Disasters (natural or man-made) can be lethal to human life, the environment, and infrastructure. The recent advancements in the Internet of Things (IoT) and the evolution in big data analytics (BDA) technologies have provided an open opportunity to develop highly needed disaster resilient smart city environments. In this paper, we propose and discuss the novel reference architecture and philosophy of a disaster resilient smart city (DRSC) through the integration of the IoT and BDA technologies. The proposed architecture offers a generic solution for disaster management activities in smart city incentives. A combination of the Hadoop Ecosystem and Spark are reviewed to develop an efficient DRSC environment that supports both real-time and offline analysis. The implementation model of the environment consists of data harvesting, data aggregation, data pre-processing, and big data analytics and service platform. A variety of datasets (i.e., smart buildings, city pollution, traffic simulator, and twitter) are utilized for the validation and evaluation of the system to detect and generate alerts for a fire in a building, pollution level in the city, emergency evacuation path, and the collection of information about natural disasters (i.e., earthquakes and tsunamis). The evaluation of the system efficiency is measured in terms of processing time and throughput that demonstrates the performance superiority of the proposed architecture. Moreover, the key challenges faced are identified and briefly discussed. INDEX TERMS Big data analytics, Internet of Things, smart city, disaster management, Hadoop, spark, smart data analytics, geo-social media analytics, disaster resilient smart city.

Internet of Things (IoT) is realized by the idea of free flow of information amongst various low power embedded devices that use Internet to communicate with one another. It is predicted that the IoT will be widely deployed and it will find applicability in various domains of life. Demands of IoT have lately attracted huge attention and organizations are excited about the business value of the data that will be generated by the IoT paradigm. On the other hand, IoT have various security and privacy concerns for the end users that limit its proliferation. In this paper we have identified, categorized and discussed various security challenges and state of the art efforts to resolve these challenges. Keywords: Security in IoT, Privacy in IoT, DoS in IoT, Secure routing in IoTIn [17] the authors identified and discussed three representative sensing applications. They are personal sensing, designated sensing and community sensing each requiring heterogeneous security and privacy

The recent development of big data analytics (BDA) and the Internet of Things (IoT) technologies create a huge opportunity for both disaster management systems and disaster-related authorities (emergency responders, police, public health, and fire departments) to acquire state-of-the-art assistance and improved insights for accurate and timely decision-making. The motivation behind this research is to pave the way for effective utilization of the available opportunities that the BDA and IoT collaboratively offer to predict, understand and monitor disaster situations. Most of the conventional disaster management systems lack the support for multiple new data sources and real-time big data processing tools that can assist decision makers with quick and accurate results. This paper highlights the importance of BDA and IoT for disaster management and investigates recent studies directed towards the same. We classify a thematic taxonomy with several related attributes and inspect the prevalent solutions to propose a conceptual reference model for the deployment of BDA-and IoT-based disaster management environments. The reference model with its proposed integrated parameters can provide guidelines to harvest, transmit, manage, and analyze disaster data from various data sources to deliver updated and valuable information for disaster management. We also enumerate some important use cases from a disaster management perspective. Finally, we highlight the main research challenges that need to be addressed in such an important field of research. INDEX TERMS Big data analytics, data sources, disaster communications, disaster management, Internet of Things, reference model, taxonomy.

Distributed denial of service (DDoS) flooding attacks are one of the main methods to destroy the availability of critical online services today. These DDoS attacks cannot be prevented ahead of time, and once in place, they overwhelm the victim with huge volume of traffic and render it incapable of performing normal communication or crashes it completely. Any delays in detecting the flooding attacks completely halts the network services. With the rapid increase of DDoS volume and frequency, the new generation of DDoS detection mechanisms are needed to deal with huge attack volume in reasonable and affordable response time. In this paper, we propose HADEC, a Hadoop-based live DDoS detection framework to tackle efficient analysis of flooding attacks by harnessing MapReduce and HDFS. We implemented a counter-based DDoS detection algorithm for four major flooding attacks (TCP-SYN, HTTP GET, UDP, and ICMP) in MapReduce, consisting of map and reduce functions. We deployed a testbed to evaluate the performance of HADEC framework for live DDoS detection on low-end commodity hardware. Based on the experiment, we showed that HADEC is capable of processing and detecting DDoS attacks in near to real time.

Distributed Denial of Service (DDoS) flooding attacks are one of the biggest challenges to the availability of online services today. These DDoS attacks overwhelm the victim with huge volume of traffic and render it incapable of performing normal communication or crashes it completely. If there are delays in detecting the flooding attacks, nothing much can be done except to manually disconnect the victim and fix the problem. With the rapid increase of DDoS volume and frequency, the current DDoS detection technologies are challenged to deal with huge attack volume in reasonable and affordable response time.In this paper, we propose HADEC, a Hadoop based Live DDoS Detection framework to tackle efficient analysis of flooding attacks by harnessing MapReduce and HDFS. We implemented a counter-based DDoS detection algorithm for four major flooding attacks (TCP-SYN, HTTP GET, UDP and ICMP) in MapReduce, consisting of map and reduce functions. We deployed a testbed to evaluate the performance of HADEC framework for live DDoS detection. Based on the experiment we showed that HADEC is capable of processing and detecting DDoS attacks in affordable time.