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Controller Area Network (CAN) is a de facto standard of in-vehicle networks. Since CAN employs broadcast communication and a slower network than other general networks (e.g. Ethernet, IEEE802.11), it is inherently vulnerable to Denial-of-Service (DoS) attacks. As a countermeasure against DoS attacks on CAN, a method for detecting a DoS attack using the entropy in a sliding window has been proposed. This method has a good advantage in terms of effectiveness and the small computational overhead. However, this method may only be effective against DoS attacks under naive conditions such as some higher priority messages. In addition, if an adversary can adjust the entropy of the DoS attack to its normal value, the conventional method cannot detect a DoS attack in which the adversary manipulates the entropy. We found this type of DoS attack, which is called an entropy-manipulated attack. In this paper, we propose a method that can detect an entropy-manipulated attack by using the similarity of two sliding windows. We confirmed that the proposed method detected the DoS attack in 100% of the cases in our experiment, and we showed that the detection time is up to 93% (14 μs) shorter than the conventional method.

Recently, cyberattacks on Controller Area Network (CAN) whichis one of the automotive networks are becoming a severe problem.CAN is a protocol for communicating among Electronic ControlUnits (ECUs) and it is a de-facto standard of automotive networks.Some security researchers point out several vulnerabilities in CANsuch as unable to distinguish spoofing messages due to no authenti cation and no sender identification. To prevent a malicious messageinjection, at least we should identify the malicious senders by ana lyzing live messages. In previous work, a delay-time based methodcalled Divider to identify the sender node has been proposed. How ever, Divider could not identify ECUs which have similar variationsbecause Divider’s measurement clock has coarse time-resolution.In addition, Divider cannot adapt a drift of delay-time caused by thetemperature drift at the ambient buses. In this paper, we proposea super fine delay-time based sender identification method withTime-to-Digital Converter (TDC). The proposed method achievesan accuracy rate of 99.67% in the CAN bus prototype and 97.04% ina real-vehicle. Besides, in an environment of drifting temperature,the proposed method can achieve a mean accuracy of over 99%., The 16th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS 2021) will be held as a virtual event due to COVID-19 from June 7th to June 11th, 2021.

Online applications adoption, and success are driven by a multitude of factors among them the service response time. This is natural as users tend to prefer a faster service than a slower. However, it is challenging to deliver consistently fast response times due to performance variability inherent to the infrastructure running the application; This performance variability causes a fraction of user requests to experience unusual latency called tail latency. In this work, a Linear Regression Based Replica Selection Algorithm is proposed. The regression model helps to estimate how long a specific query is going to take to be serviced, and based on this information, a server with more or less resources is chosen to service the query. Experiments done using data generated by a fleet of buses show that the proposed approach is successful in reducing the higher percentiles latency up to 30 % in some cases while not impacting negatively the throughput., 2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)

The number of computer controlled vehicles throughout the world is rising at a staggering speed. Even though this enhances the driving experience, it opens a new security hole in the automotive industry. To alleviate this issue, we are proposing an intrusion detection system (IDS) to the controller area network (CAN), which is the de facto communication standard of present-day vehicles. We implemented an IDS based on the analysis of ID sequences. The IDS uses a trained Long-Short Term Memory (LSTM) to predict an arbitration ID that will appear in the future by looking back to the last 20 packet arbitration IDs. The output from the LSTM network is a softmax probability of all the 42 arbitration IDs in our test car. The softmax probability is used in two approaches for IDS. In the first approach, a single arbitration ID is predicted by taking the class which has the highest softmax probability. This method only gave us an accuracy of 0.6. Applying this result in a real vehicle would give us a lot of false negatives, hence we devised a second approach that uses log loss as an anomaly signal. The evaluated log loss is compared with a predefined threshold to see if the result is in the anomaly boundary. Furthermore, We have tested our approach using insertion, drop and illegal ID attacks which greatly outperform the conventional method with practical F1 scores of 0.9, 0.84, and 1.0 respectively., 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops),23-27 March 2020,Austin, TX, USA, USA

In recent years, some bus companies have raised revenue by reviewing the route plan using the number of passengers. The company has a system that can automatically counts the number of passengers on an ongoing basis. But they are costly because they use cameras and sensors those are dedicated for counting. It is too expensive for bus companies that really need to reconsider their route planning to introduce the system. In order to solve this problem and realize efficient operation, we propose a method to count passengers by using a drive recorder and sensors those are already equipped with buses. Drive recorders and various sensors will be obliged by the government to be set up by bus operators in the future. We constructed a model using Random Forest Regression with the position of the bus from the GPS module in the buses, the position of the bus stop used for operation management, and the number of passengers estimated from the image processing method combining YOLOv3 and Deep SORT. As a result, the average correct answer rate when the passengers get on and off are 96.2% and 70.1% respectively. Our method which utilized non-dedicated camera achieved higher correct answer rate than the conventional method which utilizes dedicated camera for counting passenger., 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops),Kyoto, Japan, Japan

In recent years, some bus companies have raised revenue by reviewing the route plan using the number of passengers. The company has a system that can automatically counts the number of passengers on an ongoing basis, but it is too expensive for bus companies that really need to reconsider their route planning to introduce the system. In order to solve this problem and realize efficient operation, we propose a method to count passengers by using a drive recorder and sensors those are already equipped with buses. Drive recorders and various sensors will be obliged by the government to be set up by transport operators in the future. We constructed a model using Random Forest Regression with the position of the bus from the GPS module in the buses, the position of the bus stop used for operation management, and the number of passengers estimated from image processing using background subtraction method. As a result, the average correct answer rate is 84%.

International audience; The Kalray MPPA-256 processor is based on a recent low-energy manycore architecture. In this article, we investigate its performance in multiprecision arithmetic for number-theoretic applications. We have developed a library for modular arithmetic that takes full advantage of the particularities of this architecture. This is in turn used in an implementation of the ECM, an algorithm for integer factorization using elliptic curves. For parameters corresponding to a cryptanalytic context, our implementation compares well to state-of-the-art implementations on GPU, while using much less energy.

One of the most challenging fields in vehicular communications has been the experimental assessment of protocols and novel technologies. Researchers usually tend to simulate vehicular scenarios and/or partially validate new contributions in the area by using constrained testbeds and carrying out minor tests. In this line, the present work reviews the issues that pioneers in the area of vehicular communications and, in general, in telematics, have to deal with if they want to perform a good evaluation campaign by real testing. The key needs for a good experimental evaluation is the use of proper software tools for gathering testing data, post-processing and generating relevant figures of merit and, finally, properly showing the most important results. For this reason, a key contribution of this paper is the presentation of an evaluation environment called AnaVANET, which covers the previous needs. By using this tool and presenting a reference case of study, a generic testing methodology is described and applied. This way, the usage of the IPv6 protocol over a vehicle-to-vehicle routing protocol, and supporting IETF-based network mobility, is tested at the same time the main features of the AnaVANET system are presented. This work contributes in laying the foundations for a proper experimental evaluation of vehicular networks and will be useful for many researchers in the area., in EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 2014

International audience; Service discovery is an essential component for distributed mobile applications in vehicular communication systems. While there have been numerous service discovery protocols, applications for vehicular communication systems pose additional requirements: discover services according to geographical position inside dynamic mobile environments. In this article, we propose a geographical location aware service discovery mechanism for vehicular ad-hoc networks (VANETs). The proposed mechanism exploits an IPv6 multicast service discovery protocol on IPv6 GeoNet-working specified by the GeoNet project. Thanks to the GeoBroadcast mechanism, it efficiently propagates service discovery messages to a subset of nodes inside a relevant geographical area with encapsulating IPv6 multicast packets. We implemented the proposed mechanism to the ns-3 network simulator, furthermore we integrated the prototype system using CarGeo6, an open source implementation of IPv6 GeoNetworking, with openSLP. Our simulation and real field evaluation results show the system can discover services with low latency and low bandwidth usage in VANETs even via multi-hop.

Service discovery is an essential component for applications in vehicular communication systems. While there have been numerous service discovery protocols dedicated to a local network, mobile ad-hoc networks and the Internet, in vehicular communication systems, applications pose additional requirements; They need to discover services according to geographical position. In this paper, we propose a location-aware service discovery mechanism for Vehicular Ad-hoc NETwork (VANET). The proposed mechanism exploits IPv6 multicast on top of IPv6 GeoNetworking specified by the GeoNet project. Thanks to the GeoBroadcast mechanism, it efficiently propagates service discovery messages to a subset of nodes inside a relevant geographical area with encapsulating IPv6 multicast packets. We implemented the mechanism using CarGeo6, an open source implementation of IPv6 GeoNetworking. Our real field evaluation shows the system can discover services with low latency and low bandwidth usage in VANETs., ITST 2011: 11th International Conference on ITS Telecommunications, Aug 23-25, 2011, St. Petersburg, Russia