Kinesis:Web-Based Log Analysis for Intrusion Detection System

Abstract

In the rapidly evolving digital era, the exponential growth of web-based applications and online services has made cybersecurity a critical concern for organizations and individuals alike. Modern cyber adversaries continuously exploit server-side vulnerabilities, often launching sophisticated, stealthy, and multi-vector attacks that can bypass traditional perimeter defenses. The complexity and volume of these threats necessitate an adaptive and intelligent approach to intrusion detection—one capable of analyzing real-time data, learning from patterns, and autonomously responding to anomalies. To address this challenge, the present study introduces a Web Log–Based Intrusion Detection System (IDS), a comprehensive framework engineered to detect, analyze, and mitigate security threats by leveraging the potential of automated log analytics and intelligent data-driven mechanisms. The proposed IDS system operates on the principle that web server log files encapsulate vital behavioral signatures of both legitimate and malicious activities. By employing pattern recognition, anomaly detection, and rule-based classification algorithms, the system effectively identifies abnormal behaviors such as unauthorized access attempts, brute-force login trials, SQL injection (SQLi) patterns, cross-site scripting (XSS) payloads, directory traversal attempts, and the injection of malicious scripts or malware. A significant advantage of this approach lies in its continuous, real-time monitoring capability, which enables proactive threat detection and rapid incident response without the need for constant manual supervision. The system’s architecture is composed of multiple integrated modules, including log parsing, data preprocessing, signature extraction, threat categorization, visualization, and automated response. The log parsing component filters and structures raw data into meaningful attributes, while the anomaly detection engine utilizes hybrid methodologies—combining heuristic rules with statistical and machine learning models—to enhance detection precision and minimize false positives. Upon identifying a potential threat, the system not only generates immediate alerts but also provides contextual recommendations for remediation, such as blocking malicious IPs, adjusting access permissions, or strengthening input validation mechanisms. An interactive graphical dashboard interface further augments system usability by visualizing detected threats based on severity levels, timestamps, geographic origin, and frequency. The dashboard presents intuitive charts and analytical reports that assist cybersecurity professionals in understanding evolving attack patterns and improving defense strategies. In addition, the system’s modular and scalable design allows for seamless integration into existing network infrastructures, making it suitable for deployment across organizations of varying sizes. By incorporating automated detection, intelligent decision-making, and self-learning feedback loops, the proposed IDS system transcends conventional rule-based monitoring approaches. It significantly reduces the time and effort required for manual log inspection, enabling security teams to focus on strategic defense enhancement. The experimental evaluation and simulated attack scenarios demonstrate that the Web Log–Based IDS effectively identifies critical security events with high accuracy and low latency, ensuring enhanced reliability, resilience, and adaptability in complex cyber environments. In conclusion, this research underscores the importance of data-driven, autonomous intrusion detection frameworks in fortifying modern web infrastructures against evolving cyber threats. The proposed system not only strengthens the detection and response mechanisms of existing security operations but also contributes to the broader vision of developing intelligent, self-adaptive cybersecurity solutions that can evolve in tandem with the continuously transforming digital threat landscape.

Introduction

The rapid growth of digital infrastructure and web applications has increased the frequency and complexity of cyberattacks targeting web servers. Threats such as SQL injection, brute-force login attempts, XSS, and DDoS often leave identifiable traces in server logs; however, traditional intrusion detection systems (IDS) struggle due to rule rigidity, manual inspection requirements, and limited contextual awareness. To overcome these limitations, Kinesis, a Web Log–Based Intrusion Detection and Response System, integrates machine learning, automation, and full-stack engineering to deliver real-time, intelligent threat detection and mitigation.

Kinesis uses a modular architecture comprising a React.js dashboard, Node.js/Express backend, and a Python-based machine learning microservice. Uploaded logs are preprocessed, analyzed for known attack signatures, and evaluated using supervised/unsupervised ML models to detect anomalies and zero-day threats. Identified suspicious activities—such as repeated failed logins, abnormal request patterns, or malicious payloads—are classified, saved in MongoDB, and visualized through an interactive dashboard.
The system also provides AI-driven recommendations, offering mitigation guidance (e.g., IP blocking, rate limiting, password policy hardening) and includes a built-in security chatbot for user queries.

Key objectives include real-time log analysis, automated threat detection, contextual alerting, user-assisted remediation, and secure role-based access. Kinesis emphasizes scalability and modularity through serverless functions, WebSocket-based real-time updates, and flexible integration with SIEM tools, firewall APIs, and threat intelligence feeds. Performance goals focus on low-latency processing, high detection accuracy, and fault tolerance under heavy log loads.

The literature survey highlights the evolution from signature-based IDS tools (like Snort and Suricata) to modern ML-based systems employing anomaly detection, clustering, and deep learning (Autoencoders, LSTM, Random Forests). Research supports hybrid models—blending rules with ML—for improved detection rates and lower false positives, aligning with Kinesis’ design philosophy.

The methodology details the system’s five-layer architecture:

1. Frontend for visualization, log upload, and chatbot interaction.

2. Backend for authentication, API routing, and coordination with ML services.

3. Machine Learning microservice for preprocessing, threat classification, and feedback-driven model updates.

4. Database module for secure storage of logs and anomalies.

5. Alerting and mitigation module for real-time notifications and actionable defense steps.

Implementation includes authentication workflows, React-based dashboards, Node.js route controllers, ML pipelines (rule-based + anomaly detection), MongoDB storage, WebSocket alerting, and optional features such as scheduled analyses, IP reputation scoring, and automated firewall actions.

Overall, Kinesis demonstrates a robust full-stack IDS capable of proactive cybersecurity monitoring. It unifies real-time ML-driven threat detection, automated recommendations, user-friendly visualization, and scalable architecture. By minimizing manual effort and enabling adaptive learning through feedback loops, the system offers an efficient and modern solution to evolving web-based cyber threats.

Conclusion

The Kinesis Web Log–Based Intrusion Detection System offers a robust, intelligent, and comprehensive solution for real-time detection, analysis, and mitigation of web-based threats. By integrating both rule-based heuristics and machine learning–driven anomaly detection, the system can identify both known and novel threats within server logs, including zero-day attacks. The modular design—comprising a React.js frontend, Node.js/Express backend, and Python-based machine learning microservice—ensures scalability, maintainability, and adaptability. Notable achievements include: 1. High Detection Accuracy: Experimental results demonstrate over 95% accuracy in threat detection, with minimal false positives and negatives. 2. Real-Time Monitoring and Alerts: Near-instantaneous alerting allows security teams to respond rapidly, thereby reducing dwell time and limiting potential damage.User-Centric Visualization – The dashboard provides intuitive insights via charts, tables, and interactive trend analysis, complemented by an AI chatbot for guided mitigation. • Automated Mitigation Recommendations – Context-aware suggestions reduce manual intervention and enhance operational efficiency. • Secure and Role-Based Access – Ensures only authorized personnel can access sensitive data and critical system functions, enhancing compliance and data security.

References

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Copyright

Copyright © 2025 Sanjana Sanjai, Karan Somkuwar, Rohitkumar Dhapade, Prof. Mrunal Arjunkar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.