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The multi-mode resource-constrained project scheduling problem (MRCPSP) consists of activities, in which multiple execution modes are available for each activity and must be scheduled subject to precedence and resource constraints such that the makespan is minimized. This paper considers MRCPSP with fuzzy activity duration times. Due to the nonrenewable resources and the multiple modes, this problem is NP-hard; therefore, we implement an intelligent algorithm looking for a feasible solution integrating genetic algorithm and fuzzy set.

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Protecting websites and applications from cyber-threats is vital for any organization. A Web application firewall (WAF) prevents attacks to damaging applications. This provides a web security by filtering and monitoring traffic network to protect against attacks. A WAF solution based on the anomaly detection can identify zero-day attacks. Deep learning is the state-of-the-art method that is widely used to detect attacks in the anomaly-based WAF area. Although deep learning has demonstrated excellent results on anomaly detection tasks in web requests, there is trade-off between false-positive and missed-attack rates which is a key problem in WAF systems. On the other hand, anomaly detection methods suffer adjusting threshold-level to distinguish attack and normal traffic. In this paper, first we proposed a model based on Deep Support Vector Data Description (Deep SVDD), then we compare two feature extraction strategies, one-hot and bigram, on the raw requests. Second to overcome threshold challenges, we introduce a novel end-to-end algorithm Auto-Threshold Deep SVDD (ATDSVDD) to determine an appropriate threshold during the learning process. As a result we compare our model with other deep models on CSIC-2010 and ECML/PKDD-2007 datasets. Results show ATDSVDD on bigram feature data have better performance in terms of accuracy and generalization. <br>