Vehicle Security Research Articles

Adaptive autonomous emergency braking model based on weather conditions

Objective Vehicle active safety systems can improve vehicle security by avoiding collisions. An autonomous emergency braking (AEB) system’s safety distance calculation is usually based on normal weather conditions. The AEB system’s early warning capabilities decrease during adverse weather conditions. Methods A multilayer perceptron (MLP) model is used to obtain data from accident and weather data sets. The MLP model is trained and the severity of accidents is predicted. The severity is used as a parameter to build an adaptive AEB system algorithm that considers adverse weather conditions. Results The adaptive AEB system algorithm increases safety and reliability under adverse weather conditions. Prescan and a driver-in-the-loop system are used to test the adaptive AEB model. Both tests show that the adaptive AEB model has better performance under adverse weather than the traditional AEB model. Conclusions The experimental results demonstrate that the adaptive AEB system can increase the safety distance in rainy weather and avoid collisions under hazy conditions.

Auto-CIoV: Autonomous Connected Internet of Vehicles Security Requirements, Open Challenges with Future Research Directions

Auto-CIoV: Autonomous Connected Internet of Vehicles Security Requirements, Open Challenges with Future Research Directions

Smart Remote Transit Vehicle Monitoring with Emission Alert and Secured Access Using IOT

Abstract: The research paper focuses on addressing automobile theft by proposing a framework utilizing the Internet of Things (IoT), GPS, and GSM technologies. This system allows vehicle owners to track and monitor their vehicles at any time from anywhere, improving security against professional thieves. The IoT sends information about the vehicle's status to the guardian or owner, while GPS provides the vehicle's current location. In case of theft, the fuel supply can be gradually cut off by sending a message, disabling the vehicle, and updating data to the Google Firebase server. An RFID card is provided to start the vehicle, and an Android app is developed using the MIT AppInventor tool for monitoring maps and notifications. Additionally, emission levels are measured and updated to the cloud, and a panic button is included for driver assistance in emergencies. This proposed system enhances existing anti-theft measures while offering innovative features for better vehicle security and management.

Rancang Bangun Prototipe Sistem Start Engine dan Alarm Sepeda Motor Menggunakan Mikrokontroler Arduino Berbasis Android

The rapid development of information technology has had a significant impact on various fields, both through the use of electronics and mobile devices, as well as the easily developed Android operating system. However, the current vehicle security systems are still inadequate in ensuring the safety of vehicles, especially concerning criminal activities such as theft and robbery. To address this issue, the idea of implementing a telephone facility through research has emerged, aiming to design and create a prototype of a start engine and motorcycle alarm system using the Arduino microcontroller based on Android. The purpose of this research is to reduce the frequency of crimes that occur through tampering with vehicle contacts, with the expectation that the implementation of an improved security system will lead to a decrease in vehicle theft cases and provide better protection for vehicle owners.

Cyber4Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones

Cyber Security and forensics for Unmanned Aerial Vehicles (UAVs) pose unique requirements, solutions, and challenges. As UAVs become increasingly prevalent for legitimate and illegal use, ensuring their security and data integrity is important. Solutions have been developed to tackle these security requirements. Drone forensics enables the investigation of security incidents involving UAVs, aiding in identifying attackers or determining the cause of accidents. However, challenges persist in the domain of UAV security and forensics. This paper surveys drone threat models, security, and privacy aspects. In particular, we present the taxonomy of drone forensics for investigating drone systems and talk about relevant artifacts, tools, and benchmark datasets. While solutions exist, challenges such as evolving technology and complex operational environments must be addressed through collaboration, updated protocols, and regulatory frameworks to ensure drones’ secure and reliable operation. Furthermore, we also point out the field’s difficulties and potential future directions.

Features of ensuring transport security of transport infrastructure facilities and railway transport vehicles in the transportation of highly dangerous goods

The system of ensuring transport security in railway transport is considered, aimed at reducing the risk of committing acts of unlawful interference, including terrorist acts, using weapons, explosives and other dangerous substances, by limiting their carrying by the offender to transport infrastructure facilities and vehicles.A list of high-risk goods transported by rail is given. The normative documents regulating transport security are analyzed. The minimum number of posts of the transport security unit for the transport infrastructure facility has been determined.Measures aimed at improving the efficiency of the transport security system are presented.

A new method for the detection and identification of the replay attack on cars using SDR technology and classification algorithms

ContextCurrently, a wide variety of cars use Remote Keyless Entry (RKE) to activate the security system by radio frequency. ProblemRKE systems have security vulnerabilities with attacks such as brute force, replay, spoofing, and replay. ObjectiveThis study proposes a replay-type attack detection system using SDR and intelligent algorithms. MethodThe present dataset was obtained from an RKES type Key-fob of a 2016 model vehicle taken with different antenna polarizations at different distances. There were used three test and configuration systems based on two neural network identification models. ResultsThe proposed designs with the different classification models were able to detect replay-type vulnerabilities. In the first system, retransmitted indicators were detected with a maximum identification percentage of 90.9% and original signs of 90.9%; for the second system, 100% of the retransmitted signals and 89% of the original ones were detected, and for the third system, detected 100% of the retransmitted signals and 89.09% of the first signs. ConclusionThe models used in the system's design showed an identification percentage of more than 87% in all cases, managing to detect and identify the replay-type attack on the vehicles. System 1 presented better results with the VGG 16 system than in system 2, within the VGG 16 and SVM models having identical identification percentages obtained and, in system 3, which has the characteristics of the previous two, attained better results with SVM. The reception of signals with SDR is an effective tool for detecting vulnerabilities in vehicle security systems.

INTRUSION ALARM SYSTEM BASED ON RADIO FREQUENCY TECHNOLOGY, GPS, GSM AND DIJIKSTRAT ALGORITHM FOR NOTIFICATION TRIANGULATION

In today's world, cars are important products that are essential to life. But when it comes to vehicle security, it's hard to start. "Vehicle theft is on the rise. Most stolen vehicles are exported abroad. The aim of this research is to develop a remote sensing notification module. In the event of a break-in, the driver is notified by SMS using GSM technology, uses GPS to track the location of the vehicle and trace the route, maps the location and uses the DIJIKSTRAT algorithm to find the nearest send a notification to the police station. The module also incorporates a prediction system for suspicious events associated with an infrared sensor. The overall idea of this research is to use large amounts of information and suspicious movements to develop a scientific model for intrusion detection. When a person approaches a parked vehicle, infrared sensors detect and compare suspicious movements in the knowledge base to detect possible intrusions. This paper proposes an anti-theft system to help car owners avoid theft and locate their car after it has been stolen. The materials referred to in this project are also actually designed.

Neural‐network‐based event‐triggered adaptive security path following control of autonomous ground vehicles subject to abnormal actuator signal

AbstractThe malicious physical attacks from both sensor and actuator sides make real threats to the security and safety of autonomous ground vehicles (AGVs). This paper focuses on the problem of neural‐network‐based event‐triggered adaptive security control (ET‐ASC) scheme for path following of AGVs subject to arbitrary abnormal actuator signal. First, we assume that an arbitrary abnormal signal is caused by arbitrary malicious attacks or disturbances from actuators. Then, radial basis function neural network (RBF‐NN) is used to reconstruct such abnormal actuator signal. Second, modelling issues on security path following control of AGVs with Sigmoid‐like ETC scheme are shown when the AGV is suffering from abnormal actuator signal. In what follows, an ET‐ASC scheme is developed to mitigate the adverse effects of abnormal actuator signal with the reconstructed abnormal signal based on a novel Sigmoid‐like event‐triggered communication scheme. By using the proposed RBF‐NN‐based ET‐ASC scheme, control performance can be guaranteed under arbitrary malicious actuator signal rather than such attacks following a specific probability distribution. Final, some simulation experiments are provided to verify the effectiveness of proposed ET‐ASC scheme.

Autonomous network services using machine learning-based cybersecurity

The crucial topic of protecting Controller Area Network (CAN) bus systems in autonomous vehicles against cyberattacks is covered in this research. The risk and consequences of cyberattacks on CAN systems rise considerably as vehicles become increasingly automated and linked, creating safety and security hazards. In order to identify anomalies and categorize traffic into attack or conventional categories, the study looks at the weaknesses of CAN buses and recommends using machine learning techniques like Decision Trees, Clustering, and Deep Learning. To improve the detection of anomalies and cyber-attacks in CAN systems, the suggested methods combine data balance, feature selection, and ensemble learning with a voting-based strategy. Metrics like accuracy, precision, recall, F1-score, and confusion matrix can be used to assess the presented approaches. According to the study's findings, these suggested solutions provide a more reliable and efficient way to identify cyber-attacks and anomalies in CAN systems, boosting the development of cyber security for autonomous vehicles. While outlining the necessity of information security and the advantages of autonomous vehicles, it also suggests cutting-edge ways to strengthen their security. Overall, this article highlights the urgent need for improved security measures in autonomous cars since cyberattacks pose a serious threat to the functioning of these vehicles in a safe and secure manner. The study proposes a potential approach to enhancing the security of CAN bus systems in autonomous vehicles by suggesting cutting-edge approaches for identifying anomalies and cyber-attacks.

Prevention and suppression effects of phase change material on thermal runaway in batteries

With the rapid improvement in the electric vehicle industry, lithium-ion batteries are being used in increasing numbers. However, lithium-ion batteries are prone to thermal runaway under various factors, leading to fires and explosions that seriously affect the security of electric vehicles. Therefore, a method for effectively preventing and suppressing thermal runaway in batteries is an important problem that needs to be solved. The phase change material can absorb a large quantity of heat during the solid‒liquid transition process, exhibiting strong potential for thermal runaway protection in lithium-ion batteries. In this research, a lithium-ion battery module is proposed with cylindrical cells and paraffin phase change material. The prevention and suppression effects of the phase change material on thermal runaway in the cells are analyzed by a numerical method. The phase change material can effectively reduce the maximum temperature of the trigger cell and stop the propagation of thermal runaway in the module. By varying the thermal conductivity, latent heat, melting temperature and thickness of the phase change material, their influences on the heat generation and temperature of the trigger cell during thermal runaway are analyzed. Design recommendations for phase change materials to protect lithium-ion batteries from thermal runaway are provided based on the results.

Intelligent Accident Detection and Vehicle Security for Two Wheelers

Quick medical assistance in case of accidents aid in saving the lives. The main aim of this developed system is to alert the nearby hospital about the accident and to provide immediate medical aid. The system has various sensors such as accelerometer, vibration and tilt, to identify the variations in the vehicle. The system makes decision based on the value of the sensors and sends the information through smartphones connected to the Raspberry pi module. The vehicle security system uses a fingerprint recognition for ignition of vehicle engine to ensure the protection of the vehicle from unauthorized access.

Briefly Noted

On December 21, 2022, the European Court of Human Rights indicated interim measures in the case Armenia v. Azerbaijan (no. 4). The request for interim measures brought by the Government of Armenia alleged that Azerbaijan was blocking Armenians' access to the Lachin Corridor which, according to a press release from the Court, is used by Armenian residents in Nagoro Karbakh to access vita services, including medical care. In its decision to indicate provisional measures, the Court noted that Azerbaijan was obligated under Article 6 of the Trilateral Statement signed November 9, 2020, to “guarantee the security of persons, vehicles and cargo moving along the Lachin Corridor in both directions.” The Court also reiterated Azerbaijan's obligations under the European Convention of Human Rights, in particular Articles 2 (right to life), 3 (prohibition of torture, inhuman or degrading treatment), 6 (right to a fair trial), and 8 (right to respect for family life). The Court ordered Azerbaijan to take measures to ensure that seriously ill persons in need of medical treatment were granted safe passage through the Corridor.

Internet of Things based Electric Vehicle Security and Tracking

The aim of this research is to introduce a security system that utilizes IoT and GPS tracking technologies to provide enhanced safety measures. Cayenne and GSM technologies are employed in the proposed system to provide real-time tracking and security features. The system comprises of GPS unit, microcontroller, GSM unit, and Cayenne dashboard. The GPS module is used to obtain the location of the device, while the microcontroller processes the data and sends it to the Cayenne dashboard through the internet. The GSM module is used for real-time communication with the device and to send an alert upon occurrence of any security breaches. The Cayenne dashboard provides an easy-to-use interface for monitoring and controlling the device remotely. The proposed system provides an effective and reliable solution for tracking and securing women and children, especially in potentially dangerous situations. The system can be easily implemented and customized to suit various tracking and security requirements.

A Novel Approach to Provide Security for Autonomous Vehicles

Abstract: Autonomous Vehicles (AVs) have been developing as the time passes in the events and it reduces the workload of mankind and reducing the errors done in the traffic(accidents). AVs are not bounded to roadways but also take part in other possible transportation modes. The issues ofthe extant process have less privacy due to involvement of sensor components and inappropriate of data transfer which will be subjected to more cyber-attacks. To prevent these issues, a methodology is proposed and the cryptography algorithms is implemented for transferring the data for drones, UAV’s. Here cloud acts as negotiator between user and drones and UAV’s, user shares the files from sender to drones(UAV’s) with enhanced security. The AES algorithm is incorporated to inscribe the data from plain text into unidentified encoded form of text. The concealed text can be deciphered by entering the hash key.

VASERP: An Adaptive, Lightweight, Secure, and Efficient RFID-Based Authentication Scheme for IoV.

With the rapid growth in wireless communication and IoT technologies, Radio Frequency Identification (RFID) is applied to the Internet of Vehicles (IoV) to ensure the security of private data and the accuracy of identification and tracking. However, in traffic congestion scenarios, frequent mutual authentication increases the overall computing and communication overhead of the network. For this reason, in this work, we propose a lightweight RFID security fast authentication protocol for traffic congestion scenarios, designing an ownership transfer protocol to transfer access rights to vehicle tags in non-congestion scenarios. The edge server is used for authentication, and the elliptic curve cryptography (ECC) algorithm and the hash function are combined to ensure the security of vehicles' private data. The Scyther tool is used for the formal analysis of the proposed scheme, and this analysis shows that the proposed scheme can resist typical attacks in mobile communication of the IoV. Experimental results show that, compared to other RFID authentication protocols, the calculation and communication overheads of the tags proposed in this work are reduced by 66.35% in congested scenarios and 66.67% in non-congested scenarios, while the lowest are reduced by 32.71% and 50%, respectively. The results of this study demonstrate a significant reduction in the computational and communication overhead of tags while ensuring security.

Health-conscious energy management strategy for battery/fuel cell electric vehicles considering power sources dynamics

This paper presents a new multi-stage power management strategy aimed at enhancing the security and performance of hybrid electric vehicles (HEVs). The proposed strategy is designed to protect sensitive vehicle power sources, such as fuel cells (FCs), from potential damages caused by abrupt load variations. This is achieved through the implementation of a model-based coordinated switching strategy that utilizes transition functions that are tailored to the transient dynamics of power sources. The power flow in HEVs is managed using a fuzzy energy management approach that allows safe and predefined operation of FCs at multiple operating points. To ensure reliable and uninterrupted HEV operation, fault detection algorithms have been integrated into the second stage of the power management strategy to detect and correct any power source failures. Simulation results indicate that the proposed fuzzy energy management strategy provides efficient usage and precise control over FC power, while the coordinated switching strategy compensates for the sluggishness of FCs and reduces transient power ripples and fluctuations in the DC bus voltage during FC operating point variations. The efficiency of the proposed multi-stage power management strategy has been validated through simulations performed on the RT LAB platform and the obtained results were deemed satisfactory.

Employing automotive security to improve the security of unmanned aerial vehicles

The concern about cyber threats on unmanned aerial vehicles (UAVs) increases as they become more sophisticated and widely used in various areas. Although there have been efforts to improve the security of UAVs, their focus is limited to physical attacks and securing communication, and many areas remain open to issues. Although a more comprehensive approach is required to improve the overall areas of UAVs, it is reasonable to investigate the case study from the perspective of other similar cyber-physical systems (CPSs). Thus, we see the architectural similarity between UAVs and automobiles. Automotive security has seen significant improvement throughout the last decade as vehicles have begun to have connectivity and autonomy. There have been extensive research and development efforts in various aspects, including securing the components, securing communications, securing software updates, and securing the overall management system to provide multi-layered security for the automotive environments, and standardization and regulations are being issued. Due to the similarity, the employment of ideas from the automotive environments in the UAV environments becomes a reasonable approach. In this paper, we show the automotive security trends and discuss how UAV environments are comparable to automotive environments. We then show how security in automotives is adapted in UAVs with a case study.

Non-negative Sparse Discriminative Low Rank Preserving Projection for Robust Face Feature Extraction

Face recognition is an important aspect of intelligent security for new energy vehicles. Existing methods extract features without taking low-rank and discriminative similarity relations of data into account and resulting in low quality feature distribution. In addition, the coefficients of the learned representation matrix can be negative and lack interpretability. To address the above issues, a method named non-negative sparse discriminative low rank preserving (NNSDLRPP) which introduces sparsity, non-negativity and block-diagonal regularization is proposed. As a result, NNSDLRPP improves the interpretability of representation features while capturing discriminative information of data. Extensive experiments on two face datasets show that the proposed method outperforms other state-of-the-art methods.

The Honda Automotive Laboratories of Ohio Wind Tunnel

<div class="section abstract"><div class="htmlview paragraph">The Honda Automotive Laboratories of Ohio (HALO) includes a new aeroacoustic wind tunnel located near Marysville, Ohio that started operations in 2022. This facility provides world-class aerodynamic flow quality and acoustic testing capabilities for the development of both passenger and motorsports vehicles.</div><div class="htmlview paragraph">This closed-return ¾ open jet wind tunnel features a two-position flexible nozzle system with cross sections of 25 m<sup>2</sup> and 18 m<sup>2</sup>, providing wind speeds of up to 250 km/h and 310 km/h, respectively. There is a ±180 degree turntable with boundary layer control systems, and interchangeable single belt and 5-belt moving ground plane (MGP) modules. Extensive applications of acoustic treatment in the test section and throughout the wind tunnel circuit provide a hemi-anechoic test environment and low background noise levels. A temperature control system provides uniform and stable air temperature over an operating environment between 10 °C and 50 °C.</div><div class="htmlview paragraph">The primary instrumentation of the wind tunnel includes the wind speed, temperature, and humidity measurement systems, a 6-component force balance integrated within the turntable, and an acoustic test system (ATS). The ATS includes four planar microphone arrays for external measurements and a spherical microphone array and binaural heads for internal measurements. A flow survey traverse system equipped with a 4-D probe holder mechanism is capable of placing a probe anywhere within the test volume.</div><div class="htmlview paragraph">The HALO facility includes the wind tunnel control room, secure vehicle preparation bays and office spaces for segregated customers, and a vehicle frontal and side area measurement system.</div><div class="htmlview paragraph">This paper provides an introduction to the HALO wind tunnel testing capabilities, design features and development, and the results of the aeroacoustic commissioning program. The background noise level and flow quality characteristics are provided.</div></div>