Accountable Behavioural Change Detection (VEDAR) using Machine Learning
Submitted by Srinivasa Rao Aravilli (@aravilli) on Tuesday, 16 April 2019
Session type: Short talk of 20 mins
With exponential increase in the availability of telemetry / streaming / real-time data, understanding contextual behavior changes is a vital functionality in order to deliver unrivalled customer experience and build high performance and high availability systems. Real-time behavior change detection finds a use case in number of domains such as social networks, network traffic monitoring, ad exchange metrics etc. In streaming data, behavior change is an implausible observation that does not fit in with the distribution of rest of the data. A timely and precise revelation of such behavior changes can give us substantial information about the system in critical situations which can be a driving factor for vital decisions. Detecting behavior changes in streaming fashion is a difficult task as the system needs to process high speed real-time data and continuously learn from data along with detecting anomalies in a single pass of data. In this talk, we introduce a novel algorithm called Accountable Behavior Change Detection (VEDAR) which can detect and elucidate the behavior changes in real-time and operates in a fashion similar to human perception. We have bench marked our algorithm on open source anomaly detection datasets. We have bench marked our algorithm by comparing its performance on open source anomaly datasets against industry standard algorithms like Numenta HTM and Twitter AdVec (SH-ESD). Our algorithm outperforms above mentioned algorithms for behaviour change detection, efficacy
This talk mainly covers VEDAR algorithem in detail and benchmarks comparison with other streamingly anomoly detection. More details in the https://arxiv.org/abs/1902.06663
Aravilli Srinivasa Rao working as Sr. Engineering Manager in Cisco CTO group and leading innovation & incubation of ML and AI projects. As a speaker presented in following conferences/workshops
1) Presented about Cisco’s ML/AI Applications in PDPC/CIPL workshop in Singapore. As a panelist shared experiences and thoughts on Accountable and Responsible AI. 2 ) Presented in IoT and AI Sumit organized by CII ’s in India about IoT and ML applications and related platforms in IoT space. 3) Presented about “Streaming Anomaly Detection” in Cisco’s Data Science Summit in Prague
He has a patent in Software recommendations uisng Reinforcement Learning.