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Semantics-Based Online Malware Detection: Towards Efficient Real-Time Protection Against Malware

Semantics-Based Online Malware Detection: Towards Efficient Real-Time Protection Against Malware

Starting at: Rs.4,500.00

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Semantics-Based Online Malware Detection: Towards Efficient Real-Time Protection Against Malware

†Recently, malware has increasingly become a critical threat to embedded systems, while the conventional software solutions, such as antivirus and patches, have not been so successful in defending the ever-evolving and advanced malicious programs. In this paper, we propose a hard ware enhanced architecture, GuardOL, to perform online malware detection. GuardOL is a combined approach using processor and field-programmable gate array (FPGA). Our approach aims to capture the malicious behavior (i.e., highlevel semantics) of malware. To this end, we first propose the frequency-centric model for feature construction using system call patterns of known malware and benign samples. We then develop a machine learning approach (using multilayer perceptron) in FPGA to train classifier using these features. At runtime, the trained classifier is used to classify the unknown samples as malware or benign, with early prediction. The experimental results show that our solution can achieve high classification accuracy, fast detection, low power consumption, and flexibility for easy functionality upgrade to adapt to new malware samples. One of the main advantages of our design is the support of early predictionódetecting 46% of malware within first 30% of their execution, while 97% of the samples at 100% of their execution, with <3% false positives.



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  • Model: PROJ5863
  • 999 Units in Stock
  • Manufactured by: ClickMyProjects

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This product was added to our catalog on Monday 03 October, 2016.

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