Malware Data Science: Attack, Detection, and Attribution by Joshua Saxe (English

Description: Malware Data Science by Joshua Saxe, Hillary Sanders Estimated delivery 4-14 business days Format Paperback Condition Brand New Description Identify, analyse, and classify large-scale malware using machine learning and data visualisation. Publisher Description Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization.Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization.Security has become a "big data" problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, youll need to know how to think like a data scientist.In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis.Youll learn how to-- Analyze malware using static analysis- Observe malware behavior using dynamic analysis- Identify adversary groups through shared code analysis- Catch 0-day vulnerabilities by building your own machine learning detector- Measure malware detector accuracy- Identify malware campaigns, trends, and relationships through data visualizationWhether youre a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve. Author Biography Joshua Saxe is Chief Data Scientist at major security vendor, Sophos, where he leads a security data science research team. Hes also a principal inventor of Sophos neural network-based malware detector, which defends tens of millions of Sophos customers from malware infections. Before joining Sophos, Joshua spent 5 years leading DARPA funded security data research projects for the US government.Hillary Sanders leads the infrastructure data science team at Sophos, which develops the frameworks used to build Sophos deep learning models. Before joining Sophos, Hillary created a recipe web app and spent three years as a data scientist at Premise Data Corporation. Details ISBN 1593278594 ISBN-13 9781593278595 Title Malware Data Science Author Joshua Saxe, Hillary Sanders Format Paperback Year 2018 Pages 272 Publisher No Starch Press,US GE_Item_ID:123721450; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys

Price: 52.66 USD

Location: Calgary, Alberta

End Time: 2024-11-13T04:04:41.000Z

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