How the Internet of Things Technology Enhances Emergency Response Operations

The Internet of Things (IoT) is a novel paradigmthat connects the pervasive presence around us of a variety of things or objects to the Internet by using wireless/wired technologies to reach desired goals. Since the concept of the IoT was introduced in 2005, we see the deployment of a new generation of networked smart objects with communication, sensory and action capabilities for numerous applications, mainly in global supply chain management, environment monitoring and other non-stress environments. This paper introduces the IoT technology for use in the emergency management community. Considering the information required for supporting three sequential and distinct rhythms in emergency response operations: mobilization rhythm, preliminary situation assessment rhythm, and intervention rhythm, the paper proposes a modified task-technology fit approach that is used to investigate how the IoT technology can be incorporated into the three rhythms and enhance emergency response operations. The findings from our research support our two hypotheses: H1: IoT technology fits the identified information requirements; and H2: IoT technology provides added value to emergency response operations in terms of obtaining efficient cooperation, accurate situational awareness, and complete visibility of resources

Experience in Using RDF in Agent-Mediated Knowledge Architectures

We report on experience with using RDF to provide a rich content language for use with FIPA agent toolkits, and on RDFS as a metadata language. We emphasise their utility for programmers working in agent applica-tions and their value in Agent-Oriented Software En-gineering. Agent applications covered include Intelli-gent Information Agents, and agents forming Virtual Organisations. We believe our experience vindicates more direct use of RDF, including use of RDF triples, in programming knowledge architectures for a variety of applications

Management of Distributed Denial of Service Attack in Cloud Computing Environment

Cloud Computing is a recent technology, it provides a simple and unambiguous taxonomy of three service models available to cloud consumers: software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). There are several security issues with the delivery model of cloud. Our work is to dealing with management of Distributed Denial of Service attack on SaaS model of cloud computing environment. If DDoS attack is capable enough to violate the Service Level Agreement (SLA) on availability it can cause huge financial claim and it will affect the reputation of industries in a market. So our basic aim is to design a management model that will avoid the SLA violation on availability due to a DDoS attack. Our model works in three stages (1) Detection of DDoS attack (2) Avoidance of DDoS attack and (3) prevention of DDoS attack. Feedforward Neural Network method for detection of DDoS attack. Sigmoid function is used as Neural modal for obtaining the desire output. The Supervised learning model adjusts the connection weight and bias value of ANN model. Using predefined datasets to train the ANN model. For the Avoidance of DDoS attack data center dynamically allocate the resources on virtual machines. A new virtual machine will be clone based on the image file of the original. Replicate the resources on a virtual machine in order to avoid the SLA violation (Availability issues). Message Authentication Code (MAC) is used for prevention of DDoS. The message Authentication code increases the overhead on the network. The design goal is to decrease the overhead of MAC on the network so we are using Router Packet Filtering method that reduces that MAC overhead on packet over the network. This lower overhead increases the speed of authentication and reduces the amount of dynamically allocated resources that will prevent the violation of the SLA on cloud computing

Minimization of DDoS false alarm rate in Network Security; Refining fusion through correlation

Intrusion Detection Systems are designed to monitor a network environment and generate alerts whenever abnormal activities are detected. However, the number of these alerts can be very large making their evaluation a difficult task for a security analyst. Alert management techniques reduce alert volume significantly and potentially improve detection performance of an Intrusion Detection System. This thesis work presents a framework to improve the effectiveness and efficiency of an Intrusion Detection System by significantly reducing the false positive alerts and increasing the ability to spot an actual intrusion for Distributed Denial of Service attacks. Proposed sensor fusion technique addresses the issues relating the optimality of decision-making through correlation in multiple sensors framework. The fusion process is based on combining belief through Dempster Shafer rule of combination along with associating belief with each type of alert and combining them by using Subjective Logic based on Jøsang theory. Moreover, the reliability factor for any Intrusion Detection System is also addressed accordingly in order to minimize the chance of false diagnose of the final network state. A considerable number of simulations are conducted in order to determine the optimal performance of the proposed prototype

Fighting Cybercrime After \u3cem\u3eUnited States v. Jones\u3c/em\u3e

In a landmark non-decision last term, five Justices of the United States Supreme Court would have held that citizens possess a Fourth Amendment right to expect that certain quantities of information about them will remain private, even if they have no such expectations with respect to any of the information or data constituting that whole. This quantitative approach to evaluating and protecting Fourth Amendment rights is certainly novel and raises serious conceptual, doctrinal, and practical challenges. In other works, we have met these challenges by engaging in a careful analysis of this “mosaic theory” and by proposing that courts focus on the technologies that make collecting and aggregating large quantities of information possible. In those efforts, we focused on reasonable expectations held by “the people” that they will not be subjected to broad and indiscriminate surveillance. These expectations are anchored in Founding-era concerns about the capacity for unfettered search powers to promote an authoritarian surveillance state. Although we also readily acknowledged that there are legitimate and competing governmental and law enforcement interests at stake in the deployment and use of surveillance technologies that implicate reasonable interests in quantitative privacy, we did little more. In this Article, we begin to address that omission by focusing on the legitimate governmental and law enforcement interests at stake in preventing, detecting, and prosecuting cyber-harassment and healthcare fraud