Abstract :

With The Rapid Growth Of Digital Technologies, Cyber-attacks Have Become Increasingly Frequent And Sophisticated, Posing Serious Threats To Organizational Networks And Data Security. Traditional Manual Incident Response Methods Are Time-consuming And Often Ineffective In Handling Large-scale And Realtime Attacks. To Address These Challenges, This Project Proposes An Automated Incident Response System That Can Efficiently Detect, Analyze, And Mitigate Cyber Threats Using Network Log Data. The Proposed System Utilizes Network Traffic Analysis Through The Wireshark API To Monitor And Identify Suspicious Activities In Real-time. It Processes Uploaded Network Log Data To Detect Various Types Of Cyber-attacks Such As Distributed Denial Of Service (DDoS) And Abnormal Traffic Patterns. Once An Attack Is Identified, The System Generates Detailed Reports And Visualizations, Highlighting Attack Sources Such As IP Addresses And Port Numbers. Additionally, The System Supports Automated Response Actions, Including Isolating Compromised Systems To Prevent Further Damage And Spread Within The Network. The Application Is Designed With Multiple Modules Including User Registration, Login, Data Collection, Incident Detection, And Alert Analysis. Graphical Representations Provide Better Insights Into Attack Patterns And Frequency, Enabling Users To Make Informed Decisions. By Automating The Incident Response Process, The System Significantly Reduces Response Time, Minimizes Damage, And Enhances Overall Network Security. This Solution Is Highly Beneficial For Cybersecurity Operations, Offering Scalability, Efficiency, And Real-time Monitoring Capabilities For Modern Network Environments.

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1828-1836

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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