Malware Detection Using Machine Learning

EasyChair Preprint 12730

Abstract

In today’s digital landscape, safeguarding computer systems against malicious software, or

malware, is of paramount importance. This project delves into the realm of malware detection using

advanced Machine Learning techniques. The dataset comprises a diverse set of network traffic

attributes, providing a rich foundation for analysis and model training.

The dataset encompasses a total of 12,989 entries with 43 distinct features. These features

encapsulate crucial information about network activities, including duration, protocol type, services

utilized, flags, and numerous others. Each entry is associated with a unique identifier (Id) and is

labeled with a type of attack, enabling supervised learning for classification.

The prediction classes, representing different types of attacks, are defined as follows: ”ipsweep,”

”satan,” ”portsweep,” ”back,” and ”normal.” Each of these classes represents a distinct category of

network behavior, ranging from suspicious and potentially harmful activities to benign, legitimate

traffic.

In conclusion, this project showcases a comprehensive exploration of malware detection through

Machine Learning, utilizing a diverse dataset of network measures and safeguard digital assets

against malicious attacks.

Keyphrases: machine learning, network traffic analysis, supervised learning

@booklet{EasyChair:12730,
  author    = {Gude Venkata Sai and Kadiyam Rahul Prasad and Heman Raj Madaka and Billa Sri Varshith and Tejas Rana},
  title     = {Malware Detection Using Machine Learning},
  howpublished = {EasyChair Preprint 12730},
  year      = {EasyChair, 2024}}