AI-Driven Surveillance & Cybersecurity

@adeelpirzada

Hi, I am Adeel. I worked on an AI system that won an innovation award at Aquacon Dubai, built around anomaly detection on sensor and video data feeding into a single alert view, close to what you are describing here. I would start narrow: one camera feed plus one log source, get a working anomaly model and alert panel, then extend to audio and the SIEM stack once the pipeline proves itself. Happy to do a short written review first, covering approach, model choice and a realistic path to your 2 second latency and 5% false positive targets, yours to keep either way. Which matters more to start, the physical surveillance side or the cybersecurity log analysis?

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@toriquldev123

Hello Dear, I’m Md Toriqul Islam, an experienced full-stack developer specializing in Python, APIs, AI integrations, dashboards, and real-time data processing. I have a few questions: 1. Which VMS and SIEM platforms need integration? 2. Do you have labeled sample video/audio and security logs? 3. Which anomaly types should trigger immediate alerts? I understand you need a unified AI system analyzing video, audio, network, and endpoint data, with real-time anomaly alerts, confidence scores, retraining support, and VMS/SIEM integration. I am skilled in Python, REST APIs, databases, AI integrations, real-time processing, dashboards, and scalable backend development. I can structure the system as modular services, optimize inference pipelines for low latency, and provide deployment, monitoring, and retraining documentation. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@corpmember29

Hello, REAL-TIME AI THAT CORRELATES PHYSICAL AND CYBER ANOMALIES INTO ONE ALERT SYSTEM IS EXACTLY THE TYPE OF PLATFORM I CAN BUILD FOR YOU. I understand the requirement: video/audio feeds and cybersecurity telemetry must be continuously analysed for behavioural anomalies, without face recognition or basic perimeter-only rules. The system should learn normal patterns, identify deviations, correlate events and provide actionable alerts through one dashboard. My approach: • Video + audio anomaly detection using Python, PyTorch/TensorFlow • Network logs, traffic flows and endpoint telemetry ingestion • Common anomaly engine with configurable detection thresholds • REST/gRPC middleware for VMS and SIEM integration • Real-time alert pipeline targeting sub-2-second notification • Web/Electron dashboard with event details, confidence scores and history • Feedback/retraining pipeline to improve detection over time • Secure APIs, authentication, logging and deployment controls Core flow: VMS/microphones + SIEM/endpoint sources → ingestion layer → preprocessing → AI anomaly models → event correlation → unified alert engine → dashboard/API. I will validate the solution against your sample feeds and one-week log run, measure false positives/latency, and provide deployment and retraining documentation. I have 12+ years of IT experience across AI/ML, cloud, backend, APIs and modern technologies, including end-to-end development of intelligent systems. Thanks, Christina

@devendrathakur12

Hi, I’m Dev Singh, a Principal AI Engineer with 20+ years in AI, ML and real time data systems. I have gone through your specific requirement for multimodal anomaly detection. I would use PyTorch over TensorFlow because the video and audio models need easier experimentation with different encoders before fixing the inference pipeline. I will build Python inference services for video and audio, with a shared event schema for SIEM and VMS data. The API layer will expose REST endpoints while the dashboard shows alert confidence and timing. And I will keep training and inference separate so retraining does not disturb live alerts, at least that is where I would start. Relevant AI project details and screenshots I can send privately. Which VMS, SIEM and endpoint sources are already running? What sample video, audio and log volume can you provide for training? Is the two second latency measured from event capture to dashboard alert? Free for a quick call this week? Or answer those three and I will map the first pipeline. Dev Singh

@ShaikhAneesa

Hi there, I understand the real challenge is unifying physical and cyber security into one intelligence layer that detects abnormal behaviour in real time, rather than maintaining separate surveillance and SIEM alerts. I’m confident I can architect this around a common anomaly-detection pipeline that learns from your environment while keeping your existing VMS and SIEM as connected sources. My approach will be to first profile your video/audio feeds, logs, network flows and endpoint data and define what constitutes normal behaviour for each source. Next, I’ll build the detection layer using Python with PyTorch/TensorFlow where appropriate, combining modality-specific models with a common scoring and alerting layer. Then I’ll develop REST/gRPC middleware to ingest events, normalize signals, attach confidence scores and route alerts into one lightweight dashboard. Finally, I’ll tune thresholds against your sample feeds, measure false positives and latency, and establish a controlled retraining process so the system improves without blindly learning anomalies as normal. You’ll receive the video/audio anomaly models, cybersecurity integration layer, unified API, live alert dashboard, deployment documentation and retraining workflow, validated against your stated latency and false-positive targets. Could you share sample video/audio feeds and the SIEM/VMS platforms so I can assess the integration points and propose the right architecture? Warm Regards, Aneesa.

@ImranAli01

Hello, I went through your project description and understand you need a unified AI anomaly-detection layer that will analyse physical video/audio activity alongside network, endpoint, and security-log data through one consolidated alert system. I’ll build the detection pipeline in Python using PyTorch or TensorFlow, with separate feature-processing components for video, audio, traffic, logs, and endpoint telemetry before normalising their outputs into a common anomaly-scoring layer. The system will focus on behavioural deviations rather than face recognition or basic perimeter rules, with configurable thresholds and feedback mechanisms for future retraining. REST or gRPC middleware will connect the models to your existing VMS and SIEM, while a lightweight web dashboard will display alerts, timestamps, source, anomaly type, and confidence scores. I’ll also implement structured logging, monitoring, controlled retraining, and performance testing against your latency and false-positive requirements. To scope your project clearly, I would like to ask which VMS and SIEM platforms you currently use, and whether sample video, audio, network, and endpoint datasets are already available? A project blueprint will be shared within 24–48 hours of award. Best Regards, Imran

@arslanshahid386

Hi, I understand you're looking to enhance your security system by integrating an AI layer that monitors both your physical premises and network in real time. The focus on behavioral anomaly detection, without relying on face recognition or traditional perimeter rules, is a unique and advanced approach. The primary challenge here is developing a system that accurately learns and adapts to normal behavior patterns, while minimizing false positives. My approach would involve thorough initial assessments to establish baseline behaviors, followed by iterative model training to refine detection accuracy. Ensuring the system remains stable and adaptable as it learns is crucial. I have extensive experience in Python and Linux, and have previously developed AI-driven solutions for real-time anomaly detection in surveillance systems. This experience will be directly applicable to your project. Q1 – What types of video and audio feed formats will the system need to process? Q2 – Are there specific network protocols or data sources that the AI should monitor for anomalies? Q3 – How frequently do you anticipate needing to update the behavioral models as new data is collected? Looking forward to hearing from you.

@techmadigital

Hi, I build full-stack AI systems that turn raw video, audio, and log streams into one usable alerting layer. I would train behavioural anomaly models in Python with TensorFlow or PyTorch, then wrap them in REST or gRPC services so your VMS and SIEM connect cleanly. A lightweight web dashboard would show live alerts with confidence scores, backed by authentication, databases, and notification workflows. I handle integration middleware, testing, and deployment, plus retraining instructions so the models keep improving as data grows. I focus on practical outcomes: one consolidated panel, faster response, and fewer siloed tools. I have delivered similar full-stack SaaS, CRM, and automation work. Thanks, Nikhil

@dataspro

Nice to meet you , It is a pleasure to communicate with you. My name is Anthony Muñoz, I am the lead engineer for DSPro IT agency and I would like to offer you my professional services. I have more than 10 years of working as a Backend and Software developer, I have successfully completed numerous jobs similar to yours therefore, and after carefully reading the requirements of your project, I consider this job to be suitable to my area of knowledge and skills. I would love to work together to make this project a reality. I greatly appreciate the time provided and I remain pending for any questions or comments. Feel free to contact me. Greetings

@naminghn

Hi there, I understand the need for a robust AI layer to monitor both your physical premises and network in real-time, focusing on behavioral anomaly detection without traditional methods like face recognition. For your requirements, I can create models using Python and TensorFlow to analyze video, audio, and log data, ensuring they flag unusual activities effectively and improve over time with incoming data. Additionally, I can develop a REST API for seamless integration with your existing VMS and SIEM, providing a unified alert system. The lightweight dashboard will offer live alerts and confidence scores, along with detailed deployment guidance to facilitate easy retraining. Let’s discuss the specific video and audio environments you aim to monitor to ensure optimal setup. Regards, Ali

@Jennycreation

Hello Sir, I have already worked on many **cryptocurrency trading systems, automated trading platforms, and exchange API integration projects**. I can also show you demos of my previous work. I am a full-time freelancer with **17+ years of experience** in software development, trading automation, and financial API integrations. My relevant experience includes: * Cryptocurrency trading platforms * Binance and Bybit API integration * MT4 and MT5 trading automation * C#-based copy trading software * Arbitrage trading systems (Binance ↔ Bybit) * Automated order execution (Buy/Sell/Square Off) * Portfolio and position management * Margin and risk management features * Real-time market data integration * Trading bots and strategy automation I have already developed a **C#-based Copy Trading Software** where trades from a master account are automatically copied to multiple child accounts. I have also built **arbitrage trading systems** between Binance and Bybit using their official APIs. In addition, I have developed trading platforms using **Zerodha Kite API, Alice Blue API, Angel One API, and Profitmart API**, including features such as order placement, position management, square-off, margin calculation, and automated trading execution. I have also implemented strategy automation using **AmiBroker CSV integration**. I can start working immediately and deliver a secure, scalable, and reliable trading solution based on your requirements. Please contact me in private chat so we can discuss your project in detail. I will be happy to show you **live demos and examples of my previous trading projects**. Looking forward to working with you. Thanks & Regards, Sanjay

@lazarr4

Hello, I can build an AI anomaly-detection system that learns your normal patterns and flags deviations in real time, covering both physical and digital threats. I’ve set up a similar system for a client that reduced false alerts by 30% by combining video motion analysis with audio frequency anomalies. I’d approach this by training separate models for video and audio, then merging their outputs into a single alert stream. The system will use incremental learning to adapt as your environment changes. This will give you a unified view of threats with clear confidence scores for each alert. I’ll also include a simple API wrapper to connect to your existing VMS and SIEM. The biggest benefit will be reducing the time it takes to respond to incidents by automating pattern recognition. I’ll provide a deployment script and a short guide for retraining the models as needed. Once the project is complete, I’ll walk you through the dashboard and verify the alert latency before handing it over. If this approach makes sense, I’d be happy to discuss the details further. Thanks, Lazar.

@pinesucceed01

Hello, As an AI and Machine Learning expert with a speciality in Python programming, I have the skills needed to bring your AI-driven surveillance and cybersecurity solution to life. With over 16 years of experience, I have a deep understanding of multiple programming languages and frameworks that will allow me to implement your vision seamlessly. My experience working with TensorFlow or PyTorch should suit your preference, but I am also open to incorporating purpose-built platforms if it eases deployment without locking you in. Writing clean APIs like REST or gRPC is second nature to me. I can ensure your new surveillance system integrates well with your existing VMS and SIEM. My proficiency extends beyond code; I am also adept at writing comprehensive deployment guides, ensuring effortless implementation of the solution along with basic retraining instructions ensuring adaptability as per new data. Ultimately, our solutions need to demonstrate their value. That's why delivering a live demo on your sample feeds alongside meeting other acceptance criteria is of utmost importance to me. Hiring me means hiring not just a freelancer known for client satisfaction but a committed professional who will go above and beyond to deliver optimal outcomes for your project. Thanks!

@codeandcommand

Real time anomaly detection across cameras and network traffic in one pipeline is the hard part, most setups treat them separately. I would run a lightweight PyTorch model for video anomaly flags and a separate traffic baseline on your Ubuntu box, both feeding one alert layer. Can start today, working prototype in 3 days. Budget and timeline here are a starting point from the post, we will lock in real numbers once we go through your camera count and network setup. Want me to send a quick scope doc so we can get moving?

@miguelcen64

Hello, I can build this as a unified anomaly-detection system for both surveillance and cybersecurity, with Python as the core orchestration layer and PyTorch for the model pipeline. The architecture will separate ingestion, inference, alerting, and retraining so you can plug it into your VMS and SIEM through a clean REST or gRPC API. For the physical side, I would design the video/audio stream processing to learn baseline behaviour and flag unusual activity in real time, rather than relying on face recognition or fixed perimeter rules. On the security side, the same anomaly framework can ingest logs, traffic flows, and endpoint telemetry to drive one consolidated alert panel with confidence scores and triage context. I will also provide deployment guidance, a practical retraining workflow, and a lightweight dashboard that keeps the operator view fast and usable. Which exact video, audio, log, traffic, and endpoint data sources will we integrate first, and what formats do they expose? If you’d like, I can map the end-to-end data flow first so the integration points are clear before implementation. Best regards, Miguel

@vijaydvaswani

Hello What’s the current VMS and SIEM stack you want this integrated with, and do you already have sample video, audio, and security logs available for model validation? I can help build and test the anomaly-detection layer across video, audio, network, and endpoint data, with a unified alert dashboard and REST/gRPC integration. I’d focus heavily on measurable results such as **sub-2-second alert latency, reproducible anomaly scenarios, confidence scoring, and false-positive tracking** rather than simply demonstrating that the models run. Would you be open to starting with a small proof of concept using your sample feeds, with the alert latency and false-positive rate measured against your acceptance criteria? Thanks

@AnjosGustavo

Hi, This strongly matches my Senior ML Engineering background across computer vision, sensor data, anomaly detection, and production AI systems. I’d design this as a unified multimodal pipeline: video/audio ingestion - feature extraction - behavioral anomaly modeling - network/log/endpoint analysis - confidence scoring - alert correlation - consolidated dashboard. My ML experience with video security, object tracking, industrial sensor anomalies, financial patterns, and deep learning is especially relevant to handling very different, noisy data streams. The architecture would remain modular, with REST/gRPC APIs for VMS/SIEM integration and PyTorch/TensorFlow models that can be retrained as normal behavior evolves. I’d benchmark latency and false-positive rates against your sample data rather than assume them, then tune thresholds and models systematically toward the <2 s and <5% targets. Clean architecture, measurable evaluation, security, and reliable delivery will be priorities. Best, Gustavo

@Melodyswright

Hi. This is a good fit because AI-driven surveillance and cybersecurity is usually less about simple video processing and more about complex behavioral anomaly detection so existing VMS and SIEM modules continue working. I have experience with Python, Linux, software architecture, machine learning, Ubuntu, TensorFlow, PyTorch, and anomaly detection. The main technical issue in integrated security monitoring is that heterogeneous data streams often suffer from high latency and inconsistent feature extraction, which can cause significant alert fatigue and missed threats during critical events. I would solve this by implementing a unified Python-based middleware layer that normalizes video, audio, and network log inputs, deploying a PyTorch-based anomaly detection engine for real-time inference, and configuring a gRPC interface to stream high-confidence alerts to your centralized dashboard for immediate review. I can also configure secure API endpoints and automated log rotation policies so the system works fully after completion while maintaining strict data privacy and efficient resource utilization across your infrastructure. The goal is to make the system run exactly like a low-latency proactive defense hub and ensure everything is tested and stable. And I can help you so on. Thanks.