Role of Cyber Security on Intelligent Transportation Systems

By: Anmol1, Yulia Kharisma2, Siti Meilianawati2

1Department of CSE, Chandigarh College of Engineering and Technology, Sector 26, Chandigarh

2Department of computer science, Esa Unggul University, Indonesia


As Intelligent Transportation Systems (ITS) continue to transform the way we navigate and manage transportation networks, the integration of advanced technologies presents both exciting opportunities and significant challenges. This paper delves into the crucial role of cyber security in protecting the integrity, confidentiality, and availability of the ITS. The increased reliance on digital infrastructures in transportation exposes these systems to a wide range of cyber threats, from potential disruptions in traffic management to the compromise of sensitive vehicular data. This abstract explores the evolving landscape of cyber risks within the context of ITS, emphasizing the need for robust security measures to ensure the seamless operation and resilience of smart transportation systems. The discussion covers the identification of unique vulnerabilities in ITS, the application of encryption and authentication protocols, and the development of proactive strategies to prevent cyber attacks. By shedding light on the critical connection between cyber security and the future of intelligent transportation, this paper aims in contributing to the ongoing conversation on enhancing the safety and reliability of interconnected transportation ecosystems..

KEYWORDS: Cyber security, Intelligent Transportation Systems (ITS), Digital infrastructure, Transportation networks, Security threats, Data protection.


Envision bustling city streets teeming with seamlessly connected vehicles, navigating a complex network of smart infrastructure with ease. This is no futuristic fantasy, but rather the ambitious vision of Intelligent Transportation Systems (ITS). At its core, ITS relies on strict time constraints, dynamic data flows, and vast information volumes, making cybersecurity an absolute necessity.

While there is no standardized blueprint for ITS architecture, most developments share common layers (Figure 1). Each layer, from interconnected sensors and communication protocols to powerful data analytics engines, presents potential vulnerabilities that must be addressed with robust security measures across the entire system. It is crucial to recognize that ITS is not a standalone entity, but rather an integral part of the broader ecosystem of smart cities and the Internet of Things (IOT) [2].

Vehicle ad hoc networks, or VANETs, are an essential part of the advancements in modern ITS.. These dynamic networks enable vehicles to exchange vital information, such as direction, speed, and road conditions, through short, periodic messages called beacons [3,4,5,6]. This continuous communication enables the intelligent orchestration of traffic flow, accident prevention, and personalized travel experiences. However, the interconnectedness of ITS also presents a vast attack surface for malicious actors, threatening the safety and efficiency of this critical infrastructure. To address this challenge, it is essential to develop a comprehensive digital shield that protects every layer of ITS, from individual sensors to data exchanges and centralized processing. By identifying and addressing the unique vulnerabilities of various ITS components, implementing cutting-edge security protocols, and fostering a culture of proactive vigilance, we can safeguard this vital infrastructure and pave the way for a future where smart transportation can thrive. This research delves deeper into the intricate relationship between innovation and security in ITS, examining the various threats, analyzing vulnerabilities, and proposing innovative solutions to build resilient ITS ecosystems where security is the unwavering guardian of progress.


Since ITS is a subset of IoT, it can be developed with comparable methodologies and architectural frameworks [7]. The majority of IoT developments are outlined in Figure 1. It might be used in ITS as well [2].










Figure 1: ITS Architecture

There are four layers in the IoT (Internet of Things) framework1, and each one is in charge of specific tasks discussed below:

  1. Perception layer: The integration of users’ smart phones, in-car sensors, and infrastructure devices is part of the perception layer. These devices present security challenges during manufacturing because of configuration and initialization issue
  2. Network layer: In a Vehicle-to-Everything (VANET) network, the network layer combines wired and wireless technologies, offering anonymous authentication. Nevertheless, there are further challenges because of the nodes’ constrained range and the time constraints. The 3GPP standard (suitable for 4G and 5G LTE-Long Term Evaluation networks called Cellular Vehicle to Everything – C-V2X) and the IEEE 1609 (Wireless Access in Vehicular Environment – WAVE) family of standards, which are based on 802.11are two network technologies that have been outlined in VANET architecture standards to address these challenges [8].
  3. Support layer: ITS data processing at the support layer happens either in the fog or in the cloud, based on specific requirements for timing and space as well as security.. But because Fog-based systems are distributed, it is harder to secure the operating environments than with traditional centralized Cloud systems, creating new security challenges. Due to its distinct characteristics, including mobility, heterogeneity, and wide-scale geographic dispersion, fog computing cannot be directly subjected to the security and privacy measures currently in place for cloud computing
  4. Application layer: The Intelligent Transportation Systems (ITS) application layer handles the last-minute user interactions, such as informational messages, alerts, or vehicle system activations. Depending on its importance, security needs, and time constraints, data gathered in the sensor layer may be processed in different places before reaching the user. These computations can be performed locally in the car, at roadside units (RSU), in the cloud, or in the fog. Significantly, the data in ITS possesses Big Data properties, which qualify it for Artificial Intelligence (AI) applications. However, given its high susceptibility to various cyberattacks, the use of AI in security systems such as ITS must be carefully considered.

Table:1. A Comparison between security issues and the architectural layers.



Perception layer

Initialization and configuration of the devices during production.

Network layer

VANET anonymous authentication

Support layer

Fog protection (Fog defense)

Application layer

Complex data model, AI defense


Intelligent Transportation Systems (ITS) are comparatively new, but they reuse existing knowledge and expertise because many of the technologies that are integrated have been tested and proven in real-world scenarios [9]. While new technologies like strong authentication, encrypted communication, key management, frequent auditing, private networks, and secure routing are becoming more and more significant, traditional security measures will still be vital..

  1. Cryptographic techniques, which date back to the 1990s, are the cornerstone of cybersecurity in Intelligent Transportation Systems. However, because of their shortcomings in terms of reliability, low latency, and high throughput performance, traditional encryption standards might not be appropriate for ITS. Because of this, lightweight encryption is now essential to ITS in order to guarantee the security of data sent over the network [10,13].
  2. Another well-known security technique that can boost efficiency and security is network segmentation, but it needs to be modified to take into consideration the need for anonymity and mobility for certain nodes in ITS networks. This calls for a dynamic and adaptable approach to network segmentation that can take into account how ITS networks are evolving.


The potential applications of blockchain technology in a variety of industries, including Internet of Things (IoT) systems, have drawn a lot of attention recently [17]. In particular, by offering several security techniques, blockchain can be used to improve the cybersecurity of intelligent transportation systems (ITS). Anonymous authentication solutions are one of the main ways that blockchain is being used in ITS.

Blockchain can be used to store information about the veracity of nodes in a network, giving nodes the ability to decide whether to accept new members based on their reputation by means of decentralized storage. By doing this, malicious nodes may be discouraged from trying to enter the network without authorization.

In conclusion, blockchain technology’s potential is rooted in its capacity to offer creative and multifaceted solutions for cybersecurity in ITS [12]. By taking advantage of blockchain’s decentralized and transparent structure, ITS can gain better data integrity, authentication, and security in general.


Fog nodes are crucial for maintaining people’s privacy because they secure private information before it leaves the network’s edge. Vehicular Ad-hoc Networks (VANETs) are looking for a viable anonymous authentication solution. One of the advantages of fog technology is that it reduces the number of authentication exchanges between legitimate vehicles and Road Side Units (RSUs) by doing away with the requirement to continuously authenticate all RSUs during a vehicle’s journey [2,3,7,11]. Three layers make up the system architecture of this study: the vehicles, the fog, and the cloud layers [16].


Bloom Filters provide a solution to the problem of resource conservation when using temporary identifiers. Using this technique, all certificates created during a given time frame are kept in a Bloom Filter that automatically updates. Rather than needing a reply from a reliable source for every package that is received, the Bloom Filter is cited, which eliminates the requirement for repeated confirmation. This method could, however, result in falsely positive results. To counteract this, extra techniques like getting confirmation from a reliable source or keeping a list of individuals who are known to be illegitimate participants can be used to increase the Bloom Filter’s accuracy [18].


A thorough explanation of an application’s functionalities and interactions with its host platform forms the foundation of the security by contract paradigm. Numerous security tasks in the sensor layer, including those pertaining to devices that are currently in use, may be addressed by this technique. One suggested security solution is to use security contracts to define rules in IoT devices that can be checked against a stored security policy. This system aims to offer a dependable way to verify that, whether through user input, administrator actions, or manufacturer settings, devices are functioning in accordance with security policies. [19].

Table 2: ITS Cyber security framework and methodologies.



Perception layer

contractual security

Network layer

Block chain, models based on reputation, fog computing, bloom filters with additional techniques, and game theory

Support layer

Secure routing, private networks, frequent audits, encryption, authentication and key management

Application layer

AI, blockchain, ontology, machine learning, and game theory


In the intricate cybersecurity environment of today, a proactive and strategic defense strategy is essential. Collaboration between cybersecurity experts and other cutting-edge security solutions, like machine learning (ML) algorithms, is necessary for this [10]. Despite being widely used in cybersecurity systems, machine learning has a training phase vulnerability that leaves it open to attacks like poisoning and noise insertion. As a backup plan, machine learning techniques are frequently employed to get around this restriction.

Other methods, like ontology, which can offer a common vocabulary for characterizing security aspects related to unstructured data in the IoT domain, are being investigated to improve cybersecurity in addition to machine learning.

Another mathematical tool that has been effectively used in cybersecurity and privacy is game theory. It enables the modeling and analysis of intricate security scenarios and offers insightful information about how attackers and defenders behave. In order to create a strong and effective defense against cyber threats, a comprehensive approach to cybersecurity is ultimately required, combining the strengths of multiple techniques and technologies. [20].


Intelligent Transportation Systems (ITS) are essential elements that influence both the effectiveness of transportation services and the safety of road users [9,11]. It is imperative to guarantee the security of these systems, necessitating the development of an extensive standard and security plan. The lack of appropriate techniques for configuring and initializing devices during manufacturing, authenticating nodes in Vehicle-to-Infrastructure (V2I) networks, protecting fog-based structures, and standardizing intricate data and metadata models are just a few of the unresolved issues in ITS security [13, 14].

Traditional security techniques, like network segmentation and cryptography, must be modified to meet the particular requirements of ITS. To solve these security bottlenecks, novel techniques like artificial intelligence AI, game theory, ontologies, and machine learning are being investigated. Blockchain is one of the cutting-edge technologies being investigated for ITS security. In addition to reducing the amount of network and computing resources needed for continuous pseudonym exchange in V2I networks, blockchain has the ability to provide anonymous authentication and function as a secure data warehouse in higher architectural layers. To tackle the constantly changing threats and vulnerabilities, Intelligent Transportation Systems (ITS) security necessitates a comprehensive and flexible security approach. Modern technologies like machine learning, artificial intelligence (AI), and fog computing can be very helpful in addressing these issues and guaranteeing the security of ITS systems [17].By storing sensitive node identity information at the system’s edge and limiting its exposure to potential attacks, fog computing, for example, can help reduce the risk of security threats in anonymous authentication [15]. To add an extra degree of security, this strategy can be enhanced by combining different technologies, such as a primary method like a bloom filter and an auxiliary method like a blacklist.

The security by contract idea, which emphasizes security at the perception layer and can be especially useful in handling modifications and advancements in ITS systems, is another innovative technology. ITS systems can be better safeguarded against new threats and vulnerabilities by utilizing these cutting-edge technologies, assuring the general security and dependability of these vital systems.


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Cite As:

Anmol, Kharisma Y., Meilianawati S. (2024) Role of Cyber Security on Intelligent Transportation Systems, Insights2Techinfo, pp.1

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