Overview on Security Approaches in Intelligent Transportation Systems

Major standardization bodies developed and designed systems that should be used in vehicular ad-hoc networks. The Institute of Electrical and Electronics Engineers (IEEE) in America designed the wireless access in vehicular environments (WAVE) system. The European Telecommunications Standards Institute (ETSI) did come up with the "ITS-G5" system. Those Vehicular Ad-hoc Networks (VANETs) are the basis for Intelligent Transportation Systems (ITSs). They aim to efficiently communicate and provide benefits to people, ranging from improved safety to convenience. But different design and architectural choices lead to different network properties, especially security properties that are fundamentally depending on the networks architecture. To be able to compare different security architectures, different proposed approaches need to be discussed. One problem in current research is the missing focus on different approaches for trust establishment in VANETs. Therefore, this paper surveys different security issues and solutions in VANETs and we furthermore categorize these solutions into three basic trust defining architectures: centralized, decentralized and hybrid. These categories represent how trust is build in a system, i.e., in a centralized, decentralized way or even by combining both opposing approaches to a hybrid solution, which aims to inherit the benefits of both worlds. This survey defines those categories and finds that hybrid approaches are underrepresented in current research efforts.Comment: The Ninth International Conference on Emerging Security Information, Systems and Technologies - SECURWARE 2015, Venice, Italy, 201

Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems

Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Such data analytics capabilities cannot be provided by conventional cloud-centric data processing techniques whose communication and computing latency can be high. Instead, edge-centric solutions that are tailored to the unique ITS environment must be developed. In this paper, an edge analytics architecture for ITSs is introduced in which data is processed at the vehicle or roadside smart sensor level in order to overcome the ITS latency and reliability challenges. With a higher capability of passengers' mobile devices and intra-vehicle processors, such a distributed edge computing architecture can leverage deep learning techniques for reliable mobile sensing in ITSs. In this context, the ITS mobile edge analytics challenges pertaining to heterogeneous data, autonomous control, vehicular platoon control, and cyber-physical security are investigated. Then, different deep learning solutions for such challenges are proposed. The proposed deep learning solutions will enable ITS edge analytics by endowing the ITS devices with powerful computer vision and signal processing functions. Preliminary results show that the proposed edge analytics architecture, coupled with the power of deep learning algorithms, can provide a reliable, secure, and truly smart transportation environment.Comment: 5 figure

Differential Privacy Techniques for Cyber Physical Systems: A Survey

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of development of information and communication technologies (ICT).With the provision of CPSs, the security and privacy threats associated to these systems are also increasing. Passive attacks are being used by intruders to get access to private information of CPSs. In order to make CPSs data more secure, certain privacy preservation strategies such as encryption, and k-anonymity have been presented in the past. However, with the advances in CPSs architecture, these techniques also needs certain modifications. Meanwhile, differential privacy emerged as an efficient technique to protect CPSs data privacy. In this paper, we present a comprehensive survey of differential privacy techniques for CPSs. In particular, we survey the application and implementation of differential privacy in four major applications of CPSs named as energy systems, transportation systems, healthcare and medical systems, and industrial Internet of things (IIoT). Furthermore, we present open issues, challenges, and future research direction for differential privacy techniques for CPSs. This survey can serve as basis for the development of modern differential privacy techniques to address various problems and data privacy scenarios of CPSs.Comment: 46 pages, 12 figure

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward

Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services. Such a transformation---which will be fuelled by concomitant advances in technologies for machine learning (ML) and wireless communications---will enable a future vehicular ecosystem that is better featured and more efficient. However, there are lurking security problems related to the use of ML in such a critical setting where an incorrect ML decision may not only be a nuisance but can lead to loss of precious lives. In this paper, we present an in-depth overview of the various challenges associated with the application of ML in vehicular networks. In addition, we formulate the ML pipeline of CAVs and present various potential security issues associated with the adoption of ML methods. In particular, we focus on the perspective of adversarial ML attacks on CAVs and outline a solution to defend against adversarial attacks in multiple settings

Intelligent Physical Layer Security Approach for V2X Communication

Intelligent transportation systems (ITS) with advanced sensing and computing technologies are expected to support a whole new set of services including pedestrian and vehicular safety, internet access for vehicles, and eventually, driverless cars. Wireless communication is a major driving factor behind ITS, enabling reliable communication between vehicles, infrastructure, pedestrians and network, generally referred to as vehicle to everything (V2X) communication. However, the broadcast nature of wireless communication renders it prone to jamming, eavesdropping and spoofing attacks which can adversely affect ITS. Keeping in view this issue, we suggest the use of an intelligent security framework for V2X communication security, referred to as intelligent V2X security (IV2XS), to provide a reliable and robust solution capable of adapting to different conditions, scenarios and user requirements. We also identify the conditions that impact the security and describe the open challenges in achieving a realistic IV2XS system

Analysis of AODV over increased density and mobility in Intelligent Transportation System

Currently the area of VANET lacks in having some better designed algorithms to handle dynamic change and frequent disruption due to the high mobility of the vehicles. There are many techniques to disseminate messages across the moving vehicles but they are all highly dependent on some conditions involving flow, density and speed. The two techniques that are commonly used are AODV (Ad Hoc on Demand Distance Vector) and DSRC (Dedicated Short Range Communication). This work presents a detailed analysis of AODV. This study is focused on the use of AODV in Intelligent Transportation System. The limitations in the working of AODV routing protocol has been identified and proved. These limitations can be removed to some extent in order to increase the performance of vehicular networks and make the driving more safe and easy for a normal user as well as the implementation complications will be removed and an efficient system implementation will be possible.Comment: 10 pages,9 figures, 3 graphs, 2 tables, research paper; IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 5, No 1, 201

Blockchain for the Internet of Things: Present and Future

One of the key challenges to the IoT's success is how to secure and anonymize billions of IoT transactions and devices per day, an issue that still lingers despite significant research efforts over the last few years. On the other hand, technologies based on blockchain algorithms are disrupting today's cryptocurrency markets and showing tremendous potential, since they provide a distributed transaction ledger that cannot be tampered with or controlled by a single entity. Although the blockchain may present itself as a cure-all for the IoT's security and privacy challenges, significant research efforts still need to be put forth to adapt the computation-intensive blockchain algorithms to the stringent energy and processing constraints of today's IoT devices. In this paper, we provide an overview of existing literature on the topic of blockchain for IoT, and present a roadmap of research challenges that will need to be addressed to enable the usage of blockchain technologies in the IoT

A Survey of Data Fusion in Smart City Applications

The advancement of various research sectors such as Internet of Things (IoT), Machine Learning, Data Mining, Big Data, and Communication Technology has shed some light in transforming an urban city integrating the aforementioned techniques to a commonly known term - Smart City. With the emergence of smart city, plethora of data sources have been made available for wide variety of applications. The common technique for handling multiple data sources is data fusion, where it improves data output quality or extracts knowledge from the raw data. In order to cater evergrowing highly complicated applications, studies in smart city have to utilize data from various sources and evaluate their performance based on multiple aspects. To this end, we introduce a multi-perspectives classification of the data fusion to evaluate the smart city applications. Moreover, we applied the proposed multi-perspectives classification to evaluate selected applications in each domain of the smart city. We conclude the paper by discussing potential future direction and challenges of data fusion integration.Comment: Accepted and To be published in Elsevier Information Fusio

Fundamental Considerations around Scenario-Based Testing for Automated Driving

The homologation of automated vehicles, being safety-critical complex systems, requires sound evidence for their safe operability. Traditionally, verification and validation activities are guided by a combination of ISO 26262 and ISO/PAS 21448, together with distance-based testing. Starting at SAE Level 3, such approaches become infeasible, resulting in the need for novel methods. Scenario-based testing is regarded as a possible enabler for verification and validation of automated vehicles. Its effectiveness, however, rests on the consistency and substantiality of the arguments used in each step of the process. In this work, we sketch a generic framework around scenario-based testing and analyze contemporary approaches to the individual steps. For each step, we describe its function, discuss proposed approaches and solutions, and identify the underlying arguments, principles and assumptions. As a result, we present a list of fundamental considerations for which evidences need to be gathered in order for scenario-based testing to support the homologation of automated vehicles.Comment: Copyright 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Beyond the Dolev-Yao Model: Realistic Application-Specific Attacker Models for Applications Using Vehicular Communication

In recent time, the standards for Vehicular Ad-hoc Networks (VANETs) and Intelligent Transportation Systems (ITSs) matured and scientific and industry interest is high especially as autonomous driving gets a lot of media attention. Autonomous driving and other assistance systems for cars make heavy use of VANETs to exchange information.They may provide more comfort, security and safety for drivers. However, it is of crucial importance for the user's trust in these assistance systems that they could not be influenced by malicious users. VANETs are likely attack vectors for such malicious users, hence application-specific security requirements must be considered during the design of applications using VANETs. In literature, many attacks on vehicular communication have been described but attacks on specific vehicular networking applications are often missing. This paper fills in this gap by describing standardized vehicular networking applications, defining and extending previous attacker models, and using the resulting new models to characterize the possible attackers interested in the specific vehicular networking application. The attacker models presented in this paper hopefully provide great benefit for the scientific community and industry as they allow to compare security evaluations of different works, characterize attackers, their intentions and help to plan application-specific security controls for vehicular networking applications.Comment: The Tenth International Conference on Emerging Security Information, Systems and Technologies - SECURWARE 2016, Nice, France, 201