Keeping Internet users protected from cyberattacks and other threats is one of the most prominent security challenges for network operators nowadays. Among other critical threats, distributed denial-of-service (DDoS) becomes one of the most widespread attacks in the Internet, which is very challenging to mitigate appropriately as DDoS attacks cause the system to stop working by resource exhaustion. Software-defined networking (SDN) has recently emerged as a new networking technology offering unprecedented programmability that allows network operators to configure and manage their infrastructures dynamically. The flexible processing and centralized management of the SDN controller allow flexibly deploying complex security algorithms and mitigation methods. In this paper, we propose a novel DDoS attack mitigation in SDN-based Internet Service Provider (ISP) networks for TCP-SYN and ICMP flood attacks utilizing machine learning approach, i.e., K-Nearest-Neighbor (KNN) and XGBoost. By deploying a testbed, we implement the proposed algorithms, evaluate their accuracy, and address the trade-off between the accuracy and mitigation efficiency. Through extensive experiments, the results show that the algorithms can efficiently mitigate the attack by over 98.0% while benign traffic is not affected.

A new tirucallane-type triterpenoid igniarine (1), and four known compounds meshimakobnol A (2), meshimakobnol B (3), ergosterol (4) and ergosterol peroxide (5), were purified from the methanol extracts of the fruiting bodies of Phellinus igniarius (DC. ex Fr.) Quél. The structure of 1 was elucidated using a combination of 1D and 2D NMR techniques and HR-ESI-MS analyses. In addition, the isolated compounds were examined for their cytotoxicity against several tumour cell lines and part of the tested compounds demonstrated weak cytotoxicity.

One new cytochalasin daldinin (1), 2 known cytochalasins 2 and 3, along with 2 steroids 4 and 5, were characterized from the methanol extracts of the fruiting bodies of Daldinia concentrica. The structure of new compound 1 was elucidated using a combination of 1-and 2-dimensional nuclear magnetic resonance spectroscopic and high-resolution electrospray ionization mass spectrometric analyses. In addition, the isolated compounds were examined for their cytotoxicity against several tumor cell lines and the tested compounds demonstrated moderate-to-weak cytotoxicity.

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In software industry nowadays, Agile Software Development methods have been largely adopted. Agile Software Development methods themselves can be considered a certain level of reducing projects risks. However, optimization of software project scheduling has always been big challenges in both practice and academia, since industrial software development is a highly complex and dynamic process. There is also a need for a probabilistic method that better model and predict uncertainty in software projects. This paper proposes an enhanced method and algorithm by combining optimized agile iteration scheduling and the ability to predict and handle risks in resource-constrained contexts of Bayesian Networks. Based on the method, a software was developed as a support tool for managers to control their project schedules. The tool also provides a reliable set of strategies of sequencing tasks in agile iteration scheduling.