{"id":24465,"date":"2020-05-30T06:00:00","date_gmt":"2020-05-30T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=24465"},"modified":"2020-09-20T18:22:40","modified_gmt":"2020-09-21T01:22:40","slug":"research-highlights-midas-real-time-anomaly-fake-news-intrusion-detection","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2020\/05\/30\/research-highlights-midas-real-time-anomaly-fake-news-intrusion-detection\/","title":{"rendered":"Research Highlights: MIDAS – Real-time Anomaly\/Fake News\/Intrusion Detection"},"content":{"rendered":"\n

In the insideBIGDATA Research Highlights column we take a look at new and upcoming results from the research community for data science, machine learning, AI and deep learning. Our readers need to get a glimpse for technology coming down the pipeline that will make their efforts more strategic and competitive. In this installment we review MIDAS – Real-time Anomaly\/Fake News\/Intrusion Detection<\/em> developed by Ph.D. candidate Siddharth Bhatia and his team at the National University of Singapore.<\/p>\n\n\n\n

MIDAS finds anomalies or malicious entities in time-evolving graphs. MIDAS can be used to detect intrusions, Denial of Service (DoS), and Distributed Denial of Service (DDoS) attacks. MIDAS is a new approach to anomaly detection that outperforms baseline approaches both in speed and accuracy. It can also be used to detect fake profiles in Social Networks like Twitter, Facebook, Amazon reviews, and Financial Frauds. MIDAS requires constant memory to detect these anomalies in real-time so as to minimize the harm caused by them. Also, MIDAS is up to 48% more accurate while being up to 644 times faster than the state of the art approaches.<\/p>\n\n\n\n

MIDAS is currently being deployed in real-world systems to improve their performance. Different cybersecurity firms are seeking specially tuned versions of MIDAS according to their requirements. Different developers have implemented MIDAS in Python, Ruby, Rust, R, and Golang in addition to the C++ version that was originally released. MIDAS source code and different implementations are open source, and available on the Github Project Page.<\/a><\/p>\n\n\n\n

At the National University of Singapore, Ph.D. candidate\u00a0Siddharth Bhatia<\/a>\u00a0and his team have developed MIDAS. The MIDAS research paper can be found HERE<\/a>. The video below summarizes work with MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams.<\/p>\n\n\n\n

NOTE: the team recently posted 2 new anomaly detection research papers on arXiv.org: paper 1<\/a> and paper 2<\/a>. <\/p>\n\n\n\n

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