The continuing increase of Internet of Things (IoT) based networks have increased the need for Computer networks intrusion detection systems (IDSs). Over the last few years, IDSs for IoT networks have been increasing reliant on machine learning (ML) techniques, algorithms, and models as traditional cybersecurity approaches become less viable for IoT. IDSs that have developed and implemented using machine learning approaches are effective, and accurate in detecting networks attacks with high-performance capabilities. However, the acceptability and trust of these systems may have been hindered due to many of the ML implementations being ‘black boxes’ where human interpretability, transparency, explainability, and logic in prediction outputs is significantly unavailable. The UNSW-NB15 is an IoT-based network traffic data set with classifying normal activities and malicious attack behaviors. Using this dataset, three ML classifiers: Decision Trees, Multi-Layer Perceptrons, and XGBoost, were trained. The ML classifiers and corresponding algorithm for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets proved to be very high-performing based on model performance accuracies. Thereafter, established Explainable AI (XAI) techniques using Scikit-Learn, LIME, ELI5, and SHAP libraries allowed for visualizations of the decision-making frameworks for the three classifiers to increase explainability in classification prediction. The results determined XAI is both feasible and viable as cybersecurity experts and professionals have much to gain with the implementation of traditional ML systems paired with Explainable AI (XAI) techniques.

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An AI-powered, real-time surveillance system that fuses YOLOv8 person detection, MediaPipe skeleton tracking, face recognition, and Telegram remote control to deliver intelligent zone-based intrusion detection, automated alerts, and visual monitoring from any camera.

Final year project on Animal Intrusion Detection System which is a Deep Learning based object detection project

AI-enhanced Intrusion Detection System (IDS) designed to monitor network traffic on your home Wi-Fi network

AI-powered Cybersecurity Threat Detection System using Machine Learning and Security Logs. Detects malware, phishing attacks, and network intrusions using Python, TensorFlow, and SIEM logs.

Implementation of Research Paper titled: An explainable AI based Intrusion Detection System for DNS over HTTPS (DoH) Attacks on CICIDS dataset

Code used for the paper "AI-Driven Intrusion Detection Systems (IDS) on the ROAD Dataset: A Comparative Analysis for Automotive Controller Area Network (CAN)"

This is a Cyber Security tool that uses artificial intelligence (AI) to monitor network traffic and identify potential threats i.e, Intrusions occured in the network.

TEAM SENTINEL is an AI‑based elephant intrusion detection system that monitors forest borders using smart cameras and sensors. It analyzes risk, sends real‑time IoT alerts, and activates safe sound‑and‑light deterrents to prevent conflict while protecting both people and wildlife.

No description available

AI/Voice-activated Intrusion Detection System for real-time network monitoring with AI integration coming soon.

An Intrusion Detection System (IDS) using ensemble machine learning models and LIME for explainable AI, leveraging the CICIDS-2017 dataset for network intrusion detection with transparent predictions.

This is an IoT and AI-based intrusion/theft detection system that provides top-level security and peace of mind to property owners and managers. It uses sensors and devices to detect and alert any possible unauthorized entry or theft, taking a proactive approach to security and preventing threats from becoming serious.

Explainable AI (XAI) for Cybersecurity: Improving Intrusion Detection System (IDS) performance and transparency

No description available

Cloud-Based AI Intrusion Detection System for IoT Networks

This project contains three datasets having different modern reflective DDoS attacks such as PortMap, NetBIOS, LDAP, MSSQL, UDP, UDP-Lag, SYN, NTP, DNS, and SNMP. These datasets are based on the DCIC-DDoS2019 dataset proposed by man Sharafaldin et al. (2019). After the pre-processing of the DCIC-DDoS2019 dataset, we have created three different datasets, named Dataset_2_class, Dataset_7_class, and Dataset_13_class, for the use of the AI techniques to evaluate and analyze the performance of intrusion detection systems for the IoT networks.

Developed this Intrusion Detection System (IDS) in Python that delivers real-time network traffic analysis and threat detection. Utilized Scapy for packet capture and deep packet inspection (DPI) to analyze packet contents. Integrated a fine-tuned version of the Gemini 2.5 Pro API,giving unprecedented depth analysis,a unique implementation globally

Accurate-Cyber-Defense-Network-Intrusion-System is a high-performance, AI-assisted intrusion detection and prevention system (IDPS) designed to monitor, analyze, and defend critical networks against cyber threats.

Codebase for evaluating the adversarial robustness of AI-based Intrusion Detection Systems. Includes preprocessing, baseline model training, automated adversarial traffic generation, and evaluation scripts using CICIDS2017. Supports reproducible experiments and extensible attack pipelines.

AI-based intrusion detection system that analyzes network traffic or system logs to detect potential security threats and anomalous activities. Utilize machine learning algorithms, such as anomaly detection or behavioral analysis, to identify patterns indicative of cyber attacks.

Intrusion Detection System with Autoencoder

This is a project for an AI course I completed. I modeled both a convolutional neural network and a fully connected neural network to predict if a connection is an 'attack' or 'normal'. CNN accuracy: 99.85%. Fully connected NN accuracy: 99.82%.

No description available

The ultimate workbench for research & development of AI-powered anomaly-based Intrusion Detection Systems (IDS)

SentinelSec is a comprehensive, offline-first Intrusion Detection System (IDS) built with Python. It combines real-time packet monitoring, AI-based anomaly detection, CVE vulnerability intelligence, and rule-based threat detection in a single, powerful platform.

Sentinair is an advanced offline AI-based Intrusion Detection System tailored for isolated environments such as air-gapped military, industrial, or banking systems. It monitors system behavior patterns and flags anomalies using machine learning - all without needing internet connectivity.

This project pioneers an AI-driven Web Application Firewall (WAF) and Intrusion Prevention System (IPS) to fortify web security, utilizing dynamic clustering for real-time threat detection and mitigation. I contributed by developing programs from scratch and creating comprehensive documentation for the system's functionality.