Case Study: Spyware Detection Using Data Mining
Spyware Detection Using Data Mining
Prof. Mahendra Patil
Atharva College Of Engineering
Head Of Department(CS)
2nd line of address onlymahendra7@yahoo.com Karishma A. Pandey
Atharva College Of Engineering
1st line of address
2nd line of address pandeykarishma5@gmail.com Madhura Naik
Atharva College Of Engineering
1st line of address
2nd line of address madhura264@gmail.com
Junaid Qamar
Atharva College Of Engineering
1st line of address
2nd line of address junaiddgreat@gmail.com ABSTRACT
Malicious programs have been a serious threat for confidentiality, integrity and availability of a system. A new category of malicious programs has gained momentum called Spyware. Spyware are more dangerous for confidentiality of private
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Keywords
Malicious Code, Feature Extraction, N-Gram, ARFF (Attribute Relation File Format), CFBE (Common Feature-based Extraction), FBFE (Frequency-based Feature Extraction).
1. INTRODUCTION
Often, spyware is difficult to remove without detailed knowledge of how it works or by taking drastic measures such as wiping the system clean and starting over. In many cases, verifying the integrity of the system requires the operating system, patches, and applications to be reinstalled. These difficulties, combined with the efforts necessary to recover user data, can take a lot of time.
2. DEFINITION
Federal Trade Commission Staff Report in USA defines spyware as:
"Software that aids in gathering information about a person or organization without their knowledge and that may send such information to another entity without the consumer's consent, or that asserts control over a computer without the consumer's knowledge"[1].
3.
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