Data centric trust evaluation and prediction framework for IOT

© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas

Information-Centric Design and Implementation for Underwater Acoustic Networks

Over the past decade, Underwater Acoustic Networks (UANs) have received extensive attention due to their vast benefits in academia and industry alike. However, due to the overall magnitude and harsh characteristics of underwater environments, standard wireless network techniques will fail because current technology and energy restrictions limit underwater devices due to delayed acoustic communications. To help manage these limitations we utilize Information-Centric Networking (ICN). More importantly, we look at ICN\u27s paradigm shift from traditional TCP/IP architecture to improve data handling and enhance network efficiency. By utilizing some of ICN\u27s techniques, such as data naming hierarchy, we can reevaluate each component of the network\u27s protocol stack given current underwater limitations to study the vast solutions and perspectives Information-Centric architectures can provide to UANs. First, we propose a routing strategy used to manage and route large data files in a network prone to high mobility. Therefore, due to UANs limited transmitting capability, we passively store sensed data and adaptively find the best path. Furthermore, we introduce adapted Named Data Networking (NDN) components to improve upon routing robustness and adaptiveness. Beyond naming data, we use tracers to assist in tracking stored data locations without using other excess means such as flooding. By collaborating tracer consistency with routing path awareness our protocol can adaptively manage faulty or high mobility nodes. Through this incorporation of varied NDN techniques, we are able to see notable improvements in routing efficiency. Second, we analyze the effects of Denial of Service (DoS) attacks on upper layer protocols. Since UANs are typically resource restrained, malicious users can advantageously create fake traffic to burden the already constrained network. While ICN techniques only provide basic DoS restriction we must expand our detection and restriction technique to meet the unique demands of UANs. To provide enhanced security against DoS we construct an algorithm to detect and restrict against these types of attacks while adapting to meet acoustic characteristics. To better extend this work we incorporate three node behavior techniques using probabilistic, adaptive, and predictive approaches for detecting malicious traits. Thirdly, to depict and test protocols in UANs, simulators are commonly used due to their accessibility and controlled testing aspects. For this section, we review Aqua-Sim, a discrete event-driven open-source underwater simulator. To enhance the core aspect of this simulator we first rewrite the current architecture and transition Aqua-Sim to the newest core simulator, NS-3. Following this, we clean up redundant features spread out between the various underwater layers. Additionally, we fully integrate the diverse NS-3 API within our simulator. By revamping previous code layout we are able to improve architecture modularity and child class expandability. New features are also introduced including localization and synchronization support, busy terminal problem support, multi-channel support, transmission range uncertainty modules, external noise generators, channel trace-driven support, security module, and an adapted NDN module. Additionally, we provide extended documentation to assist in user development. Simulation testing shows improved memory management and continuous validity in comparison to other underwater simulators and past iterations of Aqua-Sim

Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices

Smart devices with built-in sensors, computational capabilities, and network connectivity have become increasingly pervasive. The crowds of smart devices offer opportunities to collectively sense and perform computing tasks in an unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine learning framework for a crowd of smart devices, which can solve a wide range of learning problems for crowdsensing data with differential privacy guarantees. Crowd-ML endows a crowdsensing system with an ability to learn classifiers or predictors online from crowdsensing data privately with minimal computational overheads on devices and servers, suitable for a practical and large-scale employment of the framework. We analyze the performance and the scalability of Crowd-ML, and implement the system with off-the-shelf smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML with real and simulated experiments under various conditions

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Can Cybersecurity Be Proactive? A Big Data Approach and Challenges

The cybersecurity community typically reacts to attacks after they occur. Being reactive is costly and can be fatal where attacks threaten lives, important data, or mission success. But can cybersecurity be done proactively? Our research capitalizes on the Germination Period—the time lag between hacker communities discussing software flaw types and flaws actually being exploited—where proactive measures can be taken. We argue for a novel proactive approach, utilizing big data, for (I) identifying potential attacks before they come to fruition; and based on this identification, (II) developing preventive counter-measures. The big data approach resulted in our vision of the Proactive Cybersecurity System (PCS), a layered, modular service platform that applies big data collection and processing tools to a wide variety of unstructured data sources to predict vulnerabilities and develop countermeasures. Our exploratory study is the first to show the promise of this novel proactive approach and illuminates challenges that need to be addressed

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

Internet-of-Things (IoT) Security Threats: Attacks on Communication Interface

Internet of Things (IoT) devices collect and process information from remote places and have significantly increased the productivity of distributed systems or individuals. Due to the limited budget on power consumption, IoT devices typically do not include security features such as advanced data encryption and device authentication. In general, the hardware components deployed in IoT devices are not from high end markets. As a result, the integrity and security assurance of most IoT devices are questionable. For example, adversary can implement a Hardware Trojan (HT) in the fabrication process for the IoT hardware devices to cause information leak or malfunctions. In this work, we investigate the security threats on IoT with a special emphasis on the attacks that aim for compromising the communication interface between IoT devices and their main processing host. First, we analyze the security threats on low-energy smart light bulbs, and then we exploit the limitation of Bluetooth protocols to monitor the unencrypted data packet from the air-gapped network. Second, we examine the security vulnerabilities of single-wire serial communication protocol used in data exchange between a sensor and a microcontroller. Third, we implement a Man-in-the-Middle (MITM) attack on a master-slave communication protocol adopted in Inter-integrated Circuit (I2C) interface. Our MITM attack is executed by an analog hardware Trojan, which crosses the boundary between digital and analog worlds. Furthermore, an obfuscated Trojan detection method(ADobf) is proposed to monitor the abnormal behaviors induced by analog Trojans on the I2C interface

A Survey on Wireless Sensor Network Security

Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Security in sensor networks is, therefore, a particularly challenging task. This paper discusses the current state of the art in security mechanisms for WSNs. Various types of attacks are discussed and their countermeasures presented. A brief discussion on the future direction of research in WSN security is also included.Comment: 24 pages, 4 figures, 2 table