Due to the reasons such as the rapid growth and spread of network services, mobile devices, and online users on the Internet leading to a remarkable increase in the amount of data. Almost every industry is trying to cope with this huge data. Big data phenomenon has begun to gain importance. However, it is not only very difficult to store big data and analyse them with traditional applications, but also it has challenging privacy and security problems. For this reason, this paper discusses the big data, its ecosystem, concerns on big data and presents comparative view of big data privacy and security approaches in literature in terms of infrastructure, application, and data. By grouping these applications an overall perspective of security and privacy issues in big data is suggested.

Cyber-attacks was organized in a simple and random way in the past. However attacks are carried out systematically and long term nowadays. In addition, the high calculation volume and continuous changes in network data distribution have made it more difficult to analyze data and detect abnormal behaviors within. For this reason, big data solutions have become essential. In this paper, firstly network anomaly and attack detection studies on big data has been reviewed. Then, a public big network data was analyzed with a new unsupervised anomaly detection approach on Apache Spark cluster in Azure HD Insight. Finally, the results obtained from a case study were evaluated, %96 accuracy was achieved. The results were visualized after dimension reduction using Principal Component Analysis (PCA). The identified anomalies may provide usable outputs to understand the behavior of the network, distinguishing the attacks, providing better cyber security, and protecting critical infrastructures.

This paper presents a review on smart grids and big data. In order to achieve this, available articles in the literature were reviewed in perception with the features of big data and smart grids. Potential issues for smart grids and big data analytics were discussed. The recent solutions and challenges in smart grids and big data analytics were presented. The technologies used for smart big data analytics were evaluated. The contribution of this article was then presented. The results have been shown that “data” is now a new feature to be added as a main component of power systems.

There are many articles published in driving/driver behavior (DDB) but few of them have focused on DDB with big data in the literature. The reasons for this might be the lack of media coverage, data, expertise or big data perspectives. This paper presents a big data perspective for investigating the DDB based on models, data features, and experiences. For this purpose, DDB studies were reviewed and grouped into six perspectives. 6V's of big data (volume, velocity, variety, veracity, vulnerability and value) were also revised and discussed how these V's were compatible with DDB data. Finally, the use of big data in DDB analysis was discussed and some suggestions were presented. The lack of big data perception on DDB research was also overviewed and a new perspective was presented for researchers, applicants or business managers to do more effective and suitable studies in the field of big data DDB.

This paper proposes ensemble strategies for the deep learning object detection models carried out by combining the variants of a model and different models to enhance the anatomical and pathological object detection performance in brain MRI. In this study, with the help of the novel Gazi Brains 2020 dataset, five different anatomical parts and one pathological part that can be observed in brain MRI were identified, such as the region of interest, eye, optic nerves, lateral ventricles, third ventricle, and a whole tumor. Firstly, comprehensive benchmarking of the nine state-of-the-art object detection models was carried out to determine the capabilities of the models in detecting the anatomical and pathological parts. Then, four different ensemble strategies for nine object detectors were applied to boost the detection performance using the bounding box fusion technique. The ensemble of individual model variants increased the anatomical and pathological object detection performance by up to 10% in terms of the mean average precision (mAP). In addition, considering the class-based average precision (AP) value of the anatomical parts, an up to 18% AP improvement was achieved. Similarly, the ensemble strategy of the best different models outperformed the best individual model by 3.3% mAP. Additionally, while an up to 7% better FAUC, which is the area under the TPR vs. FPPI curve, was achieved on the Gazi Brains 2020 dataset, a 2% better FAUC score was obtained on the BraTS 2020 dataset. The proposed ensemble strategies were found to be much more efficient in finding the anatomical and pathological parts with a small number of anatomic objects, such as the optic nerve and third ventricle, and producing higher TPR values, especially at low FPPI values, compared to the best individual methods.