Main Reference PaperSVM Training Phase Reduction Using Data Set Feature Filtering for Malware Detection, Mar 2013.
  • The project describes efficient way for detecting malware using SVM technique. The computation cost in N-Gram analysis for processing raw features of malware is reduced by lightweight filtering technique. SVM training phase is incorporated with lightweight subspace analysis filtering technique to remove irrelevant features.

+ Description
  • The project describes efficient way for detecting malware using SVM technique. The computation cost in N-Gram analysis for processing raw features of malware is reduced by lightweight filtering technique. SVM training phase is incorporated with lightweight subspace analysis filtering technique to remove irrelevant features.

  • The main objective of this project is to reduce computational overhead by filtering irrelevant features. The features of high dimensional data are reduced to low dimensional data by subspace analysis which in turn uses PCA technique. The filtered features are used in SVMtraining phase for malware detection. The potential indicator of the malware is detected.

+ Aim & Objectives
  • The main objective of this project is to reduce computational overhead by filtering irrelevant features. The features of high dimensional data are reduced to low dimensional data by subspace analysis which in turn uses PCA technique. The filtered features are used in SVMtraining phase for malware detection. The potential indicator of the malware is detected.

  • Clustering technique is used for reducing the high dimensional data into reduced feature set. In clustering, cluster consists of feature, each cluster is considered as a single feature and thus the features are drastically reduced. The reduced features are used for indicator formalware detection.

+ Contribution
  • Clustering technique is used for reducing the high dimensional data into reduced feature set. In clustering, cluster consists of feature, each cluster is considered as a single feature and thus the features are drastically reduced. The reduced features are used for indicator formalware detection.

  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1, OllyDbg 1.10, J2SE.

+ Software Tools & Technologies
  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1, OllyDbg 1.10, J2SE.

  • B.E / B.Tech / M.E / M.Tech

+ Project Recommended For
  • B.E / B.Tech / M.E / M.Tech

Professional Ethics: We S-Logix would appreciate the students those who willingly contribute with atleast a line of thinking of their own while preparing the project with us. It is advised that the project given by us be considered only as a model project and be applied with confidence to contribute your own ideas through our expert guidance and enrich your knowledge.