Firmware Malicious Attack Detection Using Deep Poison Regression
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 8, Pages 1993-2000
AbstractA data breach is an intrusion conducted on a specific or numerous network device by malicious hackers utilizing any or more devices. A cyber assault will malicious intent deactivate machines, steal data, or use a compromised machine for many other threats. Malicious hackers offer a range of cyber-attack techniques, which include malware , phishing, spyware, denial - of - service, etc. GLM is a good beginning to learn quite rigorous analytics modeling. Poisson downward trend is used to forecast a predictor variables consisting of "count data on firmware malicious file," given yet another or more categorical variable from the cyber-attacks. The variable that we would like to predict is termed malicious API calls the divergent (the answerphishing, result spyware, goalcyber-attack or error nonpredictable term sometimes). These changes are known autonomous for variouscyberattacks(or perhaps the determinant, referential or reverser) variables to anticipate the value of a malicious files variable based. The result produced a strong real meaning of 96.25% and a low malware attack of 0.03%, thus it was trained outfitted locate a potentially malicious pattern in unknown firmware of FAI Deep PR.
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