Phishing and Malware Detection Project with Full Code

Overview

In today’s digital world, the threat landscape is constantly evolving, with cybercriminals deploying increasingly sophisticated techniques to compromise systems and steal sensitive data. This Phishing and Malware Detection Project aims to provide a robust, end-to-end solution for identifying and neutralizing threats posed by malicious websites. Whether implemented as a standalone tool or incorporated into a broader cybersecurity toolchain, this project integrates static code analysis, dynamic behavior monitoring, and external intelligence APIs to make a solid and accurate threat detection engine. Designed with developers, researchers, and cybersecurity analysts in mind, it is ideally suited to a final year project or real-world use.

Core Detection Components

Static Content Analysis

The initial defense mechanism in this system is the static scanning of the target site's content. This involves the analysis of the HTML structure, in-page JavaScript code, and other hidden and visible elements without necessarily running any scripts. Some of the important features are:
HTML Content Scanning: Using the Cheerio library to scan the DOM, the system can identify hidden elements, suspect meta tags, and manipulative redirection behaviour built into the page structure.
JavaScript Code Scanning: JavaScript is a popular vector for injecting malware or leading users to phishing sites. This mechanism scans code for the following patterns: excessive obfuscation, use of hazardous functions (document.write, eval, etc.), and recognized exploit signatures.
Network Request Scanning: Outbound requests are examined for references to suspicious or untrusted URLs. These include AJAX calls, iframe sources, image sources, and embedded resources that can serve as command-and-control endpoints.
Keyword-Based Threat Detection: The product scans against a hand-curated repository of known malicious strings, phrases, and file paths frequently employed in phishing kits, adware injections, and crypto scam landing pages.

External API Integration

In order to improve the accuracy and extend the detection capability, the system incorporates a number of reputable threat intelligence services:
VirusTotal: Provides URL reputation scores and threat categorization based on combined results from a dozen antivirus engines and domain blacklists.
Google Safe Browsing API: Offers classifications on whether the site is hosting malware, phishing material, or deceptive tactics.
URLScan.io: Enables dynamic snapshotting and deep scanning of URLs to allow insight into layout manipulations, redirect chains, and external resource loads.
AbuseIPDB: Offers the reputation and abuse history of an IP address, determining if the site is hosted on infrastructure noted for malicious behavior.
PicPurify & APILayer: These services carry out image content moderation and metadata scanning that assists in detecting adult content, hate symbols, or doctored images generally on scam websites.

⚙️ Dynamic Behavior Analysis

Extending beyond static analysis, the system employs Playwright, a new browser automation library, to mimic actual user interactions and identify behavior that is only seen at runtime.
Playwright Automation: Runs websites in a headless instance of Chromium to simulate real-world visits. This assists in detecting concealed layers of malicious activity that are triggered only under certain user conditions.
Hidden Element Detection: Detects visual cloaking tactics, including invisible buttons, off-screen text links, and concealed iframes employed to hijack user clicks or steal passwords.
Obfuscated Script Identification: Scans JavaScript for indicators of base64 encoding, character substitution, and obfuscated functions that show malicious intention.
Behavioral Pattern Matching: Uses signature-based detection of known malware activity such as delayed redirect, browser fingerprinting, cryptojacking scripts, and phishing form injections.

Detection Flow – Initial Content Analysis

The whole detection pipeline runs in multiple layers for maximum accuracy and flexibility:

Parse HTML: Pulls structure, tags, and embedded content with Cheerio.
Analysis of JavaScript: Looks for malicious scripts and obfuscated code.
Keyword Matching: Triggers any evidence of scam-content or signs of compromise.

External API Scans:

Submits URLs and IPs to VirusTotal and Google Safe Browsing.
Provides website screenshots to URLScan for scanning.
Asks AbuseIPDB for IP reputation analysis.
Scans images for offensive or scam-related content.

Dynamic Browser Simulation:

Spins up headless browser using Playwright.
Captures hidden UI elements or auto-redirects.
Tracks behavioral triggers (e.g., slow script loads, iframe injections).

Suspected Threat Categories Identified

The platform is designed to detect a broad range of threats, including both conventional malware and contemporary deceptive tactics. These encompass:

Malware & viruses hosted on infected or rogue servers.
Phishing domains impersonating legitimate services (banking, social media, etc.).
Adult material & NSFW content, frequently utilized in spam lures.
Gambling sites, particularly those circumventing ad blocking or facilitating illegal gambling.
Cryptocurrency scams in the form of fake investment offers or wallet theft.
Suspicious JavaScript patterns like concealed miners or payload droppers.
Invisible or off-screen iframes commonly employed in drive-by downloads.
Encoded, minified, or obfuscated scripts concealing malicious logic.

Risk Assessment Criteria

A risk score is assigned to every website or URL that is scanned based on the number and severity of the threat indicators. The final assessment is derived from the following parameters:

Total number of red flags identified on static, dynamic, and API layers.
Severity levels grouped by detection source.
Number of VirusTotal detections (number of engines that identify the URL/IP as malicious).
Keyword matching engine confidence scores.
Image and content moderation notifications from PicPurify and APILayer.

Why This Matters

Cybersecurity is not just machine protection; it's human protection. It doesn't matter if you're creating a web browser plugin, an enterprise threat detection platform, or a class project – true and timely identification of malicious websites is paramount. This project is unique in providing a multi-pronged solution, with the combination:

static analysis for initial threat detection.
Dynamic simulation to conduct thorough behavioral inspections.
Third-party intelligence to corroborate and support conclusions.

It's a perfect foundation for students, security engineers, or startups to develop upon a scalable, open, and transparent system. Prospective use cases are:

Periodic updates of malware URL databases.
Incorporation into residential or corporate web security suites.
Creation of parental control software that filters adult or scam sites.
Utilization in automated threat research environments for cybersecurity labs or honeypots.

With its open-source philosophy and modular architecture, this Malware Detection Project not only provides a final-year academic solution but also a real-world basis for future innovation in web security and threat intelligence.

Project Includes:

PPT
Synopsis
Report
Project Source Code
Base Research Paper
Video Tutorials

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