A Taxonomy and Review of Lightweight Blockchain Solutions for Internet of Things Networks

Internet of things networks have spread to most digital applications in the past years. Examples of these networks include smart home networks, wireless sensor networks, Internet of Flying Things, and many others. One of the main difficulties that confront these networks is the security of their information and communications. A large number of solutions have been proposed to safeguard these networks from various types of cyberattacks. Among these solutions is the blockchain, which gained popularity in the last few years due to its strong security characteristics, such as immutability, cryptography, and distributed consensus. However, implementing the blockchain framework within the devices of these networks is very challenging, due to the limited resources of these devices and the resource-demanding requirements of the blockchain. For this reason, a large number of researchers proposed various types of lightweight blockchain solutions for resource-constrained networks. The "lightweight" aspect can be related to the blockchain architecture, device authentication, cryptography model, consensus algorithm, or storage method. In this paper, we present a taxonomy of the lightweight blockchain solutions that have been proposed in the literature and discuss the different methods that have been applied so far in each "lightweight" category. Our review highlights the missing points in existing systems and paves the way to building a complete lightweight blockchain solution for resource-constrained networks.Comment: 64 pages, 11 figures

MAKE-IT—A Lightweight Mutual Authentication and Key Exchange Protocol for Industrial Internet of Things

Continuous development of the Industrial Internet of Things (IIoT) has opened up enormous opportunities for the engineers to enhance the efficiency of the machines. Despite the development, many industry administrators still fear to use Internet for operating their machines due to untrusted nature of the communication channel. The utilization of internet for managing industrial operations can be widespread adopted if the authentication of the entities are performed and trust is ensured. The traditional schemes with their inherent security issues and other complexities, cannot be directly deployed to resource constrained network devices. Therefore, we have proposed a strong mutual authentication and secret key exchange protocol to address the vulnerabilities of the existing schemes. We have used various cryptography operations such as hashing, ciphering, and so forth, for providing secure mutual authentication and secret key exchange between different entities to restrict unauthorized access. Performance and security analysis clearly demonstrates that the proposed work is energy efficient (computation and communication inexpensive) and more robust against the attacks in comparison to the traditional scheme

Fenrir: Blockchain-based Inter-company App-Store for the Automotive Industry

International audienceFrom a software evolution perspective, more actors are integrating the in-vehicle software development cycle. In this process, software deployment mechanisms must include more complex techniques to meet the software verification and traceability levels required by industry safety and security constraints. In this context, we propose Fenrir, a public inter-automaker blockchain-based application store framework in which each automaker retains software installability control. This application store also aims to ensure traceability and security, while also keeping the solution light in terms of both energy consumption and computing requirements, to be used in constrained environments. We implemented Fenrir in a heterogeneous architecture composed of both on-board (bearing an ARM Cortex-A53 chipset, already deployed in cars) and off-board (Amazon EC2) nodes for a realistic automotive use-case scenario, in which we evaluated its performance and energy consumption. We demonstrate that the overheads added by our solution for an entire software deployment pipeline-comprising both deployment and usage of already deployed software packages-depends mainly on the verification mechanism, whose impact is not significant, i.e., 3.8% for the worst-case scenario and 0.3% for a typical scenario

A Comprehensive Collection and Analysis Model for the Drone Forensics Field

Unmanned aerial vehicles (UAVs) are adaptable and rapid mobile boards that can be applied to several purposes, especially in smart cities. These involve traffic observation, environmental monitoring, and public safety. The need to realize effective drone forensic processes has mainly been reinforced by drone-based evidence. Drone-based evidence collection and preservation entails accumulating and collecting digital evidence from the drone of the victim for subsequent analysis and presentation. Digital evidence must, however, be collected and analyzed in a forensically sound manner using the appropriate collection and analysis methodologies and tools to preserve the integrity of the evidence. For this purpose, various collection and analysis models have been proposed for drone forensics based on the existing literature; several models are inclined towards specific scenarios and drone systems. As a result, the literature lacks a suitable and standardized drone-based collection and analysis model devoid of commonalities, which can solve future problems that may arise in the drone forensics field. Therefore, this paper has three contributions: (a) studies the machine learning existing in the literature in the context of handling drone data to discover criminal actions, (b) highlights the existing forensic models proposed for drone forensics, and (c) proposes a novel comprehensive collection and analysis forensic model (CCAFM) applicable to the drone forensics field using the design science research approach. The proposed CCAFM consists of three main processes: (1) acquisition and preservation, (2) reconstruction and analysis, and (3) post-investigation process. CCAFM contextually leverages the initially proposed models herein incorporated in this study. CCAFM allows digital forensic investigators to collect, protect, rebuild, and examine volatile and nonvolatile items from the suspected drone based on scientific forensic techniques. Therefore, it enables sharing of knowledge on drone forensic investigation among practitioners working in the forensics domain

Assessing the Competing Characteristics of Privacy and Safety within Vehicular Ad Hoc Networks

The introduction of Vehicle-to-Vehicle (V2V) communication has the promise of decreasing vehicle collisions, congestion, and emissions. However, this technology places safety and privacy at odds; an increase of safety applications will likely result in the decrease of consumer privacy. The National Highway Traffic Safety Administration (NHTSA) has proposed the Security Credential Management System (SCMS) as the back end infrastructure for maintaining, distributing, and revoking vehicle certificates attached to every Basic Safety Message (BSM). This Public Key Infrastructure (PKI) scheme is designed around the philosophy of maintaining user privacy through the separation of functions to prevent any one subcomponent from identifying users. However, because of the high precision of the data elements within each message this design cannot prevent large scale third-party BSM collection and pseudonym linking resulting in privacy loss. In addition, this philosophy creates an extraordinarily complex and heavily distributed system. In response to this difficulty, this thesis proposes a data ambiguity method to bridge privacy and safety within the context of interconnected vehicles. The objective in doing so is to preserve both Vehicle-to-Vehicle (V2V) safety applications and consumer privacy. A Vehicular Ad-Hoc Network (VANET) metric classification is introduced that explores five fundamental pillars of VANETs. These pillars (Safety, Privacy, Cost, Efficiency, Stability) are applied to four different systems: Non-V2V environment, the aforementioned SCMS, the group-pseudonym based Vehicle Based Security System (VBSS), and VBSS with Dithering (VBSS-D) which includes the data ambiguity method of dithering. By using these evaluation criteria, the advantages and disadvantages of bringing each system to fruition is showcased

Generative Models for Novelty Detection Applications in abnormal event and situational changedetection from data series

Novelty detection is a process for distinguishing the observations that differ in some respect from the observations that the model is trained on. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains observations that were not known at the training time. In other words, the novelty class is often is not presented during the training phase or not well defined. In light of the above, one-class classifiers and generative methods can efficiently model such problems. However, due to the unavailability of data from the novelty class, training an end-to-end model is a challenging task itself. Therefore, detecting the Novel classes in unsupervised and semi-supervised settings is a crucial step in such tasks. In this thesis, we propose several methods to model the novelty detection problem in unsupervised and semi-supervised fashion. The proposed frameworks applied to different related applications of anomaly and outlier detection tasks. The results show the superior of our proposed methods in compare to the baselines and state-of-the-art methods