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Recently Distributed Denial-of-Service (DDoS) are becoming more and more sophisticated, which makes the existing defence systems not capable of tolerating by themselves against wide-ranging attacks. Thus, collaborative protection mitigation has become a needed alternative to extend defence mechanisms. However, the existing coordinated DDoS mitigation approaches either they require a complex configuration or are highly-priced. Blockchain technology offers a solution that reduces the complexity of signalling DDoS system, as well as a platform where many autonomous systems (Ass) can share hardware resources and defence capabilities for an effective DDoS defence. In this work, we also used a Deep learning DDoS detection system; we identify individual DDoS attack class and also define whether the incoming traffic is legitimate or attack. By classifying the attack traffic flow separately, our proposed mitigation technique could deny only the specific traffic causing the attack, instead of blocking all the traffic coming towards the victim(s).

Mobile users are always demanding extended availability in their battery use. Together with enlarged battery capacity, fast charging is one approach that provides an improved user experience in battery use. Recently, device manufacturers have been developing a variety of fast charging techniques for mobile devices. However, the existing techniques severely reduce the charging power when the device is in use while charging. We experimentally demonstrate that the primary cause of the reduction in the charge speed during device use is to cope with the performance degradation incurred by heat generation. We then propose an adaptive charging scheme, called Always-on Quick Charging, which enables fast charging especially when the device is in use. The key idea of our approach is to adjust the charging power while ensuring that the heat generated by the charging does not affect the performance. The proposed scheme is implemented in Google’s Pixel 2XL smartphone. The experiment with a real-world usage scenario shows that the charging speed of the proposed scheme is up to 2.4 times faster than the default scheme, while preserving device performance.

In this paper, we consider the control problem of spring forming machine. Helical springs are usually made of long stock wire which is fed into forming machine. A spring forming machine can force wire into a coiled shape, but the length of the produced spring is varying according to the internal stress of wire, mechanical uncertainty of the forming machine etc. To improve the accuracy of free length of spring produced by forming machine, we propose to adopt minimum-variance control strategy for each production cycle.

Recently, as countries have switched over from analog broadcasting to digital broadcasting, various services for digital broadcasting have been introduced to enhance customer satisfaction. However, users still experience difficulties in selecting what they want to watch due to the large volume of TV contents. Often, social trends and TV programs are closely related; TV programs could be produced to deal with some hot issues and public interest could be formed for some TV program. Nowadays, such public interest can be detected as trends from popular SNS such as Twitter. In this paper, we propose a SNS trend-based TV program recommendation scheme. To do that, we first extract trend keywords from Twitter stream data and augment them semantically by referring to recommendation log and portal sites. Finally, we analyze EPGs to find matched TV programs to the trends. We implemented a prototype system to evaluate the effectiveness of our scheme.