Hybrid intrusion detection in connected self-driving vehicles

Alheeti, Khattab M. Ali; McDonald-Maier, Klaus

doi:10.1109/iconac.2016.7604962

Cited by 20 publications

(6 citation statements)

References 15 publications

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“…Denial of Service attacks is a very serious attack where the main aim is to prevent the authentic users from accessing the network and network resources [27]. The attackers transmit dummy messages into the network to overwhelm the current users, thus reducing the efficiency and performance of the network.…”

Section: Attacks Against the Communication Systemmentioning

confidence: 99%

Autonomous vehicles: A study of implementation and security

Khan

Kumar

Kadry³

et al. 2021

IJECE

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<span>Autonomous vehicles have been invented to increase the safety of transportation users. These vehicles can sense their environment and make decisions without any external aid to produce an optimal route to reach a destination. Even though the idea sounds futuristic and if implemented successfully, many current issues related to transportation will be solved, care needs to be taken before implementing the solution. This paper will look at the pros and cons of implementation of autonomous vehicles. The vehicles depend highly on the sensors present on the vehicles and any tampering or manipulation of the data generated and <span>transmitted by these can have disastrous consequences, as human lives are at stake here. Various attacks</span> against the different type of sensors on-board an autonomous vehicle are covered.</span>

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Section: Attacks Against the Communication Systemmentioning

confidence: 99%

Autonomous vehicles: A study of implementation and security

Khan

Kumar

Kadry³

et al. 2021

IJECE

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“…These simulation datasets are used to validate the deep learning model in terms of accuracy. Intruder detection in autonomous vehicle or Self driving vehicles [15] simulated in cutting edge tools.…”

Section: Traffic Simulator and Network Simulatormentioning

confidence: 99%

Security: Intrusion Prevention System Using Deep Learning on the Internet of Vehicles

Praneeth¹,

Kumar²,

Karyemsetty³

2021

IJSSE

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Internet of vehicles supports to transfer of safety-related messages, which help to mitigate road accidents. Internet of vehicles allows vehicle to cooperative communicate, share position and speed data among vehicles and road side units. The vehicular network become prone to large number of attacks including false warnings, mispositioning of vehicles etc. The authentication of messages to identify the normal message packet from attack messages packet and its prevention is a major challenging task. This paper focuses on applying deep learning approach using binary classification to classify the normal packets from malicious packets. The process starts with preparing the training dataset from the open-source KDD99 and CICIDS 2018 datasets, consisting of 1,20,223 network packets with 41 features. The one-dimensional network data is preprocessed using an autoencoder to eliminate the unwanted data in the initial stage. The valuable features are then filtered as 23 out of 41, and the model is trained with structured deep neural networks, then combined with the Softmax classifier and Relu activation functions. The proposed Intrusion prevention model is trained and tested with google Colab, an open platform cloud service, and the open-source tensor flow. The proposed prevention classifier model was validated with the simulation dataset generated in network simulation. The experimental results show 99.57% accuracy, which is the highest among existing RNN and CNN-based models. In the future, the model can be trained on different datasets, which will further improve the model's efficiency and accuracy.

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“…Disrupt communication typically by overwhelming the network with large volumes of meaningless or false data, such as fake alert messages about road accidents and congestion [30], [31], [32], [26], [33], [27], [34], [35], [36], [37] Bus-off attack Exploit the error-handling scheme of in-vehicle networks, by deceiving an uncompromised ECU into thinking it is defective, and eventually forcing itself or even the whole network to shut down [38] Message Distortion Generate distorted reliability message in a vehicular network and activate distribution of this message to a neighbouring vehicle [39] Timing attack An integrity attack that alters message timeslots [40] Replay attack A valid data transmission, such as a command or a sensor reading, is recorded and maliciously repeated at a later point.…”

Section: Attackmentioning

confidence: 99%

A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles

Karapistoli²,

et al. 2019

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With the growing threat of cyber and cyber-physical attacks against automobiles, drones, ships, driverless pods and other vehicles, there is also a growing need for intrusion detection approaches that can facilitate defence against such threats. Vehicles tend to have limited processing resources and are energy-constrained. So, any security provision needs to abide by these limitations. At the same time, attacks against vehicles are very rare, often making knowledge-based intrusion detection systems less practical than behaviour-based ones, which is the reverse of what is seen in conventional computing systems. Furthermore, vehicle design and implementation can differ wildly between different types or different manufacturers, which can lead to intrusion detection designs that are vehicle-specific. Equally importantly, vehicles are practically defined by their ability to move, autonomously or not. Movement, as well as other physical manifestations of their operation may allow cyber security breaches to lead to physical damage, but can also be an opportunity for detection. For example, physical sensing can contribute to more accurate or more rapid intrusion detection through observation and analysis of physical manifestations of a security breach. This paper presents a classification and survey of intrusion detection systems designed and evaluated specifically on vehicles and networks of vehicles. Its aim is to help identify existing techniques that can be adopted in the industry, along with their advantages and disadvantages, as well as to identify gaps in the literature, which are attractive and highly meaningful areas of future research.

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Hybrid intrusion detection in connected self-driving vehicles

Cited by 20 publications

References 15 publications

Autonomous vehicles: A study of implementation and security

Autonomous vehicles: A study of implementation and security

Security: Intrusion Prevention System Using Deep Learning on the Internet of Vehicles

A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles

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