Network Intrusion Detection

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Keywords: Anomaly Detection, Behavioural Analysis, Kafka, Spark, spark Streaming, Unsupervised Machine Learning, Fuzzy Clustering, Time Series Analysis, Arima, Entropy Index, Index of Dispersion, Pearson correlation, MLib and R


The increasing adoption of connected devices demands faster, more reliable connectivity and new technologies and skills to successfully address challenges in areas like Quality of Experience, Resource Scalability, Predictive Maintenance, Fraud Detection and Cyber Security.


The new reality scenario produces a massive volume of streaming, time-series data presenting significant technical challenges for telecom operators. One fundamental capability for network streaming analytics is to model each stream in an unsupervised fashion and detect unusual, anomalous behaviours in real-time as training batch are not always available, particularly when new network behaviours emerge.