On the Security of Cyber-Physical Systems Against Stochastic Cyber-Attacks Models

Al‐Haija, Qasem Abu

doi:10.1109/iemtronics52119.2021.9422623

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…Cyberattacks are meant to deliberate an exploitation of system resources to compromise necessary data and utilize the system in illegal usage [ 46 ]. Cyberattacks usually are launched against the victim system to violate the major security services, such as confidentiality, integrity, and availability.…”

Section: System Modelingmentioning

confidence: 99%

Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

Al‐Haija

Badawi²

2021

Sensors

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Network Intrusion Detection Systems (NIDSs) are indispensable defensive tools against various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs employ several methods to build profiles for normal and malicious behaviors. In this paper, we design, implement, and evaluate the performance of machine-learning-based NIDS in IoT networks. Specifically, we study six supervised learning methods that belong to three different classes: (1) ensemble methods, (2) neural network methods, and (3) kernel methods. To evaluate the developed NIDSs, we use the distilled-Kitsune-2018 and NSL-KDD datasets, both consisting of a contemporary real-world IoT network traffic subjected to different network attacks. Standard performance evaluation metrics from the machine-learning literature are used to evaluate the identification accuracy, error rates, and inference speed. Our empirical analysis indicates that ensemble methods provide better accuracy and lower error rates compared with neural network and kernel methods. On the other hand, neural network methods provide the highest inference speed which proves their suitability for high-bandwidth networks. We also provide a comparison with state-of-the-art solutions and show that our best results are better than any prior art by 1~20%.

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Section: System Modelingmentioning

confidence: 99%

Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

Al‐Haija

Badawi²

2021

Sensors

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“…They can also gain unauthorized access and modify sensitive data, disrupt normal operations of the IoT, or damage the IoT altogether. The vulnerability can exist in both hardware and software components of the IoT [5]. Hardware vulnerabilities are difficult to detect and much harder to fix due to various embedded microprograms in them.…”

Section: Introductionmentioning

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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“…The obligation of getting an effective, dependable, and malleable communication foundation is accomplished by facilitating real-time data conversation between various smart and conventional CPS/IoT elements. 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].…”

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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On the Security of Cyber-Physical Systems Against Stochastic Cyber-Attacks Models

Cited by 14 publications

References 19 publications

Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

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

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

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