InnovateFPGA

Coprocessor-based Antivirus in Order to Detect Malware Preventively

AS025 »

Distinct metamodels of Smart Cities have been developed in order to solve the problems of cities in relation to different socioeconomic indicators. In view of the foregoing, information security plays an important role in guaranteeing human rights in contemporary society. From an infection by malware (malicious + software), a person or institution can suffer irrecoverable losses. As for a person, bank passwords, social networks, intimate photos or videos can be shared across the world wide web, which will affect finances, dignity and mental health. On the other side, an institution may have its vital data inaccessible and/or information from its respective customers and employees stolen. In synthesis, the theft of intimate information can lead to cases of depression, suicide and other mental disorders.
The proposed work investigates 86 commercial antiviruses. About 17% of the antiviruses did not recognize the existence of the malicious samples analyzed. Commercial antiviruses, which, even with billionaire revenue, have low effectiveness and have been criticized by incident researchers for more than a decade. Commercial antiviruses performance is based on signatures when the suspect executable is compared to a blacklist made from previous reports (and this requires that there have already had victims). Blacklists are assumed to be effectively null by the current worldwide rate of creation of virtual pests, that is 8 (eight) new malwares per second. We concluded that malwares have the ability to deceive antiviruses and other cyber-surveillance mechanisms
In order to overcome the limitations of commercial antiviruses, this project creates a core processor-based antivirus able to identify the modus operandi of a malware application before it is even executed by the user. So, our goal is to propose an antivirus, endowed with artificial intelligence, able to identify malwares through models based on fast training and high-performance neural networks. Our core processor-based antivirus is equipped with an authorial Extreme Learning Machine.
Our processor achieves an average accuracy of 99.80% in the discrimination between benign and malware executables. Preliminary results indicate that the authorial Coprocessor, built on FPGA, can speed up the response time of the proposed antivirus by about 4765 times compared to the CPU implementation employing the same FPGA. Thus, the malicious intent of the malware is preemptively detected even when executed on a slow (low processing power) device. Our antivirus enables high performance, large capacity of parallelism, and simple, low-power architecture with low power consumption. We concluded that our solution assists the main requirements for the proper operation and confection of an antivirus in hardware.

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Coprocessor-based Antivirus in Order to Detect Malware Preventively

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