Holy Name University Library

Normal view MARC view ISBD view

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 2018
Contents:
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
Summary: 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.
Item type Current location Collection Call number Status Date due Barcode
Books 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

There are no comments for this item.

Log in to your account to post a comment.