Malware Analysis
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Malware Analysis Syllabus — Unit-wise Content
The official GTU curriculum for Malware Analysis (subject code 3174506) as prescribed for CS-IoT · Semester 7 divides the course into 7 units, covering 45 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3174506)Unit 1 Introduction to Malware
- Introduction to malware
- OS security concepts
- malware threats
- evolution of malware
- malware types-viruses
- logic Bombs
- malware analysis
- Malware analysis and its significance in digital forensics
Unit 2 Static Analysis and Dynamic Analysis
- Static Analysis-Determining the File Type
- Fingerprinting the Malware
- Extracting Strings
- Determining File Obfuscation
- Inspecting PE Header Information
- Dynamic Analysis- System and Network Monitoring
- Dynamic Analysis (Monitoring) Tools
- Dynamic Analysis Steps
- Dynamic- Link Library (DLL) Analysis
Unit 3 Malware Obfuscation Techniques
- Simple Encoding
- Malware Encryption
- Custom Encoding/Encryption
- Malware Unpacking
- Code Analysis Tools
- Static Code Analysis (Disassembly) Using IDA, Disassembling Windows API, Patching Binary Using IDA, IDA Scripting and Plugins. 7 15 2021 - 22 GUJARAT TECHNOLOGICAL UNIVERSITY Bachelor of Engineering Syllabus Subject Code : 3174506 Subject Name : Malware Analysis https://syllabus.gtu.ac.in/ Page 2
Unit 4 Hunting Malware Using Memory Forensics
- Memory Forensics Steps
- Memory Acquisition
- Volatility Overview
- Enumerating Processes
- Listing Process Handles
- Listing DLLs
- Dumping an Executable and DLL, Listing Network Connections and Sockets
- Inspecting Registry
- Investigating Service
- Extracting Command History
Unit 5 Malware Detection Techniques
- Signature-based techniques: malware signatures
- packed malware signature
- metamorphic and polymorphic malware signature
- non- signature-based techniques: similarity-based techniques
- machine- learning methods
- invariant inferences
Unit 6 Android Malware
- Malware Characterization
- Case Studies – Plankton
- Smartphone (Apps) Security
Unit 7 Latest trends in Malware Analysis
- MITRE ATT&CK framework
- Case study of ransomware attacks
- use of Artificial Intelligence for malware attacks