Top-Down Machine Learning-Based Architecture for Cyberattacks Identification and Classification in IoT Communication Networks

Al-Haija, Qasem Abu

doi:10.3389/fdata.2021.782902

Cited by 13 publications

(7 citation statements)

References 43 publications

(68 reference statements)

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“…Botnet detection techniques for the IoT are either network-based or host-based [16][17][18][19][20]. However, the host-based approach is less realistic.…”

Section: Related Workmentioning

confidence: 99%

“…However, the host-based approach is less realistic. For instance, in [16,17], the authors developed a comprehensive architecture for IoT instruction detection and classification at the network layer of the IoT paradigm. Six different supervised ML methods were employed to develop the IoT-IDS: three ensemble learning methods, two neural network methods, and one kernel method.…”

Section: Related Workmentioning

confidence: 99%

ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks

Al-Haija

Al-Dala’ien

2022

JSAN

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Due to the prompt expansion and development of intelligent systems and autonomous, energy-aware sensing devices, the Internet of Things (IoT) has remarkably grown and obstructed nearly all applications in our daily life. However, constraints in computation, storage, and communication capabilities of IoT devices has led to an increase in IoT-based botnet attacks. To mitigate this threat, there is a need for a lightweight and anomaly-based detection system that can build profiles for normal and malicious activities over IoT networks. In this paper, we propose an ensemble learning model for botnet attack detection in IoT networks called ELBA-IoT that profiles behavior features of IoT networks and uses ensemble learning to identify anomalous network traffic from compromised IoT devices. In addition, our IoT-based botnet detection approach characterizes the evaluation of three different machine learning techniques that belong to decision tree techniques (AdaBoosted, RUSBoosted, and bagged). To evaluate ELBA-IoT, we used the N-BaIoT-2021 dataset, which comprises records of both normal IoT network traffic and botnet attack traffic of infected IoT devices. The experimental results demonstrate that our proposed ELBA-IoT can detect the botnet attacks launched from the compromised IoT devices with high detection accuracy (99.6%) and low inference overhead (40 µ-seconds). We also contrast ELBA-IoT results with other state-of-the-art results and demonstrate that ELBA-IoT is superior.

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“…can be implemented into the onboard computer to detect any security flaws, monitor the system's events, and report incidents that violate the security policy [19]. However, in cybersecurity, more than 99% of new intrusions are symmetrical with very small mutations of previously existing ones [20]. This requires the development of very accurate I.D.S.s with high sensitivity in detecting cyberattacks.…”

Section: Introductionmentioning

confidence: 99%

“…attacks and can threaten A.V.s' applications, systems, and network layers [11]. Generally, it is challenging to detect the FDI attack because, for example, some of its effects cannot be noticed in the short term [20].…”

Section: Introductionmentioning

confidence: 99%

Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model

Alsulami

Al-Haija²,

Alqahtani³

et al. 2022

Symmetry

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Technological advancement has transformed traditional vehicles into autonomous vehicles. Autonomous vehicles play an important role since they are considered an essential component of smart cities. The autonomous vehicle is an intelligent vehicle capable of maintaining safe driving by avoiding crashes caused by drivers. Unlike traditional vehicles, which are fully controlled and operated by humans, autonomous vehicles collect information about the outside environment using sensors to ensure safe navigation. Autonomous vehicles reduce environmental impact because they usually use electricity to operate instead of fossil fuel, thus decreasing the greenhouse gasses. However, autonomous vehicles could be threatened by cyberattacks, posing risks to human life. For example, researchers reported that Wi-Fi technology could be vulnerable to cyberattacks through Tesla and BMW autonomous vehicles. Therefore, further research is needed to detect cyberattacks targeting the control components of autonomous vehicles to mitigate their negative consequences. This research will contribute to the security of autonomous vehicles by detecting cyberattacks in the early stages. First, we inject False Data Injection (FDI) attacks into an autonomous vehicle simulation-based system developed by MathWorks. Inc. Second, we collect the dataset generated from the simulation model after integrating the cyberattack. Third, we implement an intelligent symmetrical anomaly detection method to identify false data cyber-attacks targeting the control system of autonomous vehicles through a compromised sensor. We utilize long short-term memory (LSTM) deep networks to detect False Data Injection (FDI) attacks in the early stage to ensure the stability of the operation of autonomous vehicles. Our method classifies the collected dataset into two classifications: normal and anomaly data. The experimental result shows that our proposed model’s accuracy is 99.95%. To this end, the proposed model outperforms other state-of-the-art models in the same study area.

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“…can be implemented into the onboard computer to detect any security flaws, monitor the system's events, and report incidents that violate the security policy [19]. However, like image recognition, in cybersecurity, more than 99% of new intrusions are symmetrical with very small mutations of previously existing ones [20]. This requires the development of very accurate I.D.S.s with high sensitivity in detecting cyber-attacks.…”

Section: Introductionmentioning

confidence: 99%

Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model

Alsulami¹,

Al-Haija²,

Alqahtani³

et al. 2022

Preprint

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Nowadays, technological advancement has transformed traditional vehicles into Au-tonomous Vehicles (A.V.s). In addition, in our daily lives, A.V.s play an important role since they are considered an essential component of smart cities. A.V. is an intelligent vehicle capable of main-taining safe driving by avoiding crashes caused by drivers. Unlike traditional vehicles, which are fully controlled and operated by humans, A.V.s collect information about the outside environment using sensors to ensure safe navigation. Furthermore, A.V.s reduce environmental impact because they usually use electricity to operate instead of fossil fuel, thus decreasing the greenhouse gasses. However, A.V.s could be threatened by cyberattacks, posing risks to human life. For example, re-searchers reported that Wi-Fi technology could be vulnerable to cyberattacks through Tesla and BMW AVs. Therefore, more research is needed to detect cyberattacks targeting the components of A.V.s to mitigate their negative consequences. This research will contribute to the security of A.V.s by detecting cyberattacks at the early stages. First, we inject False Data Injection (FDI) attacks into an A.V. simulation-based system developed by MathWorks. Inc. Second, we collect the dataset generated from the simulation model after integrating the cyberattack. Third, we implement an intelligent symmetrical anomaly detection method to identify FDI attacks targeting the control system of the A.V. through a compromised sensor. We use long short-term memory (LSTM) deep networks to detect FDI attacks in the early stage to ensure the stability of the operation of A.V.s. Our method classifies the collected dataset into two classifications: normal and anomaly data. The ex-perimental result shows that our proposed model's accuracy is 99.95%. To this end, the proposed model outperforms other state-of-the-art models in the same study area.

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Top-Down Machine Learning-Based Architecture for Cyberattacks Identification and Classification in IoT Communication Networks

Cited by 13 publications

References 43 publications

ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks

Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model

Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model

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