Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

Al‐Haija, Qasem Abu; Badawi, Ahmad Al

doi:10.3390/s22010241

Cited by 35 publications

(8 citation statements)

References 40 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The A.C.C. system composes speed control and distance control to adjust the dynamic of the ego car to be symmetrically commensurate with the lead car [29].…”

Section: Autonomous Vehicles Simulation Modelmentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The A.C.C. system composes speed control and distance control to adjust the dynamic of the ego car to be symmetrically commensurate with the lead car [29].…”

Section: Autonomous Vehicles Simulation Modelmentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

“…To validate the efficiency of the ELBA-IoT system, we used the standard evaluation metrics [37] over the k-fold datasets to measure the performance of the ELBA-IoT system using the four aforementioned variants of machine learning models: the AdaBoosted decision tree (ABDT) model, RUSBoosted decision tree (RBDT) model, bagged decision tree (BGDT) model, and ensemble learning model (ELBA-IoT). Figure 5 recapitulates the overall system of measurements employed in this research to validate the system performance and compare the quality of several models [17]. According to the figure (Figure 5), the performance of the developed models was evaluated in terms of confusion matrix analysis to discuss the number of positive and negative samples (predicted as true or false), the number of correctly classified samples (NCS#), the number of misclassified samples (NMS#), the number of correctly-classified samples (NCS#), the detection accuracy proportion (DAC%), the detection precision proportion (DPR%), the detection sensitivity proportion (DSN%), the detection harmonic score (DHS%), the detection inaccuracy error (IAE%), the detection imprecision error (IME%), and the detection insensitivity error (INE%).…”

Section: Implementation Of the Evaluation Process (Ep) Modulementioning

confidence: 99%

See 1 more Smart Citation

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

Al‐Haija

Al-Dala’ien

2022

JSAN

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Unlike the studies mentioned above, where models are developed through the learning-based scheme (training and testing) using predefined systematic Attack-Aware datasets [28] that contain features of common cyber-attacks (intrusions), this research contributes to the cybersecurity of A.V.s by detecting the False Data Injection (FDI) cyber-attacks developed at this research. First, we inject False Data Injection (FDI) attacks into an A.V.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Alsulami¹,

Al‐Haija²,

Alqahtani³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

Cited by 35 publications

References 40 publications

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

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

Contact Info

Product

Resources

About