Background and Objective: Almost all malwares running on web-server are php codes. Then, the present paper creates a NGAV (Next Generation Antivirus) expert in auditing threats web-based, specifically from php files, in real time.Methods: In our methodology, the malicious behaviors, of the personal computer, serve as input attributes of the statistical learning machines. In all, our dynamic feature extraction monitors 11,777 behaviors that the web fileless attack can do when launched directly from a malicious web-server to a listening service in a personal computer.Results: Our NGAV achieves an average 99.95% accuracy in the distinction between benign and malware web scripts. Distinct initial conditions and kernels of neural networks classifiers are investigated in order to maximize the accuracy of our NGAV.Conclusions: Our NGAV can supply the limitations of the commercial antiviruses as for the detection of Web fileless attack. In opposition of analysis of individual events, our engine employs authorial Web-server Sandbox, machine learning, and artificial intelligence in order to identify malicious Web-sites.

Although the development of high dynamic range images allowed broadening sky vault analysis for lighting research purposes, the sky classification is still performed using subjective methods. There is no established metric for comparison, only isolated approaches that do not address the characteristics of the images. This paper presents a proposed multidimensional method for analysing light from sky vault high dynamic range images. A Matlab routine was applied. It uses a classification system to check the relevance of each image attribute, choosing the most suitable sky model from the ISO 15469:2004 (CIE S 011/E:2003) standard. The results, that can be plotted or exported, indicate that the routine is able to choose the most relevant model for the photographed sky, thus allowing the creation of a sky classification database.

Java vulnerabilities correspond to 91% of all exploits observed on the worldwide web. The present work aims to create antivirus software with machine learning and artificial intelligence and master in Java malware detection. Within the proposed methodology, the suspected JAR sample is executed to intentionally infect the Windows OS monitored in a controlled environment. In all, our antivirus monitors and considers, statistically, 6824 actions that the suspected JAR file can perform when executed. Our antivirus achieved an average performance of 91.58% in the distinction between benign and malware JAR files. Different initial conditions, learning functions and architectures of our antivirus are investigated. The limitations of commercial antiviruses can be supplied by intelligent antiviruses. Instead of blacklist-based models, our antivirus allows JAR malware detection preventively and not reactively as Oracle’s Java and traditional antivirus modus operandi.

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