Intelligent Transportation Systems: Navigating the Cybersecurity Landscape
Intelligent Transportation Systems: Navigating the Cybersecurity Landscape With the ongoing revolution in Intelligent Transportation Systems (ITS) and how we navigate and operate transportation networks, integrating sophisticated technologies offers both substantial obstacles and intriguing potential. These systems are vulnerable to a variety of cyberthreats due to the growing dependence on digital infrastructures. These vulnerabilities can cause traffic management disturbances or compromise confidential vehicle data. This article explores the critical role that cybersecurity plays in safeguarding the availability, confidentiality, and integrity of ITS, highlighting the necessity of strong security measures to guarantee the resilience and smooth functioning of smart transportation systems.
Recognizing Cyberattacks in ITS
VANET Attack by Man-in-the-Middle
The man-in-the-middle attack is a well-known cyberattack in which a hacker intercepts messages being sent between two parties and modifies the content before forwarding it. Vehicular Ad-Hoc Networks (VANETs) are an especially vulnerable to this kind of attack. Attackers can interfere with applications for road safety and effective traffic management by changing location information and interfering with vehicle-to-vehicle communication.
Attacks by Routing
In VANETs, routing protocols enable multi-hop communication; however, they are susceptible to attacks in which malevolent nodes obstruct data transmission to its intended destination. A hostile node drops all packets intended for retransmission in a black hole attack, but only drops certain packets in a gray hole assault. The dependability of data transfer in ITS can be significantly impacted by these attacks.
Attacks with Timing
Timing attacks cause communication lags, which interfere with real-time applications. For example, in a cooperative adaptive cruise control system, crashes can be avoided by promptly sending an emergency message. Even if the data is correctly received, an attacker-caused delay could lead to an accident because of the braking system's delayed reaction.
Spoofing
Spoofing attacks involve the dissemination of tampered data to elicit erroneous responses from the system. Sending fake GPS coordinates to interfere with a navigation system's functionality is one example. This may confuse automobiles and result in serious problems with functioning.
Denial-of-Service (DoS) Attacks
Attacks known as denial-of-service aim to prevent system components from operating. DoS attacks pose a special risk to ITS when they compromise aspects that are essential for safety. A common VANET denial-of-service assault, the Sybil attack involves a malicious vehicle posing as numerous people, injecting bogus broadcast messages, and interfering with regular information exchange.
Attack on Internal Vehicle Network
Many internal vehicle networks are open to assault since they were created before cars were connected. By using the Controller Area Network (CAN) protocol, for instance, an attacker can obtain access to the internal network and possibly take control of vital systems like airbags.
Identity Theft
In ITS, identity privacy refers to safeguarding individual user information, such as that of drivers, passengers, and pedestrians. Data regarding habits, location, and personal information may be extracted by attackers. For instance, learning how VANET nicknames are given enables attackers to monitor the whereabouts of vehicles.
Listening in
Through the passive assault of eavesdropping, an attacker can obtain information without interfering with communication. For instance, a hostile party could obtain a user's bank account information by listening in on vehicle-to-infrastructure communication during toll payments.
An assault on the fog
Because of their physical accessibility and constrained resources in comparison to the Cloud, fog components in ITS are susceptible to attack. Attackers have the ability to modify sensor aggregate data, which can change data analysis algorithms that are utilized in traffic control.
Artificial Intelligence Attacks
Data manipulation, environmental disruptions, or policy manipulation are examples of AI attacks in ITS. For example, an attacker could submit data that has been altered to trick machine learning algorithms and create erroneous trends in models.
Challenges with ITS Architecture and Security
ITS is comparable to the Internet of Things (IoT) in that it was created with comparable methodologies and architectural frameworks. ITS architectures usually comprise four levels, each of which is in charge of particular tasks:
Layer of Perception
The in-car sensors, infrastructure devices, and cellphones of users are all included in the perception layer. At this layer, configuration and initialization during production are frequently linked to security vulnerabilities. Since most internal vehicular networks were not intended for connected automobiles, attacks can be made against them.
Layer of Networks
In VANET, anonymous authentication is provided by combining wired and wireless technologies at the network layer. Nevertheless, there are more difficulties because of the nodes' constrained range and the time constraints. The IEEE 1609 standard (Wireless Access in Vehicular Environment, or WAVE) and the 3GPP standard (Cellular Vehicle to Everything, or C-V2X) are two important network technologies. C-V2X specifies security standards for both managed and unmanaged modes of operation, while WAVE specifies hierarchical certificate-based authentication procedures.
Layer of Support
Data is processed in the Fog or Cloud at the support layer based on temporal and spatial details as well as security considerations. Fog-based systems are more difficult to secure than centralized Cloud systems because of their distributed architecture, which creates unique security difficulties. Specialized security measures are needed because of the distinctive characteristics of fog computing, which include mobility, heterogeneity, and large-scale geo-distribution.
Level of Application
Information, alerts, and system activations from the last user interaction are reflected in the application layer. The sensor layer can analyze data locally, in cars, in roadside units (RSUs), in the fog, or in the cloud. AI applications can benefit from the use of ITS data, which is classified as Big Data. However, because AI is susceptible to cyberattacks, it must be carefully evaluated in security-critical systems such as ITS.
Conventional and Novel Security Methods in ITS
Conventional Approaches to ITS Security
The well-known security method of network segmentation can improve security and efficiency, but it must be modified for ITS. Given that nodes in ITS networks must remain anonymous and be free to move around, this calls for a dynamic and flexible approach to network segmentation.
Novel Strategies in ICT Security
Blockchain
The potential uses of blockchain technology in a variety of industries, including Internet of Things systems, have drawn interest. Blockchain can improve cybersecurity in ITS in a number of ways. An important use is anonymous authentication. Blockchain technology has the capacity to retain data regarding node veracity, which enables nodes to select new members based on reputation. Malicious nodes are prevented from entering the network uninvited by this decentralized storage.
Unidentified Fog Verification
Fog nodes are essential for maintaining privacy because they secure user information before it leaves the network's edge. Vehicular Ad-hoc Networks, or VANETs, are looking for practical means of anonymous authentication. By decreasing the quantity of authentication exchanges that occur between cars and Roadside Units (RSUs), fog technology gets rid of the requirement for a vehicle to continually authenticate each RSU while it is traveling.
ITS Bloom Filters
Bloom Filters provide a way to use temporary IDs while conserving resources. A Bloom Filter that updates automatically holds all certificates created within a given time period. By using the Bloom Filter, this method avoids the requirement for repeated confirmation, as it does not demand a trustworthy source reply for each package that is received. Nevertheless, more methods are required to offset possible false positives.
Security via Contract: Also known as Contractual Security
A thorough explanation of an application's features and how it interacts with its host platform is part of security by contract. Various security tasks in the sensor layer are addressed by this technique, especially for devices that are already in use. To ensure that IoT devices operate in accordance with security policies, security contracts might include guidelines that are compared to a stored security policy.
Senseful Safety in the Internet of Things
It is imperative to adopt a proactive and strategic protection posture in the current complex cybersecurity scenario. Collaboration between cybersecurity specialists and cutting-edge security solutions, such as machine learning (ML) algorithms, is necessary for this. Machine learning is commonly employed in cybersecurity systems, although it includes flaws including noise insertion and poisoning assaults. To get over these restrictions, machine learning techniques are used as a backup.
Other approaches, such as ontologies, provide a standard vocabulary to describe security issues of unstructured data in the Internet of Things. Furthermore, game theory has been used to cybersecurity with great success, allowing for the modeling and analysis of intricate security scenarios. A strong defense against cyber attacks is created by combining a variety of approaches and technology into a complete cybersecurity approach.
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