Property-based network discovery of IoT nodes using bloom filters

© Springer International Publishing AG 2018. As the number of IoT devices is exponentially growing, and IoT networks are expanding in their size and complexity, timely device discovery is becoming a pressing concern. The extreme (and constantly growing) number of network nodes, dynamically connecting to and disconnecting from a network, renders existing routing techniques, such as multicasting and broadcasting, unscalable, especially when using the IPv6 128-bit addresses. To address this limitation, this paper discusses the potential of implementing the IoT device discovery, based on device properties, such as type, functionality, location, etc., and presents an approach to enable property-based access to IoT nodes using Bloom filters. The proposed approach demonstrates space- and network-efficient characteristics, as well as provides an opportunity to perform device discovery at various granularity levels

Targeted content delivery to IoT devices using bloom filters

© Springer International Publishing AG 2017. The increasing number of smart interactive devices connected to the network opens new business opportunities for digital content and advertisement providers, interested in reaching out to new customer audiences. To this end, they employ various device discovery and data collection techniques to gather user- and device-specific information in order to build a user profile and deliver targeted content accordingly. However, the extreme (and constantly growing) number of smart devices, dynamically connecting to and disconnecting from a network in the IoT scenario, renders existing routing techniques, such as multicasting and broadcasting, unscalable, especially when using the IPv6 128-bit addresses. Moreover, these existing solutions can hardly provide information about technical capabilities of end devices. To address this limitation, this paper discusses the potential of implementing the IoT device discovery for device-specific content delivery, based on device properties, such as screen size and resolution, network connectivity, presence of speakers, supported languages, etc., and presents an approach to enable property-based access to IoT nodes using Bloom filters. The proposed approach demonstrates space- and network-efficient characteristics, as well as provides an opportunity to perform device discovery at various granularity levels

Enhancing cyber assets visibility for effective attack surface management : Cyber Asset Attack Surface Management based on Knowledge Graph

The contemporary digital landscape is filled with challenges, chief among them being the management and security of cyber assets, including the ever-growing shadow IT. The evolving nature of the technology landscape has resulted in an expansive system of solutions, making it challenging to select and deploy compatible solutions in a structured manner. This thesis explores the critical role of Cyber Asset Attack Surface Management (CAASM) technologies in managing cyber attack surfaces, focusing on the open-source CAASM tool, Starbase, by JupiterOne. It starts by underlining the importance of comprehending the cyber assets that need defending. It acknowledges the Cyber Defense Matrix as a methodical and flexible approach to understanding and addressing cyber security challenges. A comprehensive analysis of market trends and business needs validated the necessity of asset security management tools as fundamental components in firms' security journeys. CAASM has been selected as a promising solution among various tools due to its capabilities, ease of use, and seamless integration with cloud environments using APIs, addressing shadow IT challenges. A practical use case involving the integration of Starbase with GitHub was developed to demonstrate the CAASM's usability and flexibility in managing cyber assets in organizations of varying sizes. The use case enhanced the knowledge graph's aesthetics and usability using Neo4j Desktop and Neo4j Bloom, making it accessible and insightful even for non-technical users. The thesis concludes with practical guidelines in the appendices and on GitHub for reproducing the use case

Energy-aware strategy for data forwarding in IoT ecosystem

The Internet of Things (IoT) is looming technology rapidly attracting many industries and drawing research attention. Although the scale of IoT-applications is very large, the capabilities of the IoT-devices are limited, especially in terms of energy. However, various research works have been done to alleviate these shortcomings, but the schemes introduced in the literature are complex and difficult to implement in practical scenarios. Therefore, considering the energy consumption of heterogeneous nodes in IoT eco-system, a simple energy-efficient routing technique is proposed. The proposed system has also employed an SDN controller that acts as a centralized manager to control and monitor network services, there by restricting the access of selfish nodes to the network. The proposed system constructs an analytical algorithm that provides reliable data transmission operations and controls energy consumption using a strategic mechanism where the path selection process is performed based on the remaining energy of adjacent nodes located in the direction of the destination node. The proposed energy-efficient data forwarding mechanism is compared with the existing AODV routing technique. The simulation result demonstrates that the protocol is superior to AODV in terms of packet delivery rate, throughput, and end-to-end delay

A Holistic Analysis of Internet of Things (IoT) Security : Principles, Practices, and New Perspectives

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