Case Study: Spyware Detection Using Data Mining

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Spyware Detection Using Data Mining

Prof. Mahendra Patil
Atharva College Of Engineering
Head Of Department(CS)
2nd line of address Karishma A. Pandey
Atharva College Of Engineering
1st line of address
2nd line of address Madhura Naik
Atharva College Of Engineering
1st line of address
2nd line of address
Junaid Qamar
Atharva College Of Engineering
1st line of address
2nd line of address 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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Malicious Code, Feature Extraction, N-Gram, ARFF (Attribute Relation File Format), CFBE (Common Feature-based Extraction), FBFE (Frequency-based Feature Extraction).
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.

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].
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People often use the same username and password for many different systems, so these stolen credentials may be used to access other systems not yet infected. Once access is gained, additional information theft or malware installation can take place. Another way spyware puts systems at future risk is by installing backdoor access mechanisms. These backdoors give the malware operator access to control the system or to command the system to download and run arbitrary applications. Attackers can build vast collections of compromised systems without originally compromising a single
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