Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity

Aldhyani, Theyazn H. H.; Alkahtani, Hasan

doi:10.3390/s22010360

Cited by 72 publications

(40 citation statements)

References 53 publications

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“…The input gate revives the memory cells in the LSTM structure, and the hidden state is always controlled by the output gate. Furthermore, LSTM uses an embedded memory block and gate mechanism that enables it to address complications related to the disappearing gradient and the explosion gradient present in the RNN learning [ 66 ]. The structure of the LSTM model is presented in Figure 7 .…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

Alkahtani

Aldhyani

2022

Sensors

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With the rapid expansion of the use of smartphone devices, malicious attacks against Android mobile devices have increased. The Android system adopted a wide range of sensitive applications such as banking applications; therefore, it is becoming the target of malware that exploits the vulnerabilities of the security system. A few studies proposed models for the detection of mobile malware. Nevertheless, improvements are required to achieve maximum efficiency and performance. Hence, we implemented machine learning and deep learning approaches to detect Android-directed malicious attacks. The support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), long short-term memory (LSTM), convolution neural network-long short-term memory (CNN-LSTM), and autoencoder algorithms were applied to identify malware in mobile environments. The cybersecurity system was tested with two Android mobile benchmark datasets. The correlation was calculated to find the high-percentage significant features of these systems in the protection against attacks. The machine learning and deep learning algorithms successfully detected the malware on Android applications. The SVM algorithm achieved the highest accuracy (100%) using the CICAndMal2017 dataset. The LSTM model also achieved a high percentage accuracy (99.40%) using the Drebin dataset. Additionally, by calculating the mean error, mean square error, root mean square error, and Pearson correlation, we found a strong relationship between the predicted values and the target values in the validation phase. The correlation coefficient for the SVM method was R2 = 100% using the CICAndMal2017 dataset, and LSTM achieved R2 = 97.39% in the Drebin dataset. Our results were compared with existing security systems, showing that the SVM, LSTM, and CNN-LSTM algorithms are of high efficiency in the detection of malware in the Android environment.

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Section: Methodsmentioning

confidence: 99%

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

Alkahtani

Aldhyani

2022

Sensors

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“…Many additional studies of Cabassi at al. [ 7 ], Aldhyani et al [ 3 ], and Tuli et al [ 16 ], have looked at the security dangers and issues that drones pose. Khan et al [ 17 ] presented an effective and intelligent edge-assisted IDT authentication scheme that secured the NoD.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It provides enhanced capabilities by object configuration in the smart city [ 2 ]. Drone technology has recently led to the development of miniature drones such as quadcopters and micro drones [ 3 ]. These small drones have the advantage of being able to easily enter any infrastructure for tracking numerous domains including industrial surveillance and disaster response, search and rescue, military usage, accurate agriculture, shipping, and delivery.…”

Section: Introductionmentioning

confidence: 99%

Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective

Aldaej

Ahanger

Atiquzzaman

et al. 2022

Sensors

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Drone advancements have ushered in new trends and possibilities in a variety of sectors, particularly for small-sized drones. Drones provide navigational interlocation services, which are made possible by the Internet of Things (IoT). Drone networks, on the other hand, are subject to privacy and security risks due to design flaws. To achieve the desired performance, it is necessary to create a protected network. The goal of the current study is to look at recent privacy and security concerns influencing the network of drones (NoD). The current research emphasizes the importance of a security-empowered drone network to prevent interception and intrusion. A hybrid ML technique of logistic regression and random forest is used for the purpose of classification of data instances for maximal efficacy. By incorporating sophisticated artificial-intelligence-inspired techniques into the framework of a NoD, the proposed technique mitigates cybersecurity vulnerabilities while making the NoD protected and secure. For validation purposes, the suggested technique is tested against a challenging dataset, registering enhanced performance results in terms of temporal efficacy (34.56 s), statistical measures (precision (97.68%), accuracy (98.58%), recall (98.59%), F-measure (99.01%), reliability (94.69%), and stability (0.73).

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“…• Fuzzy Attack: The technique of introducing random data into software and evaluating the results to uncover possibly exploitable vulnerabilities is what a fuzzy attack is [23]. • Spoofing Attack: Spoofing is the act of changing the appearance of a message or identification so that it pretends to come from a reliable, authentic source [10]. Spoofing attacks are further categorized into revolution per minute (RPM) and gear spoofing attack [24].…”

Section: B Can Attack Typesmentioning

confidence: 99%

“…The best part is that they let drivers access their phone calls, emails, and voicemails without having to take their hands off the wheel [14]. An IVN is formed by all of the communications that take place between these sections [10] [11].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Intra-Vehicle Networks

Dwivedi¹

2022

Preprint

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The progression of innovation and technology and ease of inter-connectivity among networks has allowed us to evolve towards one of the promising areas, the Internet of Vehicles. Nowadays, modern vehicles are connected to a range of networks, including intra-vehicle networks and external networks. However, a primary challenge in the automotive industry is to make the vehicle safe and reliable; particularly with the loopholes in the existing traditional protocols, cyberattacks on the vehicle network are rising drastically. Practically every vehicle uses the universal Controller Area Network (CAN) bus protocol for the communication between electronic control units to transmit key vehicle functionality and messages related to driver safety. The CAN bus system, although its critical significance, lacks the key feature of any protocol authentication and authorization. Resulting in compromises of CAN bus security leads to serious issues to both car and driver safety. This paper discusses the security issues of the CAN bus protocol and proposes an Intrusion Detection System (IDS) that detects known attacks on in-vehicle networks. Multiple Artificial Intelligence (AI) algorithms are employed to provide recognition of known potential cyber-attacks based on messages, timestamps, and data packets traveling through the CAN. The main objective of this paper is to accurately detect cyberattacks by considering time-series features and attack frequency. The majority of the evaluated AI algorithms, when considering attack frequency, correctly identify known attacks with remarkable accuracy of more than 99%. However, these models achieve approximately 92% to 97% accuracy when timestamps are not taken into account. Long Short Term Memory (LSTM), Xgboost, and SVC have proved to the well-performing classifiers.

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Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity

Cited by 72 publications

References 53 publications

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective

Anomaly Detection in Intra-Vehicle Networks

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