Intrusion Detection Research Paper Outline

pptx

School

Kaplan University *

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Course

MISC

Subject

Information Systems

Date

Nov 24, 2024

Type

pptx

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Uploaded by AdmiralQuailMaster1067

Research Paper Outline: Intrusion Detection Using Machine Learning Student’s Name Affiliation Course Tutor Due Date

Abstract  Concerns about cyber security.  Intrusion detection systems (IDSS).  Traditional intrusion detection frameworks are ineffective.  Incorporating machine learning and intrusion detection systems.

Introduction  The definition of Intrusion Detection System (IDS). Software application that detects a system attacks by use of different machine learning methods.  Firewalls and password security are examples of intrusion detection methods that do not guarantee overall system security.  According to scholars at Google's DeepMind artificial intelligence lab, machine learning is the most effective way to examine networks, detect anomalies, and stop intrusions.

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Research Paper Sections  The main sections of the research paper are listed below. 1. Techniques for Intrusion Detection 2. Flaws on use of Conventional Intrusion Detection Technologies

Research Paper Sections Continuation 3. Machine Learning (ML)

Research Paper Sections Continuation

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Research Paper Sections Continuation 5. An Overview of the Most Effective Methods

Research Paper Sections Continuation 6. Advantages of Intrusion Detection Systems Using Machine Learning

Summary  K-means clustering will be considered as the perfect method for intrusion detection.  Machine learning is utilized in companies for massive data.  K-meaning clustering algorithm is one of the most efficient strategies for quickly detecting intrusion.

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Conclusion  Businesses and governments should adopt machine learning to protect networks.  Large businesses and the governments should lead the way in implementing machine learning.

References Aljanabi, M., Ismail, M. A., & Ali, A. H. (2021). Intrusion detection systems, issues, challenges, and needs. International Journal of Computational Intelligence Systems , 14 (1), 560-571. Dini, P., & Saponara, S. (2021). Analysis, design, and comparison of machine- learning techniques for networking intrusion detection. Designs , 5 (1), 9. McElwee, S. (2017, March). Active learning intrusion detection using k- means clustering selection. In SoutheastCon 2017 (pp. 1-7). IEEE.

Intrusion Detection Research Paper Outline

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