SII Model for Self-Start Detection on the Propagation Dynamics of Modern Email Malware
Journal Title: UNKNOWN - Year 2015, Vol 4, Issue 3
Abstract
In recent years, email is the basic service for person to person communication, and email facilitates by its high speed, and process ability. The email malware exhibits two new propagation features; reinfection and self-start. Reinfection is the process by which an infected user sends out malware copies, whenever the infected user opens the malicious hyperlink or attachment. Self-Start is the process by which the infected user spreads the malware copies, whenever certain events are triggered. To solve this problem, derive a new analytical model by introducing a concept of virtual nodes. The malware detector serves as an empirical means of evaluating malware detection techniques detection capabilities. The new analytical model can efficiently predict the reinfection detection and effectively overcome the associated computational challenges.
Reed Solomon Decoder with Parallel Syndrome Computation on FPGA: A Review
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Phytochemical, GC-MS Analysis and Antibacterial Activity of Bioactive Compounds of Petroleum Ether Leaf Extracts of Salix viminalis
Medicinal Plants have been used for centuries as remedies for human illness. The objectives of the study was to isolate and analyze phytochemical constituents of Petroleum ether leaf extracts of Salix viminalis and their...
A Study on the Effectiveness of Performance Appraisal in G. B. Engineering Enterprises [P] Ltd. Thuvakudi, Trichy, Tamil Nadu, India
A Study on the Effectiveness of Performance Appraisal in G. B. Engineering Enterprises [P] Ltd. Thuvakudi, Trichy, Tamil Nadu, India
An Efficient Routing Approach for In-Network Aggregation in Wireless Sensor Networks
A wireless sensor network (WSNs) consists of many sensor nodes and these networks are deployed in different classes of applications for accurate monitoring. Wireless sensor nodes are limited energy supply has constrained...
Precocious Study of Micro Algae for Biofuels of Aundha Region Dist. Hingoli
A survey of microalgae biodiversity in Aundha region, identified, investigated for the production of a number of different biofuels including biodiesel, dried biomass, bio-oil and biogas. High oil yielding species abunda...