Malware data science : attack detection and attribution / Joshua Saxe, Hillary Sanders.
By: Saxe, Joshua.
Contributor(s): Sanders, Hillary.
Publisher: San Francisco, California, USA : No Starch Press, ©2018Description: xxvi, 243 pages : illustrations, charts ; 24 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781593278595 (print); 1593278594 (print); 9781593278601 (ebk.); 1593278608 (ebk.).Subject(s): Malware (Computer software) | Computer viruses | Debugging in computer science | Computer securityDDC classification: 005.84 Sa97 2018Item type | Current location | Collection | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|
Books | College Library General Circulation Section | GC | GC 005.84 Sa97 2018 (Browse shelf) | Available | HNU001306 |
Includes index.
Table of contents: Introduction --
1: Basic Static Malware Analysis --
2: Beyond Basic Static Analysis: x86 Disassembly --
3: A Brief Introduction to Dynamic Analysis --
4: Identifying Attack Campaigns Using Malware Networks --
5: Shared Code Analysis --
6: Understanding Machine Learning-Based Malware Detectors --
7: Evaluating Malware Detection Systems --
8: Building Machine Learning Detectors --
9: Visualizing Malware Trends --
10: Deep Learning Basics --
11: Building a Neural Network Malware Detector with Keras --
12: Becoming a Data Scientist
This title shows you how to apply machine learning, statistics and data visualization as you build your own detection and intelligence system. Following an overview of basic reverse engineering concepts like static and dynamic analysis, you'll learn to measure code similarities in malware samples and use machine learning frameworks like scikit-learn and Keras to build and train your own detectors.
College of Engineering and Computer Studies Bachelor of Science in Computer Science
Text in English
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