NETWORK Anomalies

A local institution partnered with our team to conduct a comprehensive data analytics assessment of its network traffic to identify anomalies and potential security threats. The provided datasets contained detailed network logs, including source and destination IPs, packet sizes, protocols, and timestamps over a defined period.

The primary objectives of this analysis were:

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• Enhancing Network Security – Analyzing network traffic patterns to detect irregular activities that could indicate potential cybersecurity threats.

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• Data Integration & Preprocessing – Compiling multiple datasets, merging them using Python, and applying data cleaning techniques to ensure data consistency and completeness.

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• Anomaly Detection Modeling – Implementing machine learning algorithms to identify suspicious network behavior that deviates from typical traffic patterns.

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• Visualizing Findings for Actionable Insights – Mapping detected anomalies in an interactive Tableau dashboard to support cybersecurity teams in proactive threat mitigation.

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To achieve these objectives, we performed Exploratory Data Analysis (EDA) to uncover key traffic trends and correlations. Using Python, we engineered meaningful features, handled missing data through imputation techniques, and optimized the dataset for machine learning models. By applying anomaly detection algorithms, we identified deviations in network activity that warranted further investigation. The final dataset, enriched with detected anomalies, was visualized through a Tableau dashboard, enabling security teams to quickly interpret and act on potential threats.

This analysis provided the institution with valuable insights to strengthen its network monitoring capabilities, improve response times to security incidents, and enhance overall cybersecurity posture.