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Malware Forensics v4:AI &ChatGPT Mastery in Malware Analysis

Name: Malware Forensics v4:AI &ChatGPT Mastery in Malware Analysis
Rating: 4.3 (11 reviews)

Deciphering the Digital Threatscape : Next-Gen Techniques in AI-Powered Malware Forensics

Created byDharmanandana Reddy Pothula

Last updated 1/2025

English

English [Auto],

What you'll learn

Malware Forensics
Malware Analysis, Intrusion and Prediction
Digital Forensics
Detection of Fileless Malware with AI
Detection of Stealthy Malware with AI
Cloud-Based Malware Analysis
Static Malware Analysis with AI
Malware Analysis with ChatGPT
Metamorphic Malware Analysis with AI
Malware Executable File Analysis with AI
Malware Forensics using AI
Malware Evasion Analysis

Course content

6 sections • 27 lectures • 5h 27m total length

Decoding Fileless Malware: A Deep Dive into AI-Driven Detection Mechanisms10:01
Explore how AI-driven detection uncovers fileless malware that operates in memory, leveraging legitimate processes like PowerShell and WMI, and how behavioral features power adaptive defenses.
Unraveling the Features of Fileless Malware for Effective Detection15:50
Explore how AI-driven analysis uses datasets and engineered features—system calls, DLLs, C2, exfiltration, and memory forensics—to detect fileless malware, evaluate thresholds, and train models for accurate detection.
Advanced Techniques in Fileless Malware Detection16:17
Learn detection techniques for fileless malware that operates in memory, monitors system and API calls, DLL injections, suspicious processes, registry changes, and scheduled tasks.
Generating Real-Time Datasets for In-Memory Fileless Malware Detection8:26
Generate a real-time data set for AI-driven fileless malware detection by combining memory forensics tools like Volatility and Recall, behavioral analysis with Cuckoo Sandbox, and meticulous data labeling.
Algorithmic Approaches in AI-Driven Fileless Malware Analysis7:42
Apply AI-driven fileless malware detection by analyzing sequences of system API calls with LSTM, and flag anomalies via isolation forest and autoencoders to create a layered defense.
Building Malware Detection Model:Utilizing Memory Forensics for Dataset Creation7:01
Automate memory-forensics data collection to build a scalable malware detection dataset for deep learning, using volatility, parallel processing, data aggregation, labeling, and synthetic data to handle obfuscated malware.
Lab:Developing Automated Deep Learning Strategies for Fileless Malware Detection24:08
Develop an automated deep learning pipeline using LSTM to detect fileless malware by analyzing memory, living off the land techniques, and registry based persistence.

Deep Learning's Edge and Memory forensics in Unmasking Obfuscated Malware10:13
Detect obfuscated malware using memory forensics and deep learning, leveraging volatility to extract features and feed an lstm model for accurate detection.
Detecting Obfuscated Malware Using Memory Forensics with Deep Learning8:11
Detect obfuscated malware by combining memory forensics with deep learning, using LSTMs to analyze memory dumps and detect anomalies.
Lab: Implementing Deep Learning for Enhanced Detection of Stealthy Malware16:33
Demonstrates using an lstm network to detect obfuscated malware from memory dumps by extracting features, training and testing on split data, and classifying samples as benign or malicious.

Enhancing Malware Analysis through Advanced Automation Platforms10:13
Explore advanced automated platforms for malware analysis, performing static and dynamic analysis, extracting IOCs, and providing seamless workflow integration with real-time insights and interactive visualizations.
Harnessing Malware Sandbox Platforms for Enhanced Enterprise Security Infrastruc9:37
Explore how malware sandbox platforms enhance enterprise security by safely executing and analyzing suspicious files and URLs, enabling behavioral analysis and automated protection across inline security controls.
Merging Defenses: How Fileless Malware Detection Integrates with System Architec11:59
Integrate an AI-based fileless malware detection solution with system architecture to monitor memory dumps. Classify activity, raise alerts for incident response, and display insights on a centralized monitoring dashboard.

Understanding Metamorphic Malware: Evolution, Behavior, and Defense13:05
Explore metamorphic malware evolution, its changing code and signatures, and the defense strategies that detect it through behavior-based detection, heuristic analysis, and AI-driven analytics.
Dissecting Metamorphic Malware -Understanding What Changes & What Stays the Same10:48
Identify changes in metamorphic malware: code structure, syntax, encryption, and control flow, vs features like behavioral patterns, network signatures, semantic footprint, memory access, and graph neural networks for detection.
Advanced Techniques for Metamorphic Malware Detection12:08
Explore advanced metamorphic malware detection using AI-driven techniques: deep reinforcement learning, sequence-to-sequence models, and graph neural networks, highlighting adaptive analysis, continuous learning, structural pattern recognition, and relational learning.
Applying Sequence-to-Sequence models for Metamorphic Malware Analysis6:45
Discover how sequence-to-sequence models detect metamorphic malware by translating binary code to assembly, extracting opcode sequences, and tokenizing them for a neural encoder–decoder to predict future transformations.
Exploring Control Flow Graphs and Graph Neural Networks14:53
Learn how control flow graphs and graph neural networks detect metamorphic malware by mapping malware actions to a graph, then applying graph convolutional networks to track disguises.
Deep Dive into Malware Analysis using Control Flow Graphs & Graph Neural Network9:15
Develop malware analysis skills using control flow graphs and graph neural networks to detect malicious behavior, starting from disassembly of binaries to construct CFGs and analyze assembly instructions.
Metamorphic Malware Detection using Graph Neural Networks3:49
Learn to detect metamorphic malware using graph neural networks that analyze control flow graphs and generate node embeddings to flag suspicious patterns before harm occurs.
Lab: Metamorphic Malware Detection using Graph Neural Networks24:07
Demonstrates metamorphic malware detection with graph neural networks, explaining the workflow from generating control flow graph data to training and inferring on new samples.

Behavioral and Executable Malware: A Unified AI Detection Strategy15:41
Learn a unified AI detection strategy for behavioral and executable malware, using machine learning to analyze file attributes, API calls, control and data flow, network activity, and memory patterns.
Lab: Detecting Malware in Executable Files14:18
Explore detecting malware in executable files using a random forest classifier, feature engineering with file size, number of sections, entropy, and suspicious APIs, trained on simulated data.

Enhancing Malware Behavioral Analysis with ChatGPT6:44
Harness ChatGPT to enhance malware behavioral analysis through static and dynamic file analysis in sandbox environments, translating observations into reports and guiding integrated threat decisions.
Incorporating ChatGPT into the Malware Analysis Workflow6:05
See how ChatGPT integrates into the malware analysis workflow, from static analysis with Ida Pro and Ghidra to behavioral, code snippet, and threat intelligence analyses, guiding decisions.
ChatGPT's Multifaceted Approach to Malware Analysis6:31
Describe malware behavior to ChatGPT to flag ransomware activity and compare patterns to spyware. Analyze code snippets, logs, and threat intelligence, blending AI with human expertise.
Lab: Deploying ChatGPT for In-Depth Malware Code Behavior Analysis26:54
Analyze malware behavior with chatgpt by modeling text descriptions and code snippets, using the openai api to prompt for malicious indicators and generate actionable insights.

Requirements

Basic knowledge of Python will be good
Basic Knowledge of Machine Learning and AI will be good
But everything will be taught from the round up

Description

"Malware Forensics v4: AI & ChatGPT Mastery in Malware Analysis" is an advanced course meticulously designed to tackle the evolving menace of sophisticated malware through the lens of artificial intelligence (AI), deep learning (DL), and the groundbreaking capabilities of ChatGPT. This course is structured to arm cybersecurity professionals with the latest knowledge and practical skills required to decode, analyze, and effectively counteract an array of modern malware threats.

Chapter 1: Advanced Detection of Fileless Malware - Integrating Memory Forensics with AI&DL

Delve into the intricate world of Fileless malware detection, where AI and DL converge with memory forensics. Master the creation of real-time datasets for in-memory detection and explore algorithmic approaches for AI-driven malware analysis. Engage in a lab that develops automated deep learning strategies targeting elusive Fileless malware.

Chapter 2: Advanced Detection of Stealthy Malware - Leveraging Memory Forensics & Deep Learning

Understand how to unmask obfuscated malware using the combined strength of memory forensics and deep learning. Learn to detect stealthy threats by implementing cutting-edge deep learning methodologies in a hands-on lab environment.

Chapter 3:Modernizing Future of Malware Defense - Automated Platforms and Sandbox Solutions

Modernize your approach to malware defense with automated platforms and sandbox solutions. Discover how malware sandbox platforms can enhance enterprise security and how Fileless malware detection can be integrated within system architectures.

Chapter 4: Advanced Malware Dynamics - Decoding & Analyzing Metamorphic Malware

Engage with the dynamics of metamorphic malware, utilizing advanced detection techniques and applying sequence-to-sequence models and graph neural networks (GNNs). Dive into malware classification through reverse engineering for CFGs and experience practical application in our specialized lab.

Chapter 5: AI-Driven Analysis of Malware Executables

Explore a unified AI detection strategy for behavioral and executable malware. Gain hands-on experience detecting malware within executable files, leveraging the power of AI.

Chapter 6: Innovative Effective Malware Analysis with ChatGPT - Strategies & Techniques

Capitalize on the multifaceted approach of ChatGPT for malware behavioral analysis. Incorporate ChatGPT into your workflow and deploy it for in-depth malware code behavior analysis through an interactive lab session.

This course offers an exceptional blend of theory and hands-on experience, perfect for cybersecurity experts aspiring to be at the forefront of malware forensics. Join us to refine your expertise and stay one step ahead in the cybersecurity domain.

Who this course is for:

Anyone who want to enhance skills in cyber security threat detection
Any people who want to create added value to their cyber security business
Any one who want to start or pursue career in Digital Forensics
Any one who want to start or pursue career in Malware Forensics
Any one who want to start or pursue career in Malware Analysis
Anyone who want to enhance skills in Malware Analysis
Anyone who want to enhance skills in Cloud based Malware Analysis
Any one who are not satisfied with their job, but want to become Malware Analysis Expert
Any one who want to start or pursue career in AI & ChatGPT based Malware Forensics
Any one who want to enhance skills in AI & ChatGPT based Malware Detection and Analysis

Malware Forensics v4:AI &ChatGPT Mastery in Malware Analysis

What you'll learn

Explore related topics

Course content

Advanced Detection of Fileless Malware : Integrating Memory Forensics with AI&DL7 lectures • 1hr 29min

Advanced Detection of Stealthy Malware:Leveraging Memory Forensics &DeepLearning3 lectures • 35min

Modernizing Future of Malware Defense :Automated Platforms and Sandbox Solutions3 lectures • 32min

Advanced Malware Dynamics : Decoding & Analyzing Metamorphic Malware8 lectures • 1hr 35min

AI-Driven Analysis of Malware Executables2 lectures • 30min

Innovative Effective Malware Analysis with ChatGPT : Strategies & Techniques4 lectures • 46min

Requirements

Description

Who this course is for: