Visible to the public Biblio

Filters: Keyword is controller area network security  [Clear All Filters]
2021-09-07
Fernando, Praveen, Wei, Jin.  2020.  Blockchain-Powered Software Defined Network-Enabled Networking Infrastructure for Cloud Management. 2020 IEEE 17th Annual Consumer Communications Networking Conference (CCNC). :1–6.
Cloud architecture has become a valuable solution for different applications, such as big data analytics, due to its high degree of availability, scalability and strategic value. However, there still remain challenges in managing cloud architecture, in areas such as cloud security. In this paper, we exploit software-defined networking (SDN) and blockchain technologies to secure cloud management platforms from a networking perspective. We develop a blockchain-powered SDN-enabled networking infrastructure in which the integration between blockchain-based security and autonomy management layer and multi-controller SDN networking layer is defined to enhance the integrity of the control and management messages. Furthermore, our proposed networking infrastructure also enables the autonomous bandwidth provisioning to enhance the availability of cloud architecture. In the simulation section, we evaluate the performance of our proposed blockchain-powered SDN-enabled networking infrastructure by considering different scenarios.
Hossain, Md Delwar, Inoue, Hiroyuki, Ochiai, Hideya, FALL, Doudou, Kadobayashi, Youki.  2020.  Long Short-Term Memory-Based Intrusion Detection System for In-Vehicle Controller Area Network Bus. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). :10–17.
The Controller Area Network (CAN) bus system works inside connected cars as a central system for communication between electronic control units (ECUs). Despite its central importance, the CAN does not support an authentication mechanism, i.e., CAN messages are broadcast without basic security features. As a result, it is easy for attackers to launch attacks at the CAN bus network system. Attackers can compromise the CAN bus system in several ways: denial of service, fuzzing, spoofing, etc. It is imperative to devise methodologies to protect modern cars against the aforementioned attacks. In this paper, we propose a Long Short-Term Memory (LSTM)-based Intrusion Detection System (IDS) to detect and mitigate the CAN bus network attacks. We first inject attacks at the CAN bus system in a car that we have at our disposal to generate the attack dataset, which we use to test and train our model. Our results demonstrate that our classifier is efficient in detecting the CAN attacks. We achieved a detection accuracy of 99.9949%.
Lenard, Teri, Bolboacă, Roland, Genge, Bela.  2020.  LOKI: A Lightweight Cryptographic Key Distribution Protocol for Controller Area Networks. 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP). :513–519.
The recent advancement in the automotive sector has led to a technological explosion. As a result, the modern car provides a wide range of features supported by state of the art hardware and software. Unfortunately, while this is the case of most major components, in the same vehicle we find dozens of sensors and sub-systems built over legacy hardware and software with limited computational capabilities. This paper presents LOKI, a lightweight cryptographic key distribution scheme applicable in the case of the classical invehicle communication systems. The LOKI protocol stands out compared to already proposed protocols in the literature due to its ability to use only a single broadcast message to initiate the generation of a new cryptographic key across a group of nodes. It's lightweight key derivation algorithm takes advantage of a reverse hash chain traversal algorithm to generate fresh session keys. Experimental results consisting of a laboratory-scale system based on Vector Informatik's CANoe simulation environment demonstrate the effectiveness of the developed methodology and its seamless impact manifested on the network.
Young, Clinton, Svoboda, Jordan, Zambreno, Joseph.  2020.  Towards Reverse Engineering Controller Area Network Messages Using Machine Learning. 2020 IEEE 6th World Forum on Internet of Things (WF-IoT). :1–6.
The automotive Controller Area Network (CAN) allows Electronic Control Units (ECUs) to communicate with each other and control various vehicular functions such as engine and braking control. Consequently CAN and ECUs are high priority targets for hackers. As CAN implementation details are held as proprietary information by vehicle manufacturers, it can be challenging to decode and correlate CAN messages to specific vehicle operations. To understand the precise meanings of CAN messages, reverse engineering techniques that are time-consuming, manually intensive, and require a physical vehicle are typically used. This work aims to address the process of reverse engineering CAN messages for their functionality by creating a machine learning classifier that analyzes messages and determines their relationship to other messages and vehicular functions. Our work examines CAN traffic of different vehicles and standards to show that it can be applied to a wide arrangement of vehicles. The results show that the function of CAN messages can be determined without the need to manually reverse engineer a physical vehicle.
Sami, Muhammad, Ibarra, Matthew, Esparza, Anamaria C., Al-Jufout, Saleh, Aliasgari, Mehrdad, Mozumdar, Mohammad.  2020.  Rapid, Multi-vehicle and Feed-forward Neural Network based Intrusion Detection System for Controller Area Network Bus. 2020 IEEE Green Energy and Smart Systems Conference (IGESSC). :1–6.
In this paper, an Intrusion Detection System (IDS) in the Controller Area Network (CAN) bus of modern vehicles has been proposed. NESLIDS is an anomaly detection algorithm based on the supervised Deep Neural Network (DNN) architecture that is designed to counter three critical attack categories: Denial-of-service (DoS), fuzzy, and impersonation attacks. Our research scope included modifying DNN parameters, e.g. number of hidden layer neurons, batch size, and activation functions according to how well it maximized detection accuracy and minimized the false positive rate (FPR) for these attacks. Our methodology consisted of collecting CAN Bus data from online and in real-time, injecting attack data after data collection, preprocessing in Python, training the DNN, and testing the model with different datasets. Results show that the proposed IDS effectively detects all attack types for both types of datasets. NESLIDS outperforms existing approaches in terms of accuracy, scalability, and low false alarm rates.
Kalkan, Soner Can, Sahingoz, Ozgur Koray.  2020.  In-Vehicle Intrusion Detection System on Controller Area Network with Machine Learning Models. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–6.
Parallel with the developing world, transportation technologies have started to expand and change significantly year by year. This change brings with it some inevitable problems. Increasing human population and growing transportation-needs result many accidents in urban and rural areas, and this recursively results extra traffic problems and fuel consumption. It is obvious that the issues brought by this spiral loop needed to be solved with the use of some new technological achievements. In this context, self-driving cars or automated vehicles concepts are seen as a good solution. However, this also brings some additional problems with it. Currently many cars are provided with some digital security systems, which are examined in two phases, internal and external. These systems are constructed in the car by using some type of embedded system (such as the Controller Area Network (CAN)) which are needed to be protected form outsider cyberattacks. These attack can be detected by several ways such as rule based system, anomaly based systems, list based systems, etc. The current literature showed that researchers focused on the use of some artificial intelligence techniques for the detection of this type of attack. In this study, an intrusion detection system based on machine learning is proposed for the CAN security, which is the in-vehicle communication structure. As a result of the study, it has been observed that the decision tree-based ensemble learning models results the best performance in the tested models. Additionally, all models have a very good accuracy levels.
Lenard, Teri, Bolboacă, Roland, Genge, Bela, Haller, Piroska.  2020.  MixCAN: Mixed and Backward-Compatible Data Authentication Scheme for Controller Area Networks. 2020 IFIP Networking Conference (Networking). :395–403.
The massive proliferation of state of the art interfaces into the automotive sector has triggered a revolution in terms of the technological ecosystem that is found in today's modern car. Accordingly, on the one hand, we find dozens of Electronic Control Units (ECUs) running several hundred MB of code, and more and more sophisticated dashboards with integrated wireless communications. On the other hand, in the same vehicle we find the underlying communication infrastructure struggling to keep up with the pace of these radical changes. This paper presents MixCAN (MIXed data authentication for Control Area Networks), an approach for mixing different message signatures (i.e., authentication tags) in order to reduce the overhead of Controller Area Network (CAN) communications. MixCAN leverages the attributes of Bloom Filters in order to ensure that an ECU can sign messages with different CAN identifiers (i.e., mix different message signatures), and that other ECUs can verify the signature for a subset of monitored CAN identifiers. Extensive experimental results based on Vectors Informatik's CANoe/CANalyzer simulation environment and the data set provided by Hacking and Countermeasure Research Lab (HCRL) confirm the validity and applicability of the developed approach. Subsequent experiments including a test bed consisting of Raspberry Pi 3 Model B+ systems equipped with CAN communication modules demonstrate the practical integration of MixCAN in real automotive systems.
Schell, Oleg, Kneib, Marcel.  2020.  VALID: Voltage-Based Lightweight Intrusion Detection for the Controller Area Network. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :225–232.
The Controller Area Network (CAN), a broadcasting bus for intra-vehicle communication, does not provide any security mechanisms, although it is implemented in almost every vehicle. Attackers can exploit this issue, transmit malicious messages unnoticeably and cause severe harm. As the utilization of Message Authentication Codes (MACs) is only possible to a limited extent in resource-constrained systems, the focus is put on the development of Intrusion Detection Systems (IDSs). Due to their simple idea of operation, current developments are increasingly utilizing physical signal properties like voltages to realize these systems. Although the feasibility for CAN-based networks could be demonstrated, the least approaches consider the constrained resource-availability of vehicular hardware. To close this gap, we present Voltage-Based Lightweight Intrusion Detection (VALID), which provides physics-based intrusion detection with low resource requirements. By utilizing solely the individual voltage levels on the network during communication, the system detects unauthorized message transmissions without any sophisticated sampling approaches and feature calculations. Having performed evaluations on data from two real vehicles, we show that VALID is not only able to detect intrusions with an accuracy of 99.54 %, but additionally is capable of identifying the attack source reliably. These properties make VALID one of the most lightweight intrusion detection approaches that is ready-to-use, as it can be easily implemented on hardware already installed in vehicles and does not require any further components. Additionally, this allows existing platforms to be retrofitted and vehicular security systems to be improved and extended.
Sunny, Jerin, Sankaran, Sriram, Saraswat, Vishal.  2020.  A Hybrid Approach for Fast Anomaly Detection in Controller Area Networks. 2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). :1–6.
Recent advancements in the field of in-vehicle network and wireless communication, has been steadily progressing. Also, the advent of technologies such as Vehicular Adhoc Networks (VANET) and Intelligent Transportation System (ITS), has transformed modern automobiles into a sophisticated cyber-physical system rather than just a isolated mechanical device. Modern automobiles rely on many electronic control units communicating over the Controller Area Network (CAN) bus. Although protecting the car's external interfaces is an vital part of preventing attacks, detecting malicious activity on the CAN bus is an effective second line of defense against attacks. This paper proposes a hybrid anomaly detection system for CAN bus based on patterns of recurring messages and time interval of messages. The proposed method does not require modifications in CAN bus. The proposed system is evaluated on real CAN bus traffic with simulated attack scenarios. Results obtained show that our proposed system achieved a good detection rate with fast response times.
Zhang, Xing, Cui, Xiaotong, Cheng, Kefei, Zhang, Liang.  2020.  A Convolutional Encoder Network for Intrusion Detection in Controller Area Networks. 2020 16th International Conference on Computational Intelligence and Security (CIS). :366–369.
Integrated with various electronic control units (ECUs), vehicles are becoming more intelligent with the assistance of essential connections. However, the interaction with the outside world raises great concerns on cyber-attacks. As a main standard for in-vehicle network, Controller Area Network (CAN) does not have any built-in security mechanisms to guarantee a secure communication. This increases risks of denial of service, remote control attacks by an attacker, posing serious threats to underlying vehicles, property and human lives. As a result, it is urgent to develop an effective in-vehicle network intrusion detection system (IDS) for better security. In this paper, we propose a Feature-based Sliding Window (FSW) to extract the feature of CAN Data Field and CAN IDs. Then we construct a convolutional encoder network (CEN) to detect network intrusion of CAN networks. The proposed FSW-CEN method is evaluated on real-world datasets. The experimental results show that compared to traditional data processing methods and convolutional neural networks, our method is able to detect attacks with a higher accuracy in terms of detection accuracy and false negative rate.
2020-07-20
Nishida, Kanata, Nozaki, Yusuke, Yoshikawa, Masaya.  2019.  Security Evaluation of Counter Synchronization Method for CAN Against DoS Attack. 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE). :166–167.
MAC using a counter value in message authentication for in-vehicle network prevents replay attack. When synchronization deviation of the counter value occurs between the sender and receiver, a message cannot be authenticated correctly because the generated MACs are different. Thus, a counter synchronization method has been proposed. In addition, injection and replay attack of a synchronization message for the synchronization method have been performed. However, DoS attack on the synchronization method has not been conducted. This study performs DoS attack in order to evaluate security of the synchronization method. Experimental results reveal the vulnerability of the synchronization method against DoS attack.
Xu, Tangwei, Lu, Xiaozhen, Xiao, Liang, Tang, Yuliang, Dai, Huaiyu.  2019.  Voltage Based Authentication for Controller Area Networks with Reinforcement Learning. ICC 2019 - 2019 IEEE International Conference on Communications (ICC). :1–5.
Controller area networks (CANs) are vulnerable to spoofing attacks such as frame falsifying attacks, as electronic control units (ECUs) send and receive messages without any authentication and encryption. In this paper, we propose a physical authentication scheme that exploits the voltage features of the ECU signals on the CAN bus and applies reinforcement learning to choose the authentication mode such as the protection level and test threshold. This scheme enables a monitor node to optimize the authentication mode via trial-and-error without knowing the CAN bus signal model and spoofing model. Experimental results show that the proposed authentication scheme can significantly improve the authentication accuracy and response compared with a benchmark scheme.
Boumiza, Safa, Braham, Rafik.  2019.  An Anomaly Detector for CAN Bus Networks in Autonomous Cars based on Neural Networks. 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). :1–6.
The domain of securing in-vehicle networks has attracted both academic and industrial researchers due to high danger of attacks on drivers and passengers. While securing wired and wireless interfaces is important to defend against these threats, detecting attacks is still the critical phase to construct a robust secure system. There are only a few results on securing communication inside vehicles using anomaly-detection techniques despite their efficiencies in systems that need real-time detection. Therefore, we propose an intrusion detection system (IDS) based on Multi-Layer Perceptron (MLP) neural network for Controller Area Networks (CAN) bus. This IDS divides data according to the ID field of CAN packets using K-means clustering algorithm, then it extracts suitable features and uses them to train and construct the neural network. The proposed IDS works for each ID separately and finally it combines their individual decisions to construct the final score and generates alert in the presence of attack. The strength of our intrusion detection method is that it works simultaneously for two types of attacks which will eliminate the use of several separate IDS and thus reduce the complexity and cost of implementation.
Lekidis, Alexios, Barosan, Ion.  2019.  Model-based simulation and threat analysis of in-vehicle networks. 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS). :1–8.
Automotive systems are currently undergoing a rapid evolution through the integration of the Internet of Things (IoT) and Software Defined Networking (SDN) technologies. The main focus of this evolution is to improve the driving experience, including automated controls, intelligent navigation and safety systems. Moreover, the extremely rapid pace that such technologies are brought into the vehicles, necessitates the presence of adequate testing of new features to avoid operational errors. Apart from testing though, IoT and SDN technologies also widen the threat landscape of cyber-security risks due to the amount of connectivity interfaces that are nowadays exposed in vehicles. In this paper we present a new method, based on OMNET++, for testing new in-vehicle features and assessing security risks through network simulation. The method is demonstrated through a case-study on a Toyota Prius, whose network data are analyzed for the detection of anomalies caused from security threats or operational errors.
Hayward, Jake, Tomlinson, Andrew, Bryans, Jeremy.  2019.  Adding Cyberattacks To An Industry-Leading CAN Simulator. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :9–16.
Recent years have seen an increase in the data usage in cars, particularly as they become more autonomous and connected. With the rise in data use have come concerns about automotive cyber-security. An in-vehicle network shown to be particularly vulnerable is the Controller Area Network (CAN), which is the communication bus used by the car's safety critical and performance critical components. Cyber attacks on the CAN have been demonstrated, leading to research to develop attack detection and attack prevention systems. Such research requires representative attack demonstrations and data for testing. Obtaining this data is problematical due to the expense, danger and impracticality of using real cars on roads or tracks for example attacks. Whilst CAN simulators are available, these tend to be configured for testing conformance and functionality, rather than analysing security and cyber vulnerability. We therefore adapt a leading, industry-standard, CAN simulator to incorporate a core set of cyber attacks that are representative of those proposed by other researchers. Our adaptation allows the user to configure the attacks, and can be added easily to the free version of the simulator. Here we describe the simulator and, after reviewing the attacks that have been demonstrated and discussing their commonalities, we outline the attacks that we have incorporated into the simulator.
Fowler, Daniel S., Bryans, Jeremy, Cheah, Madeline, Wooderson, Paul, Shaikh, Siraj A..  2019.  A Method for Constructing Automotive Cybersecurity Tests, a CAN Fuzz Testing Example. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :1–8.
There is a need for new tools and techniques to aid automotive engineers performing cybersecurity testing on connected car systems. This is in order to support the principle of secure-by-design. Our research has produced a method to construct useful automotive security tooling and tests. It has been used to implement Controller Area Network (CAN) fuzz testing (a dynamic security test) via a prototype CAN fuzzer. The black-box fuzz testing of a laboratory vehicle's display ECU demonstrates the value of a fuzzer in the automotive field, revealing bugs in the ECU software, and weaknesses in the vehicle's systems design.
Castiglione, Arcangelo, Palmieri, Francesco, Colace, Francesco, Lombardi, Marco, Santaniello, Domenico.  2019.  Lightweight Ciphers in Automotive Networks: A Preliminary Approach. 2019 4th International Conference on System Reliability and Safety (ICSRS). :142–147.
Nowadays, the growing need to connect modern vehicles through computer networks leads to increased risks of cyberattacks. The internal network, which governs the several electronic components of a vehicle, is becoming increasingly overexposed to external attacks. The Controller Area Network (CAN) protocol, used to interconnect those devices is the key point of the internal network of modern vehicles. Therefore, securing such protocol is crucial to ensure a safe driving experience. However, the CAN is a standard that has undergone little changes since it was introduced in 1983. More precisely, in an attempt to reduce latency, the transfer of information remains unencrypted, which today represents a weak point in the protocol. Hence, the need to protect communications, without introducing low-level alterations, while preserving the performance characteristics of the protocol. In this work, we investigate the possibility of using symmetric encryption algorithms for securing messages exchanged by CAN protocol. In particular, we evaluate the using of lightweight ciphers to secure CAN-level communication. Such ciphers represent a reliable solution on hardware-constrained devices, such as microcontrollers.
Rumez, Marcel, Dürrwang, Jürgen, Brecht, Tim, Steinshorn, Timo, Neugebauer, Peter, Kriesten, Reiner, Sax, Eric.  2019.  CAN Radar: Sensing Physical Devices in CAN Networks based on Time Domain Reflectometry. 2019 IEEE Vehicular Networking Conference (VNC). :1–8.
The presence of security vulnerabilities in automotive networks has already been shown by various publications in recent years. Due to the specification of the Controller Area Network (CAN) as a broadcast medium without security mechanisms, attackers are able to read transmitted messages without being noticed and to inject malicious messages. In order to detect potential attackers within a network or software system as early as possible, Intrusion Detection Systems (IDSs) are prevalent. Many approaches for vehicles are based on techniques which are able to detect deviations from specified CAN network behaviour regarding protocol or payload properties. However, it is challenging to detect attackers who secretly connect to CAN networks and do not actively participate in bus traffic. In this paper, we present an approach that is capable of successfully detecting unknown CAN devices and determining the distance (cable length) between the attacker device and our sensing unit based on Time Domain Reflectometry (TDR) technique. We evaluated our approach on a real vehicle network.
Tanksale, Vinayak.  2019.  Intrusion Detection For Controller Area Network Using Support Vector Machines. 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems Workshops (MASSW). :121–126.
Controller Area Network is the most widely adopted communication standard in automobiles. The CAN protocol is robust and is designed to minimize overhead. The light-weight nature of this protocol implies that it can't efficiently process secure communication. With the exponential increase in automobile communications, there is an urgent need for efficient and effective security countermeasures. We propose a support vector machine based intrusion detection system that is able to detect anomalous behavior with high accuracy. We outline a process for parameter selection and feature vector selection. We identify strengths and weaknesses of our system and propose to extend our work for time-series based data.
Urien, Pascal.  2019.  Designing Attacks Against Automotive Control Area Network Bus and Electronic Control Units. 2019 16th IEEE Annual Consumer Communications Networking Conference (CCNC). :1–4.
Security is a critical issue for new car generation targeting intelligent transportation systems (ITS), involving autonomous and connected vehicles. In this work we designed a low cost CAN probe and defined analysis tools in order to build attack scenarios. We reuse some threats identified by a previous work. Future researches will address new security protocols.
2019-12-16
Guo, Wenbo, Mu, Dongliang, Xu, Jun, Su, Purui, Wang, Gang, Xing, Xinyu.  2018.  LEMNA: Explaining Deep Learning Based Security Applications. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :364–379.
While deep learning has shown a great potential in various domains, the lack of transparency has limited its application in security or safety-critical areas. Existing research has attempted to develop explanation techniques to provide interpretable explanations for each classification decision. Unfortunately, current methods are optimized for non-security tasks ( e.g., image analysis). Their key assumptions are often violated in security applications, leading to a poor explanation fidelity. In this paper, we propose LEMNA, a high-fidelity explanation method dedicated for security applications. Given an input data sample, LEMNA generates a small set of interpretable features to explain how the input sample is classified. The core idea is to approximate a local area of the complex deep learning decision boundary using a simple interpretable model. The local interpretable model is specially designed to (1) handle feature dependency to better work with security applications ( e.g., binary code analysis); and (2) handle nonlinear local boundaries to boost explanation fidelity. We evaluate our system using two popular deep learning applications in security (a malware classifier, and a function start detector for binary reverse-engineering). Extensive evaluations show that LEMNA's explanation has a much higher fidelity level compared to existing methods. In addition, we demonstrate practical use cases of LEMNA to help machine learning developers to validate model behavior, troubleshoot classification errors, and automatically patch the errors of the target models.
Guija, Daniel, Siddiqui, Muhammad Shuaib.  2018.  Identity and Access Control for Micro-services Based 5G NFV Platforms. Proceedings of the 13th International Conference on Availability, Reliability and Security. :46:1–46:10.
The intrinsic use of SDN/NFV technologies in 5G infrastructures promise to enable the flexibility and programmability of networks to ensure lower cost of network and service provisioning and operation, however it brings new challenges and requirements due to new architectural changes. In terms of security, authentication and authorization functions need to evolve towards the new and emerging 5G virtualization platforms in order to meet the requirements of service providers and infrastructure operators. Over the years, a lot of authentication techniques have been used. Now, a wide range of options arise allowing to extend existing authentication and authorization mechanisms. This paper focuses on proposing and showcasing a 5G platform oriented solution among different approaches to integrate authentication and authorization functionalities, an adapted secure and stateless mechanism, providing identity and permissions management to handle not only users, but also system micro-services, in a network functions virtualization management and orchestration (NFV MANO) system, oriented to deploy virtualized services. The presented solution uses the NFV-based SONATA Service Platform which offers capabilities for a continuous integration and delivery DevOps methodology that allow high levels of programmability and flexibility to manage the entire life cycle of Virtual Network Functions, and enables the perfect scenario to showcase different approaches for authentication and authorization mechanisms for users and micro-services in a 5G platform.
Leu, Patrick, Puddu, Ivan, Ranganathan, Aanjhan, Capkun, Srdjan.  2018.  I Send, Therefore I Leak: Information Leakage in Low-Power Wide Area Networks. Proceedings of the 11th ACM Conference on Security & Privacy in Wireless and Mobile Networks. :23–33.
Low-power wide area networks (LPWANs), such as LoRa, are fast emerging as the preferred networking technology for large-scale Internet of Things deployments (e.g., smart cities). Due to long communication range and ultra low power consumption, LPWAN-enabled sensors are today being deployed in a variety of application scenarios where sensitive information is wirelessly transmitted. In this work, we study the privacy guarantees of LPWANs, in particular LoRa. We show that, although the event-based duty cycling of radio communication, i.e., transmission of radio signals only when an event occurs, saves power, it inherently leaks information. This information leakage is independent of the implemented crypto primitives. We identify two types of information leakage and show that it is hard to completely prevent leakage without incurring significant additional communication and computation costs.
Murvay, Pal-Stefan, Groza, Bogdan.  2018.  A Brief Look at the Security of DeviceNet Communication in Industrial Control Systems. Proceedings of the Central European Cybersecurity Conference 2018. :5:1–5:6.
Security is a vital aspect of industrial control systems since they are used in critical infrastructures and manufacturing processes. As demonstrated by the increasing number of emerging exploits, securing such systems is still a challenge as the employed fieldbus technologies do not offer intrinsic support for basic security objectives. In this work we discuss some security aspects of DeviceNet, a communication protocol widely used for control applications especially in the North American industrial sector. Having the Controller Area Network (CAN) protocol at its base, DeviceNet inherits all the vulnerabilities that were already illustrated on CAN in-vehicle communication. We discuss how the lack of security in DeviceNet can be exploited and point on the fact that these vulnerabilities can be modelled by existing formal verification tools and countermeasures can be put in place.
Chen, Yunfang, Wu, Que, Zhang, Wei, Liu, Qiangchun.  2018.  SD-WAN Source Route Based on Protocol-oblivious Forwarding. Proceedings of the 8th International Conference on Communication and Network Security. :95–99.
Larger companies need more sites in the wide area network (WAN). However, internet service providers cannot obtain sufficient capacity to handle peak traffic, causing a terrible delay. The software-defined network (SDN) allows to own more programmability, adaptability, and application-aware, but scalability is a critical problem for merging both. This paper proposes a solution based on Protocol-Oblivious Forwarding (POF). It is a higher degree of decoupling control and data planes. The control plane uses fields unrelated to the protocol to unify packet match and route, and the data plane uses a set of general flow instructions in fast forwarding. As a result, we only save three flow tables on the forwarding paths so that each packet keeps a pipeline in the source route header to mark the next output ports. This solution can support a constant delay while the network expands.