The ability to detect malware has needed to drastically change in the past few years away from traditional signature or list based techniques. Couple this with the rise of mobile device based attacks, where the scale of the data is predicted to be 60% of the internet in 2018*, our online lives will need Machine Learning (ML) and Data Science to ensure its security. At Wandera we have successfully implemented a malware detection (and classification) ML model at scale with the use of Apache Spark (MLib) and the PMML via OpenScoring paradigm.
In this talk we will touch on the training data and why we use Spark at all, the features we extract from mobile phone applications and how we then obtain our high accuracy scores in the cloud. At Wandera we have successfully implemented a Malware detection (and classification) ML model at scale with the use of Apache Spark (MLib) and the PMML via OpenScoring paradigm. *https://blog.cloudflare.com/our-predictions-for-2018/
Session hashtag: #SAISDev9
David has PhD in Applied Mathematics at Imperial College in 2014. Since then David has made the move into the Data Science industry. Starting at KPMG he was able to tackle various customer issues from a data and prospective predictive analytics viewpoint. He also gained valuable expertise in python, Scala and Apache Spark. With these Data Science weapons he decided to find more experience in a truly data driven and technically challenging environment. As the Lead Data Scientist at Wandera he has changed how DS is done with reusable code, visualisation improvements and using Machine Learning in various applications.