datasets, anomaly-based intrusion detection approaches are suffering from consistent and accurate performance evolutions. Our evaluations of the existing eleven datasets since 1998 show that most are out of date and unreliable. Some of these datasets suffer from the lack of traffic diversity and volumes, some do not cover the variety of known attacks, while others anonymize packet payload data, which cannot reflect the current trends. Some are also lacking feature set and metadata. CICIDS2017 dataset contains benign and the most up-to-date common attacks, which resembles the true real-world data (PCAPs). It also includes the results of the network traffic analysis using CICFlowMeter with labeled flows based on the time stamp, source, and destination IPs, source and destination ports, protocols and attack (CSV files). Also available is the extracted features definition. Generating realistic background traffic was our top priority in building this dataset. We have used our proposed B-Profile system (Sharafaldin, et al. 2016) to profile the abstract behavior of human interactions and generates naturalistic benign background traffic. For this dataset, we built the abstract behaviour of 25 users based on the HTTP, HTTPS, FTP, SSH, and email protocols. The data capturing period started at 9 a.m., Monday, July 3, 2017 and ended at 5 p.m. on Friday July 7, 2017, for a total of 5 days. Monday is the normal day and only includes the benign traffic. The implemented attacks include Brute Force FTP, Brute Force SSH, DoS, Heartbleed, Web Attack, Infiltration, Botnet and DDoS. They have been executed both morning and afternoon on Tuesday, Wednesday, Thursday and Friday.

Analyze traffic flow with YOLOv8 and ByteTrack: Vehicle detection and tracking, speed estimation, path outlining, and direction analysis at intersections. A comprehensive tool for traffic insights.

This repository contains software for multi-agent simulation model of mixed traffic flow of connected (HVs) and automated vehicles (AVs) in Python using pygame, matplotlib, numpy, scipy and seaborn libraries. The software is capable of simulating many different cases of traffic flow and creates data files and figures for the purpose of analysis. Currently I am working on making the front end of the software more user friendly for potential commercialization.

Tranalyzer generates extended netflow-like flow statistics from large pcap files or extensive ethernet interface measurements. It is intended to serve as a tool for IT troubleshooting, encrypted traffic mining and forensic analysis.

Repository for the paper Network Traffic Classification based on Single Flow Time Series Analysis

Video analysis of traffic using OpenCV and Python

Interactive web-based dashboard to manage traffic flow using YOLOX, DeepSORT

Prediction of travel time has major concern in the research domain of Intel- ligent Transportation Systems (ITS). Clustering strategy can be used as a powerful tool of discovering hidden knowledge that can easily be applied on historical traffic data to predict accurate travel time. In our Modified K-means Clustering (MKC) approach, a set of historical data is portioned into a group of meaningful sub- classes (also known as clusters) based on travel time, frequency of travel time and velocity for a specific road segment and time group. The information from these are processed and provided back to the travellers in real time. Traffic flow modelling and driving condition analysis have many applications to various areas, such as Intelligent Trans- portation Systems (ITS), adaptive cruise control, pollutant emissions dispersion and safety.

PacketHawk is a lightweight Python-based network sniffer that captures and analyzes live network traffic, providing visibility into packets, protocols, and communication flows for security analysis and learning.

基于 YoLov8 与 Tesser act-OCR 的车流量分析系统

Our platform utilizes advanced technologies to optimize traffic flow and minimize congestion at intersections. Through real-time data analysis and adaptive signal control systems, we empower traffic authorities to make informed decisions and dynamically adjust signal timings,

The vehicle counting procedure offers accurate data on traffic flow, vehicle collisions, and traffic peak times on highways. Using digital image processing technologies on traffic camera video outputs is an acceptable strategy for achieving these goals. The Kalman filter is used to create a vehicle counter-classifier based on a mixture of video-image processing methods such as object detection, edge detection, frame distinction, and the Kalman filter. The proposed technique has been implemented using the Python programming language. The method's accuracy in vehicle counts and classification was assessed, yielding a classification accuracy of around 95% and a vehicle detection target error of about 4%. Vehicle Counting, Vehicle Detection, Traffic Analysis, Object Detection, Video-Image Processing are some of the terms used in this paper.

Lane-wise Traffic Vehicle Counter using YOLOv8 & Python

Analysis of the traffic flow in the cities of Bristol and Cincinnati, considering data gathered by Uber Movement.

UrbanFlow applies machine learning to analyze urban traffic data, using clustering, regression, and classification to predict congestion levels, identify high-risk areas, and uncover key traffic patterns. The system combines K-Means, Random Forest, and XGBoost with feature engineering and PCA for accurate, data-driven urban mobility insights.

XDPBroker: A High-Performance XDP/eBPF Packet Broker for Flow-Based Distribution of VXLAN-Encapsulated Traffic for Network Analysis

An in-depth analysis of traffic flow.; How containers communicate on the same node or multi-node, packets headers content, filter based on packet header fields;

This project provides an integrated environment with tools to study traffic flow in Teatinos, Málaga. It automatically collects, refines, and stores traffic information for effective historical analysis. Additionally, it uses an Agent-Based Model (ABM) to simulate traffic behavior.

Flow-a-gorithm is an algorithm-centric framework for detecting anomalies in network traffic through graph-based modeling and subgraph analysis. This project combines subgraph isomorphism, incremental detection algorithms, and a Graph Convolutional Network (GCN) to identify suspicious patterns in dynamic network flow data.

Traffic Analysis of Fluctuating Flows from PCAP data

Tor traffic analysis platform for extracting, classifying, and visualizing Tor network flows from PCAPs

Developed a GIS-based evacuation route optimization tool using Python libraries (GeoPandas, NetworkX, Folium). Implemented machine learning and geospatial analysis for dynamic route planning, enhanced with real-time data visualization and traffic flow simulations for emergency scenarios.

Traffic Flow Analysis example

🚦 TrafficVision: AI-Powered Traffic Density Detection TrafficVision is an AI-based traffic monitoring system that uses OpenCV and Deep Learning to analyze video footage and detect vehicles in real time. This project leverages a pre-trained SSD MobileNet model to count vehicles and estimate traffic density.

Programmable tool for protocol simulation and traffic flow analysis in peer-to-peer networks

Analysis the route of network traffic flow(netflow packets) based on the network topology , support both ospf&bgp and isis network

This IPFIX (IP Flow Information Export) Generator is a tool designed to create and send IPFIX traffic for testing, demonstration, and analysis purposes.