Applying Technology in Infrastructure
Introduction
The technological advancements witnessed in the recent past has enabled the emergence of numerous applications, both software and hardware, which aim to ease human life. Among the emerging applications is autonomous vehicles (AVs) whose potential to revolutionize the transport industry is insurmountable. The introduction of Google’s first fleet of AVs in 2010 marked a turning point in the conception and development of AV technology (Taeihagh, & Lim, 2018). This assertion is evidenced by the large of number of AV technologies which have been developed by companies from across the globe since 2010. Based on assessment of the current high pace of AV development, it is predicted that AVs will be rolled out to the public market by 2020. Additionally, it is expected that AVs will make-up a quarter of the global market by the year 2040 (Taeihagh, & Lim, 2018).
An analysis of scholarly works and literature available on AV technology highlights that a large number of authors focus on both the economic and societal effects of AVs. The economic and societal benefits associated with the emerging application of autonomous vehicles stems from its ability to develop new paths for mobility, enhance safety, reduce fuel consumption, reduce costs, and so on (Taeihagh & Lim, 2018). On the other hand, there are numerous concerns that the negative effects from their unintended consequences outweighs its benefits. The main issue of concern in AVs is the risk posed by hackers, cyber-criminals, and cyber-terrorists (Taeihagh & Lim, 2018). The fact that the technology used in AVs rely on wireless connection opens up this innovation to numerous cyber-security threats and risks.
Characteristics of Autonomous Vehicles and Critical Infrastructure
Rathod, D. Sheetal. (2013). “An autonomous driverless car: An idea to overcome the urban challenges.” Journal of Information Engineering and Application, Vol. 3, No. 13
According to Rathod (2013), autonomous vehicles can be defined as a passenger vehicle which has the ability to drive itself. There are numerous names used to denote autonomous vehicles (AVs) including driverless car, autopilot vehicle, automated guided vehicle, auto drive car, and so on. One example of an autonomous vehicle is the Google Driverless Car, which is regarded as one of the most advanced AV in the industry. On the other hand, critical infrastructure refers to systems or assets that are essential in maintaining vital functions within societies such as health, social or economic well-being, security, safety, and so on. The cyber-security issues arising from critical infrastructure in the transportation sector as it regards to autonomous vehicles (AVs) is complicated by the overlap between technological and cyber-physical aspects. This assertion is evident in AVs where both cyber-physical security and cyber-security issues are evident.
The main characteristic of AVs includes the ability to navigate without a driver. The navigation system used in AVs combines information inputted or gathered about the streets, roads, and other transit routes with artificial intelligence (AI) software. Subsequently, the AI software locates the vehicle on the map by combining information acquired by sensors on top and in front of the car with live images from cameras installed on the front, the back, and the side of the car. For example, Google’s driverless car navigates using the information gathered by Google Street View, live images from cameras, LIDAR sensor on top, and radar sensor on the front of the car (Rathod, 2013). The control mechanism of AVs is divided into three categories, firstly, the sensors which consist of radars, cameras, laser sensors, GPS, ultrasonic sensors, and so on. Secondly, the logic processing units such as user interface, AI software, decision making software, and check functionality. Thirdly, the mechanical control system which is composed of servo motors and relays and brake, driving wheel, and throttle control (Rathod, 2013).
Autonomous Vehicles improve Cyber-Security and reduce Risks
Wei, Z., Yang, Y., Rehana, Y., Wu, Y., Weng, J., and Deng, R. (2017). “IoVShield: An efficient vehicular intrusion detection system for self-driving.” Information Security Practice and Experience, pp. 638-647
The cyber-security issues facing AV industry has prompted investment into strategies to deter or counter cyber-criminal activities (Wei, Yang, Rehana, Wu, Weng, and Deng, 2017). The investment has reinvigorated cyber-security efforts by introducing new talents and players into a sector, which had been previously delegated to the government. The involvement of the private sector and the immense insight on technology and security that they possess has led to the creation of numerous codes, malware, and software, which aim to improve cyber-security not only for AVs but the society as a whole. According to the Wei et al. (2018), the most effective software created to protect AVs from hackers is Vehicular Intrusion Detection System (VIDS). The VIDS was created using intrusion detection systems (IDS) which has been in existence since the 1980s. The effectiveness of VIDS over IDS is based on its utilization of a comprehensive cross-domain detection model along with ECU domain-based detection model (Wei et al., 2018). The cross-domain-based detection has deep learning techniques which exploit stream bit value while its ECU model uses Controller Area Network (CAN) frames. Experimental tests between VIDS and IDS highlights that the former has a higher intrusion detection rate than the latter. Additionally, the flexibility of VIDS implies that the software can be applied to vehicular and non-vehicular systems. Based on the authors’ assertion, the need for tighter cyber-security for AVs catalyzed the creation software and codes. The codes and software have lowered cyber-attacks and improved cyber-security in society as a whole.
AVs Decrease the Vulnerabilities in Transport System
Smith, Anna. (2018). “The impact of self-driving cars on transportation.” The Eye for Transport Organization.
The main vulnerabilities in the current transport system which relies on human drivers includes a high number of accidents (Smith 2018). The fact that a human is prone to error highlights that the delegation of certain functions which can determine individual’s life or death to machines decreases the vulnerability created by human drivers. Other vulnerabilities which have been addressed by AVs include mobility, costs, fuel consumption, safety, and customer satisfaction. An increase in safety is attributed to lower rates of traffic and vehicular accidents. The technology increases mobility in society by providing the minor, elderly, and the handicapped the independence to move freely.
According to Smith (2018), the main vulnerability in freight and trucking industry is the high cost of servicing wages for drivers and co-drivers. The introduction of driverless technology will enable trucks to deliver the goods without the need for the drivers which eradicates the wage bill. Additionally, trucking and freight companies incur immense losses due to accidents caused human error on the part of the drivers. The high precision and accuracy of AV technology will drastically reduce the number of accidents and the losses. Another improvement to trucking and freight sector is the delays caused by the need for human drivers due to rest.
Exploitation of AVs by criminals and terrorists
Taeihagh, A. & Lim, H. S. (2018). “Governing autonomous vehicles: Emerging responses for safety, liability, privacy, cyber-security, and industry risks.” Transport Reviews.
According to Taeihagh & Lim, 2018, the privacy and other benefits provided by AVs are harnessed both positively and negatively. The negative exploitation of AV technology includes the opportunities it presents to terrorists to re-innovate their mode of operation to include the use of vehicle-borne improvised explosive device (VBIED). The potential security threat from AVs has been reiterated by the US Federal Bureau of Investigation (FBI) soon after Google rolled out its fleet of AVs (Taeihagh & Lim, 2018). The authors cite an FBI bulletin which highlighted that researchers were able to commander AVs by hacking their wireless connection. This assertion implies that AVs are vulnerable to actions by criminal elements and terrorists to exploit its weaknesses. The study highlighted by the FBI indicated that researchers have successfully hacked an AV’s central operations system and commanded the vehicle’s braking, steering, and engine functions.
The authors conclude that the utilization of AVs for nefarious purposes should be at the forefront of future policies of both cyber-security and counter-terrorism organizations. The main challenges in creating a meaningful and effective policy to tackle threats posed by the exploitation of AVs by hackers, criminals, and terrorists in future, is the drastic and unpredictable evolution of potential risks along with technological advancements. This assertion implies that the current security issues will not likely be the same as the threats in a truly autonomous world.
Implications
The magnitude of threat posed by the exploitation of AVs by criminals and terrorists highlights that the emerging issues cannot be sufficiently addressed by regulations alone. It is important that governments participate fully in their respective AV industry right from the manufacturing to the roll out stage. The participation of the government will ensure that AV manufacturer have taken basic security precaution by adhering to a predefined secure coding practice. Cyber security agencies should work in collaboration with transport authorities and AV manufacturer to craft a future roadmap of security which encompasses or includes the threat posed by millions of AVs in the public sphere.
References
Rathod, D. Sheetal. (2013). “An autonomous driverless car: An idea to overcome the urban challenges.” Journal of Information Engineering and Application, Vol. 3, No. 13
Smith, Anna. (2018). “The impact of self-driving cars on transportation.” The Eye for Transport Organization.
Taeihagh, A. & Lim, H. S. (2018). “Governing autonomous vehicles: Emerging responses for safety, liability, privacy, cyber-security, and industry risks.” Transport Reviews.
Wei, Z., Yang, Y., Rehana, Y., Wu, Y., Weng, J., and Deng, R. (2017). “IoVShield: An efficient vehicular intrusion detection system for self-driving.” Information Security Practice and Experience, pp. 638-647
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