Description:

Malware (“malicious software") poses a significant threat to businesses worldwide, exploiting system vulnerabilities to cause havoc in computing environments. To safeguard your organisation, robust malware detection tools are essential. However, traditional detection methods often fall short due to the constantly evolving nature of malware and the ever-changing tactics employed by cybercriminals.

This talk introduces a state-of-the-art malware detection system: ML Defender. By harnessing the power of artificial intelligence and deep learning algorithms, organisations can proactively uncover malware threats and prevent potential damage.

The talk will cover the following key topics:

1. The Importance of Detecting and Mitigating Malware:

   • Discuss the critical implications and risks associated with malware attacks on businesses.
   • Highlight the need for proactive measures to detect and mitigate malware threats.

2. Understanding Deep Learning:

   • Provide an overview of deep learning and its underlying principles.
   • Explain how deep learning models can process complex data and extract meaningful patterns.

3. Deep Learning for Object Recognition and Image Classification:

   • Illustrate the capabilities of deep learning in object recognition and image classification tasks.
   • Showcase how deep learning algorithms can effectively analyze and interpret visual content.

4. Leveraging Deep Learning for Malware Detection:

   • Explore how deep learning techniques can be adapted and utilized for the detection of malware.
   • Discuss the advantages of deep learning models in identifying complex and evasive malware variants.

5. Supervised Deep Learning for Malware Detection and Considerations:

   • Introduce supervised deep learning approaches specifically designed for malware detection.
   • Address considerations such as dataset creation, model training, and performance evaluation.

6. Benefits of Deep Learning-Based Malware Detection:

   • Highlight the advantages of deep learning-based malware detection over traditional methods.
   • Discuss its potential to provide more accurate and efficient detection capabilities.

7. Reducing the Risk of Malware Attacks with ROAR:

   • Introduce the concept of ROAR (Risk-oriented Adaptive Response) as a holistic approach to mitigating malware attacks.
   • Showcase how deep learning-based malware detection can be integrated into a comprehensive security strategy.

Attendees will gain insights into the critical importance of detecting and mitigating malware threats, the capabilities of deep learning in the context of malware detection, and the benefits of adopting deep learning-based approaches to enhance organizational security.

Prerequisites:

Participants are recommended to have a basic understanding of the following concepts before attending the session:

1. Malware: Familiarity with the concept of malware, including its definition, types (e.g., viruses, worms, trojans), and potential impacts on computing systems and organizations.

2. Cybersecurity Fundamentals: An awareness of foundational concepts related to cybersecurity, such as system vulnerabilities, attack vectors, and common defense mechanisms (e.g., antivirus software, firewalls).

3. Machine Learning Basics: A general understanding of machine learning principles, including supervised learning, training and testing datasets, and model evaluation metrics (e.g., accuracy, precision, recall).

4. Deep Learning Overview: An introductory knowledge of deep learning, its distinction from traditional machine learning, and the role of artificial neural networks in deep learning models.

5. Image Classification: Familiarity with the concept of image classification and its application in various domains, such as object recognition, visual content analysis, and computer vision.

While these prerequisites provide a foundational understanding, participants of all skill levels are welcome to attend the session.

The talk will aim to present the topics in a clear and accessible manner, ensuring that both beginners and experienced professionals can benefit from the discussion and insights shared.

Content URLs:

https://mldefender.s3.ap-south-1.amazonaws.com/UI/index.html

Speaker Info:

Continuous Learner | AI & ML Enthusiast | Talks about Deep Learning-Neural Networks | PyTorch | AWS | Docker | Python | Automation | Blogger | Working in Cyber Security Domain

Speaker Links:

• https://www.youtube.com/playlist?list=PLqYDykjFMcnGPLPC7cBp9zqgXaRqoUJrC
  • https://www.linkedin.com/in/suman-kanukollu/
  • ML Defender Demo : https://mldefender.s3.ap-south-1.amazonaws.com/UI/index.html
  • https://suman-projects.s3.ap-south-1.amazonaws.com/myWebPage/index.html