Anomaly Detection for Cybersecurity Network Traffic Data
The client wanted to design a network intrusion detection system for monitoring the network traffic flow to identify attacks. The intrusion system was designed to detect out the anomaly-based patterns. The solution provided was well equipped to availability, authority, confidentiality and integrity rules in the form of minimum viable product.
Exploration of the related dataset with million records and studying network data features comprehensiveness.
Conducting traffic analysis based on statistical transformations and designing out the architectural framework required for the system.
Simulating the system to extract useful features based on the bytes, time and content with additional matched features.
Labelling out the different types of attacks to configure the matched transaction records.
Designing appropriate decision engine approaches and evaluating the competencies to figure out the best one.
It helped to figure out the appropriate benchmark datasets required for identifying the synthetic attack types that are anomalous to the network.
The exploration helped for designing forensic mechanism for botnets activities in devices connected to networks.
It helped to detect the fake domain requests received by the network by exploring the classification of the attack types.
The analysis helped to identify anomalous attack groups by 5% more than the previous traditional way of attack classifiers.
It paved way to surpass the bottlenecks of signature-based systems with fixed attacks which created problem for the client to understand other possibilities.
The analysis was given a shape of a minimum viable product that could serve the purpose of analyzing the network data as a generalized framework.