Polynomial Based Dynamic Key Management for Secure Cluster Communication in Wireless Mobile Sensor Network

For inter and intra cluster communication, member nodes jointly build a mutual session key called cluster key to allow secure communication. Most existing schemes for cluster key management use messages exchange among the member nodes within a cluster for the new cluster key establishment when a node leaves or joins a cluster. This causes significant communication and computation costs. Furthermore, the secure distribution of cluster keys among member nodes in frequently changing environments is a difficult task without encryption and decryption operations. For secure cluster key management, we utilized polynomial (P) to accomplish effective intra-cluster key management and produced polynomial for making an inter-cluster key distribution. The main contribution is to generate polynomials and broadcast to nodes whenever a change occurs in a network or demanding nodes for secure key management. The presented scheme supports scalability for an increasing number of nodes using polynomials. The proposed scheme increases the lifetime of the network by decreasing the key pool size

A Two-Level Information Modelling Translation Methodology and Framework to Achieve Semantic Interoperability in Constrained GeoObservational Sensor Systems

As geographical observational data capture, storage and sharing technologies such as in situ remote monitoring systems and spatial data infrastructures evolve, the vision of a Digital Earth, first articulated by Al Gore in 1998 is getting ever closer. However, there are still many challenges and open research questions. For example, data quality, provenance and heterogeneity remain an issue due to the complexity of geo-spatial data and information representation. Observational data are often inadequately semantically enriched by geo-observational information systems or spatial data infrastructures and so they often do not fully capture the true meaning of the associated datasets. Furthermore, data models underpinning these information systems are typically too rigid in their data representation to allow for the ever-changing and evolving nature of geo-spatial domain concepts. This impoverished approach to observational data representation reduces the ability of multi-disciplinary practitioners to share information in an interoperable and computable way. The health domain experiences similar challenges with representing complex and evolving domain information concepts. Within any complex domain (such as Earth system science or health) two categories or levels of domain concepts exist. Those concepts that remain stable over a long period of time, and those concepts that are prone to change, as the domain knowledge evolves, and new discoveries are made. Health informaticians have developed a sophisticated two-level modelling systems design approach for electronic health documentation over many years, and with the use of archetypes, have shown how data, information, and knowledge interoperability among heterogenous systems can be achieved. This research investigates whether two-level modelling can be translated from the health domain to the geo-spatial domain and applied to observing scenarios to achieve semantic interoperability within and between spatial data infrastructures, beyond what is possible with current state-of-the-art approaches. A detailed review of state-of-the-art SDIs, geo-spatial standards and the two-level modelling methodology was performed. A cross-domain translation methodology was developed, and a proof-of-concept geo-spatial two-level modelling framework was defined and implemented. The Open Geospatial Consortium’s (OGC) Observations & Measurements (O&M) standard was re-profiled to aid investigation of the two-level information modelling approach. An evaluation of the method was undertaken using II specific use-case scenarios. Information modelling was performed using the two-level modelling method to show how existing historical ocean observing datasets can be expressed semantically and harmonized using two-level modelling. Also, the flexibility of the approach was investigated by applying the method to an air quality monitoring scenario using a technologically constrained monitoring sensor system. This work has demonstrated that two-level modelling can be translated to the geospatial domain and then further developed to be used within a constrained technological sensor system; using traditional wireless sensor networks, semantic web technologies and Internet of Things based technologies. Domain specific evaluation results show that twolevel modelling presents a viable approach to achieve semantic interoperability between constrained geo-observational sensor systems and spatial data infrastructures for ocean observing and city based air quality observing scenarios. This has been demonstrated through the re-purposing of selected, existing geospatial data models and standards. However, it was found that re-using existing standards requires careful ontological analysis per domain concept and so caution is recommended in assuming the wider applicability of the approach. While the benefits of adopting a two-level information modelling approach to geospatial information modelling are potentially great, it was found that translation to a new domain is complex. The complexity of the approach was found to be a barrier to adoption, especially in commercial based projects where standards implementation is low on implementation road maps and the perceived benefits of standards adherence are low. Arising from this work, a novel set of base software components, methods and fundamental geo-archetypes have been developed. However, during this work it was not possible to form the required rich community of supporters to fully validate geoarchetypes. Therefore, the findings of this work are not exhaustive, and the archetype models produced are only indicative. The findings of this work can be used as the basis to encourage further investigation and uptake of two-level modelling within the Earth system science and geo-spatial domain. Ultimately, the outcomes of this work are to recommend further development and evaluation of the approach, building on the positive results thus far, and the base software artefacts developed to support the approach

Wearable Wireless Devices

No abstract available

Wearable Wireless Devices

No abstract available

Defense in Depth of Resource-Constrained Devices

The emergent next generation of computing, the so-called Internet of Things (IoT), presents significant challenges to security, privacy, and trust. The devices commonly used in IoT scenarios are often resource-constrained with reduced computational strength, limited power consumption, and stringent availability requirements. Additionally, at least in the consumer arena, time-to-market is often prioritized at the expense of quality assurance and security. An initial lack of standards has compounded the problems arising from this rapid development. However, the explosive growth in the number and types of IoT devices has now created a multitude of competing standards and technology silos resulting in a highly fragmented threat model. Tens of billions of these devices have been deployed in consumers\u27 homes and industrial settings. From smart toasters and personal health monitors to industrial controls in energy delivery networks, these devices wield significant influence on our daily lives. They are privy to highly sensitive, often personal data and responsible for real-world, security-critical, physical processes. As such, these internet-connected things are highly valuable and vulnerable targets for exploitation. Current security measures, such as reactionary policies and ad hoc patching, are not adequate at this scale. This thesis presents a multi-layered, defense in depth, approach to preventing and mitigating a myriad of vulnerabilities associated with the above challenges. To secure the pre-boot environment, we demonstrate a hardware-based secure boot process for devices lacking secure memory. We introduce a novel implementation of remote attestation backed by blockchain technologies to address hardware and software integrity concerns for the long-running, unsupervised, and rarely patched systems found in industrial IoT settings. Moving into the software layer, we present a unique method of intraprocess memory isolation as a barrier to several prevalent classes of software vulnerabilities. Finally, we exhibit work on network analysis and intrusion detection for the low-power, low-latency, and low-bandwidth wireless networks common to IoT applications. By targeting these areas of the hardware-software stack, we seek to establish a trustworthy system that extends from power-on through application runtime

Strategies for Improving Data Protection to Reduce Data Loss from Cyberattacks

Accidental and targeted data breaches threaten sustainable business practices and personal privacy, exposing all types of businesses to increased data loss and financial impacts. This single case study was conducted in a medium-sized enterprise located in Brevard County, Florida, to explore the successful data protection strategies employed by the information system and information technology business leaders. Actor-network theory was the conceptual framework for the study with a graphical syntax to model data protection strategies. Data were collected from semistructured interviews of 3 business leaders, archival documents, and field notes. Data were analyzed using thematic, analytic, and software analysis, and methodological triangulation. Three themes materialized from the data analyses: people--inferring security personnel, network engineers, system engineers, and qualified personnel to know how to monitor data; processes--inferring the activities required to protect data from data loss; and technology--inferring scientific knowledge used by people to protect data from data loss. The findings are indicative of successful application of data protection strategies and may be modeled to assess vulnerabilities from technical and nontechnical threats impacting risk and loss of sensitive data. The implications of this study for positive social change include the potential to alter attitudes toward data protection, creating a better environment for people to live and work; reduce recovery costs resulting from Internet crimes, improving social well-being; and enhance methods for the protection of sensitive, proprietary, and personally identifiable information, which advances the privacy rights for society

Analysis and design of security mechanisms in the context of Advanced Persistent Threats against critical infrastructures

Industry 4.0 can be defined as the digitization of all components within the industry, by combining productive processes with leading information and communication technologies. Whereas this integration has several benefits, it has also facilitated the emergence of several attack vectors. These can be leveraged to perpetrate sophisticated attacks such as an Advanced Persistent Threat (APT), that ultimately disrupts and damages critical infrastructural operations with a severe impact. This doctoral thesis aims to study and design security mechanisms capable of detecting and tracing APTs to ensure the continuity of the production line. Although the basic tools to detect individual attack vectors of an APT have already been developed, it is important to integrate holistic defense solutions in existing critical infrastructures that are capable of addressing all potential threats. Additionally, it is necessary to prospectively analyze the requirements that these systems have to satisfy after the integration of novel services in the upcoming years. To fulfill these goals, we define a framework for the detection and traceability of APTs in Industry 4.0, which is aimed to fill the gap between classic security mechanisms and APTs. The premise is to retrieve data about the production chain at all levels to correlate events in a distributed way, enabling the traceability of an APT throughout its entire life cycle. Ultimately, these mechanisms make it possible to holistically detect and anticipate attacks in a timely and autonomous way, to deter the propagation and minimize their impact. As a means to validate this framework, we propose some correlation algorithms that implement it (such as the Opinion Dynamics solution) and carry out different experiments that compare the accuracy of response techniques that take advantage of these traceability features. Similarly, we conduct a study on the feasibility of these detection systems in various Industry 4.0 scenarios