A Real Time Node Connectivity Algorithm for Synchronous Cyber Physical and IoT Network Systems

Al‐Haija, Qasem Abu; McCurry, Charles D.; Zein-Sabatto, Saleh

doi:10.1109/southeastcon44009.2020.9249730

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…The lowest performance rates were reported for the ensemble of subspace discriminator (En_Dsc) scoring accuracy that falls below the best model by 10–20%. Overall, model provided the best outcomes in terms of all performance indicators along with a very low detection/classification time ( ) that makes it suitable for real-time communication of Internet of things (IoT) and Cyber-Physical Systems (CPS) networks [ 43 , 46 ].…”

Section: Results and Analysismentioning

confidence: 99%

An intelligent identification and classification system for malicious uniform resource locators (URLs)

Al‐Haija

Al-Fayoumi

2023

Neural Comput & Applic

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Uniform Resource Locator (URL) is a unique identifier composed of protocol and domain name used to locate and retrieve a resource on the Internet. Like any Internet service, URLs (also called websites) are vulnerable to compromise by attackers to develop Malicious URLs that can exploit/devastate the user’s information and resources. Malicious URLs are usually designed with the intention of promoting cyber-attacks such as spam, phishing, malware, and defacement. These websites usually require action on the user’s side and can reach users across emails, text messages, pop-ups, or devious advertisements. They have a potential impact that can reach, in some cases, to compromise the machine or network of the user, especially those arriving by email. Therefore, developing systems to detect malicious URLs is of great interest nowadays. This paper proposes a high-performance machine learning-based detection system to identify Malicious URLs. The proposed system provides two layers of detection. Firstly, we identify the URLs as either benign or malware using a binary classifier. Secondly, we classify the URL classes based on their feature into five classes: benign, spam, phishing, malware, and defacement. Specifically, we report on four ensemble learning approaches, viz. the ensemble of bagging trees (En_Bag) approach, the ensemble of k-nearest neighbor (En_kNN) approach, and the ensemble of boosted decision trees (En_Bos) approach, and the ensemble of subspace discriminator (En_Dsc) approach. The developed approaches have been evaluated on an inclusive and contemporary dataset for uniform resource locators (ISCX-URL2016). ISCX-URL2016 provides a lightweight dataset for detecting and categorizing malicious URLs according to their attack type and lexical analysis. Conventional machine learning evaluation measurements are used to evaluate the detection accuracy, precision, recall, F Score, and detection time. Our experiential assessment indicates that the ensemble of bagging trees (En_Bag) approach provides better performance rates than other ensemble methods. Alternatively, the ensemble of the k-nearest neighbor (En_kNN) approach provides the highest inference speed. We also contrast our En_Bag model with state-of-the-art solutions and show its superiority in binary classification and multi-classification with accuracy rates of 99.3% and 97.92%, respectively.

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Section: Results and Analysismentioning

confidence: 99%

An intelligent identification and classification system for malicious uniform resource locators (URLs)

Al‐Haija

Al-Fayoumi

2023

Neural Comput & Applic

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“…Cyber-Physical Systems (CPS) are orchestrated systems that incorporate computation, networking, and physical processes as well as security services against several cyber-attack vectors [2]. CPS combines the dynamics of the physical processes with those of the software and networking components, providing abstractions and modeling, design, and analysis techniques for the integrated real-time CPS [3,4]. As such, technological evolution has transformed traditional vehicles into autonomous vehicles that play an essential role in developing smart cities [5].…”

Section: Introductionmentioning

confidence: 99%

Analytical Study for Cyber Dynamic Continuous Time Vehicle Model with Networked PID Controller

Al‐Haija¹

2022

MMEP

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This paper uses different physical scenarios to analyze the cyber dynamic continuous time vehicle model with a networked PID controller. First, we model the problem of stateful continuous-time components for the dynamic physical vehicle motion on a graded and flat road without using a PID controller. Second, we model the problem of stateful continuous-time components for the dynamic physical vehicle motion on a graded road using a PID controller in different specification scenarios. Third, we model the problem of stateful continuous-time components for the cyber-physical dynamic vehicle model motion using a networked PID controller at different specification scenarios. The simulation results indicated the superiority and efficacy of the cyber-physical dynamic vehicle model providing high stability and controllability in the vehicle motion using a PID controller and Transceiver (network node).

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“…introduces many benefits, such as exchanging information between embedded devices, sensors, actuators, and other technology. This is important to ensure the requirement for high connectivity between such synchronous cyber-physical systems [5]. In addition, integrating a communication network into physical components allows remote control and resource management [6].…”

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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A Real Time Node Connectivity Algorithm for Synchronous Cyber Physical and IoT Network Systems

Cited by 7 publications

References 14 publications

An intelligent identification and classification system for malicious uniform resource locators (URLs)

An intelligent identification and classification system for malicious uniform resource locators (URLs)

Analytical Study for Cyber Dynamic Continuous Time Vehicle Model with Networked PID Controller

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

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