AI-Powered Cybersecurity Threat Detection | C++ & Python | Real-Time Network Security System

Summary

In this video by Neural & Wires, viewers are introduced to a sophisticated real-time cybersecurity threat detection system powered by AI. The creator integrates C++ for packet capture and analysis, Python for machine learning, and OpenCV for live threat visualization. This system is equipped to detect various network intrusions, including DoS and phishing attacks, by analyzing network packet data. Future enhancements are suggested, such as deep learning integration and deployment on cloud platforms like AWS for broader Network Security Operations. Key takeaways include AI's transformative role in cyber defenses, providing smarter Intrusion Detection Systems and the potential for automated threat responses.

Highlights

• AI and machine learning integrate for dynamic threat detection 🎯
• C++ and Python synergy strengthens cybersecurity systems 🛡️
• Real-time data visualizations keep users informed and alert 🚨
• Potential cloud deployment enhances scalability and response speed ☁️
• Future upgrades could include deep learning to tackle complex threats 📚

Key Takeaways

• AI enhances cybersecurity by quickly identifying network threats ⚡
• Combining C++ and Python maximizes performance and detection capabilities 💪
• Machine learning models evolve over time, getting smarter with data 🤖
• Visualization tools help analyze threats in real-time 🔍
• Future developments include cloud integration and deep learning for better defense ☁️

Overview

Cybersecurity is evolving rapidly, and traditional methods often fall behind. This video demonstrates a cutting-edge system that uses AI to detect cyber threats in real-time. By integrating C++ with Python, the creator maximizes efficiency and speed, providing a robust defense mechanism for network security.

The system works by capturing live network data and analyzing it with machine learning models, classifying traffic as safe or suspicious. It utilizes a real-time visual dashboard to show potential threats, helping security professionals quickly identify and respond to issues. This approach demonstrates how automation and AI can transform cybersecurity practices.

Looking ahead, the creator suggests deploying the system on cloud platforms like AWS to enhance its capabilities. They also discuss the potential for integrating deep learning methods to improve threat detection accuracy further. This would offer a formidable defense against evolving cyber threats, marking a significant step forward in network security technology.

Chapters

• 00:00 - 00:30: Introduction to AI-Powered Cybersecurity In the chapter titled "Introduction to AI-Powered Cybersecurity," the author introduces an AI-powered cybersecurity threat detection system. The chapter begins with a brief mention of the growing cyber threats and the limitations of traditional security tools. This project combines C++, Python, AI, and network security to provide a real-time solution for detecting cyber threats. The chapter serves as a starting point for understanding this innovative approach to cybersecurity.
• 00:30 - 01:00: System Components Overview The chapter 'System Components Overview' delves into the core components that make up the program. It starts by explaining the Packet Cifer Switch, which operates with C++ and the t-shark API to capture real-time network traffic. This leads into the AI-Powered Threat Detection component, designed with Python and machine learning to analyze this traffic for any potential threats. Lastly, the chapter covers Live Threat Visualization, which uses Python's OpenCV and Matplotlib to provide a real-time visual dashboard. The underlying theme is the program's relevance to cybersecurity, addressing challenges like malware and net phishing.
• 01:00 - 01:30: Packet Capture and AI Threat Detection The chapter discusses how AI models can be used for detecting and preventing threats in real-time by analyzing network traffic patterns. DDOS attacks, which exploit network vulnerabilities, are used as an example of such threats. It introduces the process of starting packet capture with the tool T-shark, which captures network packets for subsequent analysis. The program further analyzes live network traffic and classifies them as either safe or a threat.
• 01:30 - 02:00: Live Network and CSV Updates The chapter titled 'Live Network and CSV Updates' discusses the process of capturing network data and storing it in a CSV file each time the program is run. This CSV contains details such as IP addresses, protocol types, packet sizes, and AI threat classifications. Over time, the AI model learns from this new data, improving its accuracy. The chapter also covers the dynamic updating of the file as new threats are detected, and the real-time threat visualization using open tools is demonstrated.
• 02:00 - 02:30: Real-Time Threat Visualization The chapter titled 'Real-Time Threat Visualization' discusses how dashboards provide live monitoring of cyber threats and trends over time. It emphasizes the importance of identifying high-risk protocols, such as TLS, DNS, and ARP spoofing attacks, for effective cybersecurity. The chapter explains that understanding traffic trends is crucial in identifying types of cyber attacks, including man-in-the-middle (MiTM), denial-of-service (DoS), and data exfiltration. It also highlights how security professionals utilize similar monitoring tools in security operation centers to prevent attacks. The chapter briefly touches on the suitability of using C++ for network security applications.
• 02:30 - 03:00: Advantages of Using C++ for Cybersecurity The chapter discusses the advantages of using C++ in cybersecurity applications. It highlights C++'s speed and performance, noting that it runs closer to the hardware compared to other languages like Python, making it suitable for high-speed network traffic analysis. Additionally, C++ offers enhanced memory control, which helps reduce vulnerabilities such as memory leaks and buffer overflows. These are common issues exploited in cyber attacks. The chapter also points out that many low-level security tools, such as Wireshark, Nmap, and Snort, are built using C++ due to its efficiency, emphasizing the language's importance in the cybersecurity field.
• 03:00 - 03:30: AI Model and Machine Learning Algorithms Cisco and other tech companies develop intrusion detection systems using C++ for performance and reliability in cybersecurity.
• 03:30 - 04:00: Future Upgrades and Enhancements The chapter titled 'Future Upgrades and Enhancements' discusses how to improve a system that uses a predict threat function in Python to detect threats in real-time based on packet size and protocol. This function identifies suspicious traffic, like unexpected large packet sizes or rare protocol behaviors, which could indicate intrusion attempts. Integrating Python with C++ through the python cap API is described as a method for improving the system.
• 04:00 - 04:30: Cybersecurity Industry Impact The chapter discusses an upgrade to a project by integrating deep learning techniques such as LCTM (Long Short-Term Memory) or Convolutional Neural Networks (CNS) instead of relying on Random Forest algorithms. This is aimed at analyzing package sequences and improving threat detection capabilities for cybersecurity applications.
• 04:30 - 05:00: Conclusion and Call to Action The conclusion emphasizes the innovative use of AI to prevent zero-day attacks by training on diverse ATT attack patterns. The proposal includes deploying this system as a cloud-based security solution, particularly as an AWS Lambda function. Its main objectives are to secure cloud environments such as AWS, Azure, and Google Cloud from cyber threats and to automate incident response by integrating threat intelligence. Additionally, the system connects to threat intelligence APIs like AlienVault OTX to compare packet data with known attack sources, enabling a proactive defense mechanism. The chapter calls for action by encouraging the implementation of such a system to enhance cybersecurity measures.

AI-Powered Cybersecurity Threat Detection | C++ & Python | Real-Time Network Security System Transcription

• 00:00 - 00:30 [Music] [Applause] [Music] hey everyone welcome back today I'm going to show you my AI power cyber security threat detection system cyber threats are growing rapidly and traditional security tools can't always keep up this project integrate C++ python Ai and network security to detect cyber threats in real time let's dive in
• 00:30 - 01:00 before running the program let's break down how it works under the hood there are three main components packet cifer switch is C++ and t-shark API captures real-time Network traffic AI powered threat detection which is Python and machine learning analyzes traffic and detects anls live threat visualization which is a python open CV and mat plop lip provides a real-time visual dashboard cyber security relevance attackers use malware net fishing and
• 01:00 - 01:30 the do DOS attacks to exploit vulnerabilities in network traffic our AI model detects suspicious patterns in real time prev preventing potential threats before they escalate so starting packet capture and AI threat detection let's start the packet cifer which uses t-shark to capture Network packets and save them for analysis the program starts analyzing live Network traffic and classifies packets as safe or threat
• 01:30 - 02:00 let's understand the live Network and CSV updates each time we run the program it locks newly captured packets into network data CSV let's take a look this CSV stores IP addresses protocol types packet sizes and AI thread classifications the AI model learns from new data each time making it more accurate over time the file updates dynamically as new threats are detected launching the realtime threat visualization now let's visualize the threats using open c
• 02:00 - 02:30 dashboard features live attack monitoring threats Trends over time and identifies high-risk protocol such as TLS DNS ARP spoofing attacks cyber security relevance understanding traffic Trends helps identify cyber attacks like mitm man in the middle dos and data exfiltration Security Professionals use similar monitoring Tools in in cosos Security operation centers to prevent attacks so why we use C++ for network security C++ is an excellent choice for
• 02:30 - 03:00 cyber security applications here is why Speed and Performance unlike python C++ runs closer to the hardware making it ideal for high-speed Network traffic analysis memory control and security memory leaks and buffer overflows are common in cyper exploits C++ allows fine grain control over memory reducing attack vectors integration with lowlevel tools many security tools like wir shark and nmap and snort are built with c and C++ due to their efficiency cyber security to use case companies like
• 03:00 - 03:30 Cisco P alter networks and crowd strike develop firewalls and and intrusion detection systems using C++ for performance and reliability how the AI model Works machine learning and cyber security now let's explore the AI model behind this system machine learning model how it detects risk extracting features from Network traffic we collect protocol types package sizes and time stamps to train the model training the AI supervised learning random Forest algorithm use we use a random Forest classifier to classify packets into safe
• 03:30 - 04:00 traffic or anomalous suspicious traffic labeling threats based on packet size and protocol large package sizes and rare protocol behaviors for example unexpected TLC traffic may indicate an intrusion attempt making predictions in real time the predict threat function in Python loads the train model and detects whether an incoming packet is safe or threat this function is called directly from C++ using the python cap API how to approve this system now let's discuss
• 04:00 - 04:30 how we can upgrade this project to be even more powerful we can integrate deep learning uh lctm or CNS instead of random fors we can use recurrent neural networks to analyze package sequences and detect Advanced threat use case detecting malware traffic as the squee as normal packets train on a larger data set currently our data set is small to improve accuracy we can use CIS IDs 2017 data set which is intrusion detection system traffic Dera cyber intrusion data set data set custom real world traffic
• 04:30 - 05:00 locks use case of preventing zero day attacks by training AI on diverse ATT attack patterns so we can deploy as a cloud-based security system instead of running locally deploy this as an AWS lampda function to monitor Cloud network activity so use case securing Cloud environments AWS aora Google cloud from cyber threats and automate incident response with threat intelligence connect the system to trade intelligence API like wiress toal alien W otx to compare packet data with known attacks sources use case if the system detects
• 05:00 - 05:30 traffic from a blacklisted IP address it can automatically block it why this matter cyber security industry impact this is more than just a coding project this is the feature of cyber security and reord application Network intrusion detection IDs IPS prodcts companies from ransomware fishing and Insider threats AI driven Security operation Center automates real time threat response and smart firewalls and Clause security enhances next CH firewall capabilities with AI according to cyber security
• 05:30 - 06:00 Advent cyber crime damages will hit 10.5 trillion annually by 2025 AI based security systems like this help fight back and that's how we build an AI power cyber security threat detection system what we covered today is captured and analyze real-time Network traffic we use AI to detect threats and visualize attack patterns we explor future upgrades with deep learning and Cloud security if you found this video helpful hit the like button subscribe for more Ai and cyber security content and let me
• 06:00 - 06:30 know in the comments what You' like to see next see you in the next one [Music]