Recently, Software Defined Networks (SDN) and Cloud Computing have been widely adopted by researchers as well as industry. However, widespread acceptance of these novel networking paradigms has been hampered by the security threats. Advances in the processing technologies have helped attackers in increasing the attacks too, for instance, the development of Denial of Service (DoS) attacks to Distributed DoS (DDoS) attacks which are seldom identified by conventional firewalls. In this paper, we present the state of art of the DDoS attacks in SDN and cloud computing scenarios. Especially, we focus on the analysis of the SDN and cloud computing architecture. Besides, we also overview the research works and open problems in identifying and tackling the DDoS attacks.

Background: Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug–drug, drug–disease, and protein–protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches. Results: We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and $$LRW_5$$L R W 5 are the top 3 best performers on all five datasets. Conclusions: This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug–drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks.

One of the key goals of Informetrics is to identify citation-based popular articles among so many other aspects, such as determining popular research topics, identifying influential scholars, and predicting hot trends in science. These can be achieved by applying network science approaches to scientific networks and formulating the problem as a popular (most-cited) node ranking task. To rank the papers based on their future citation gain. In this work a deep learning based framework is proposed. Which helps in automatic node level feature extraction and can make node level prediction in dynamic graphs such as citation networks. To achieve this we have learned global ranking preserve d dimensional node embedding. We have only considered temporal features, which makes it suitable for generalisation to other networks. Although our model can consider node level explicit features also. Further we have given novel cost function which can be easily solve ranking problem for dynamic graphs using probabilistic regression method. Which can be easily optimised. Another novelty of our work is our model can be trained using different snapshots of the graph and different time. Further trained model can be used to make future prediction. The proposed model has been tested on an arXiv paper citation network using six standard information retrieval-based metrics. The results show that our proposed model outperforms, on average, other state-of-the-art static models as well as dynamic node ranking models. The outcome of this research study leads to informed data-driven decision-making in science, such as the allocation and distribution of research funds and investment in strategic research centers. When considering past time window size as 10 months and making prediction after 10 months our proposed model’s performance on various ranking based evaluation metrics are as follows: AUC-0.974, Kendal’s rank correlation tau-0.455, Precision- 0.643, Novelty-0.0456, Temporal novelty-0.375 and on NDCG-0.949. Our model is able to make long term trend prediction with just training on short time window.

Network science plays a big role in the representation of real-world phenomena such as user-item bipartite networks presented in e-commerce or social media platforms. It provides researchers with tools and techniques to solve complex real-world problems. Identifying and predicting future popularity and importance of items in e-commerce or social media platform is a challenging task. Some items gain popularity repeatedly over time while some become popular and novel only once. This work aims to identify the key-factors: popularity and novelty. To do so, we consider two types of novelty predictions: items appearing in the popular ranking list for the first time; and items which were not in the popular list in the past time window, but might have been popular before the recent past time window. In order to identify the popular items, a careful consideration of macro-level analysis is needed. In this work we propose a model, which exploits item level information over a span of time to rank the importance of the item. We considered ageing or decay effect along with the recent link-gain of the items. We test our proposed model on four various real-world datasets using four information retrieval based metrics.