GuardNet: A Python-Based Intrusion Detection System for Home Networks

The modern home network has evolved from a simple gateway for personal computers to a complex, heterogeneous environment teeming with dozens of interconnected devices, including smartphones, smart TVs, security cameras, and voice assistants. This rapid expansion, largely driven by the IoT revolution, has created an unprecedented attack surface within the home. Many IoT devices are designed with a focus on functionality and cost-effectiveness rather than security, often resulting in products with hardcoded credentials, unpatched firmware vulnerabilities, and insecure communication protocols. This inherent insecurity transforms the typical home network into a fertile ground for cybercriminals seeking to steal personal information, compromise privacy through surveillance, or enlist devices into botnets like Mirai for conducting large-scale Distributed Denial of Service (DDoS) attacks. The primary stakeholders, non-technical home users, are often unaware of the risks lurking within their own networks. The security measures provided by their Internet Service Providers and consumer-grade routers are typically limited to a basic Stateful Packet Inspection (SPI) firewall and Network Address Translation (NAT), which are insufficient for detecting or preventing sophisticated intrusions. These tools cannot identify malicious activity within the local network, such as a compromised device scanning for other vulnerable targets, nor can they recognize outbound communication to known command-and-control (C2) servers. Users are effectively blind to the traffic flowing in and out of their homes, unable to discern legitimate communication from malicious activity. A significant gap exists between the security needs of the modern home and the tools available to the average user. Professional-grade Intrusion Detection Systems like Snort, Suricata, or commercial offerings are powerful but carry a steep learning curve, require dedicated hardware, and demand constant maintenance and rule-tuning. Their complexity and cost make them inaccessible and impractical for the intended user base. There is a pressing need for a solution that democratizes network security, offering a 'plug-and-play' experience that is lightweight, affordable, and capable of translating complex network events into simple, understandable, and actionable alerts. Without such a tool, home networks will remain a soft, high-value target for a growing number of automated and targeted cyberattacks, leaving millions of users and their data exposed.

The proposed solution is GuardNet, a modular, Python-based Intrusion Detection System (IDS) specifically designed for the home network environment. GuardNet aims to provide robust, real-time threat detection capabilities through a simple and intuitive user interface, making advanced network security accessible to non-technical users. The system will be designed to run on a variety of platforms, including low-power single-board computers like the Raspberry Pi, or as a background service on a user's desktop computer. The core of the solution is a passive monitoring approach, where GuardNet listens to all network traffic flowing through the home router without being in-line, thus ensuring it cannot become a bottleneck or a single point of failure for the network. The system's architecture is composed of four primary modules. The first is the Packet Capture Module, which will leverage the powerful `Scapy` library to capture raw network packets from a designated network interface operating in promiscuous mode. This module is the foundational data source for the entire system. The captured data is then fed into the Detection Engine. This second module is the intelligence of GuardNet, employing a signature-based approach. It will compare incoming and outgoing traffic against a curated and extensible database of threat signatures. These signatures will define patterns of malicious behavior, such as specific port scanning techniques used by malware, communication with IP addresses on known blacklists, the use of insecure protocols by IoT devices, or the presence of specific payloads associated with common exploits. The signature database will be a simple, human-readable format like JSON or YAML, allowing for easy updates and community contributions. The third module is the Network Discovery and Baselining Service, which will periodically use tools like `Nmap` to actively scan the local network. This service will identify all connected devices, attempt to fingerprint their operating systems and services, and maintain a persistent inventory. This baseline is crucial for detecting new, unauthorized devices that connect to the network, a common indicator of a security breach. The final component is the Alerting and Presentation Module. When the Detection Engine finds a match, it will trigger an alert containing detailed yet understandable information: the source of the threat, the targeted device, the nature of the attack, and a recommended course of action. These alerts will be stored in a local SQLite database and presented to the user through a clean, web-based dashboard powered by Flask. This dashboard will serve as the central control panel, allowing users to view alerts, manage their device inventory, and configure system settings. For critical alerts, the system will also support push notifications via email or other services, ensuring timely user notification.