A Novel Approach towards the Design and Implementation of Virtual Network Based on Controller in Future IoT Applications

Mehmood, Faisal; Ullah, Israr; Ahmad, Shabir; Kim, Do‐Hyeun

doi:10.3390/electronics9040604

Cited by 16 publications

(8 citation statements)

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“…In [ 101 ], authors provide a secure virtual framework that was built in a smart home environment. The proposed framework is created to be further applied on all virtual smart use cases, from smart cars to smart factories.…”

Section: Methods and Datasetsmentioning

confidence: 99%

Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1—A New IoT Dataset

Liu

Thapa

Shaver

et al. 2021

Sensors

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As Internet of Things (IoT) networks expand globally with an annual increase of active devices, providing better safeguards to threats is becoming more prominent. An intrusion detection system (IDS) is the most viable solution that mitigates the threats of cyberattacks. Given the many constraints of the ever-changing network environment of IoT devices, an effective yet lightweight IDS is required to detect cyber anomalies and categorize various cyberattacks. Additionally, most publicly available datasets used for research do not reflect the recent network behaviors, nor are they made from IoT networks. To address these issues, in this paper, we have the following contributions: (1) we create a dataset from IoT networks, namely, the Center for Cyber Defense (CCD) IoT Network Intrusion Dataset V1 (CCD-INID-V1); (2) we propose a hybrid lightweight form of IDS—an embedded model (EM) for feature selection and a convolutional neural network (CNN) for attack detection and classification. The proposed method has two models: (a) RCNN: Random Forest (RF) is combined with CNN and (b) XCNN: eXtreme Gradient Boosting (XGBoost) is combined with CNN. RF and XGBoost are the embedded models to reduce less impactful features. (3) We attempt anomaly (binary) classifications and attack-based (multiclass) classifications on CCD-INID-V1 and two other IoT datasets, the detection_of_IoT_botnet_attacks_N_BaIoT dataset (Balot) and the CIRA-CIC-DoHBrw-2020 dataset (DoH20), to explore the effectiveness of these learning-based security models. Using RCNN, we achieved an Area under the Receiver Characteristic Operator (ROC) Curve (AUC) score of 0.956 with a runtime of 32.28 s on CCD-INID-V1, 0.999 with a runtime of 71.46 s on Balot, and 0.986 with a runtime of 35.45 s on DoH20. Using XCNN, we achieved an AUC score of 0.998 with a runtime of 51.38 s for CCD-INID-V1, 0.999 with a runtime of 72.12 s for Balot, and 0.999 with a runtime of 72.91 s for DoH20. Compared to KNN, XCNN required 86.98% less computational time, and RCNN required 91.74% less computational time to achieve equal or better accurate anomaly detections. We find XCNN and RCNN are consistently efficient and handle scalability well; in particular, 1000 times faster than KNN when dealing with a relatively larger dataset-Balot. Finally, we highlight RCNN and XCNN’s ability to accurately detect anomalies with a significant reduction in computational time. This advantage grants flexibility for the IDS placement strategy. Our IDS can be placed at a central server as well as resource-constrained edge devices. Our lightweight IDS requires low train time and hence decreases reaction time to zero-day attacks.

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Section: Methods and Datasetsmentioning

confidence: 99%

Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1—A New IoT Dataset

Liu

Thapa

Shaver

et al. 2021

Sensors

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“…The physical objects in museums, such as statues, coins, swords, musical instruments, etc., belong to prehistoric and historic periods [ 1 ]. These objects are lifeless and dreary, having no understanding of what has been happening in the surroundings [ 2 , 3 ]. These lifeless objects need to be organized and streamlined into a uniform layered.…”

Section: Introductionmentioning

confidence: 99%

An IoT-Enabled Information System for Smart Navigation in Museums

Khan

Rahman

Faisal

et al. 2021

Sensors

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The Internet of Things (IoT) is a new paradigm that connects objects to provide seamless communication and contextual information to anyone, anywhere, at any time (AAA). These Internet-of-Things-enabled automated objects interact with visitors to present a variety of information during museum navigation and exploration. In this article, a smart navigation and information system (SNIS) prototype for museum navigation and exploration is developed, which delivers an interactive and more exciting museum exploration experience based on the visitor’s personal presence. The objects inside a museum share the information that assist and navigate the visitors about the different sections and objects of the museum. The system was deployed inside Chakdara Museum and experimented with 381 users to achieve the results. For results, different users marked the proposed system in terms of parameters such as interesting, reality, ease of use, satisfaction, usefulness, and user friendly. Of these 381 users, 201 marked the system as most interesting, 138 marked most realistic, 121 marked it as easy-in-use, 219 marked it useful, and 210 marked it as user friendly. These statistics prove the efficiency of SNIS and its usefulness in smart cultural heritage, including smart museums, exhibitions and cultural sites.

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“…IoT connects all objects and devices to a virtual world [4] with all‐digital tools. This paradigm is both advantageous and disadvantageous [5,6]. “Internet of Things,” a term coined by Kevin Ashton in 1999 [7], can generate, analyze, and make decisions about connected objects; accordingly, it has received much attention in recent years and is projected to connect up to 100 billion devices by 2025 [8].…”

Section: Introductionmentioning

confidence: 99%

A new method to detect attacks on the Internet of Things (IoT) using adaptive learning based on cellular learning automata

2021

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The Internet of Things (IoT) is a new paradigm that connects physical and virtual objects from various domains such as home automation, industrial processes, human health, and monitoring. IoT sensors receive information from their environment and forward it to their neighboring nodes. However, the large amounts of exchanged data are vulnerable to attacks that reduce the network performance. Most of the previous security methods for IoT have neglected the energy consumption of IoT, thereby affecting the performance and reducing the network lifetime. This paper presents a new multistep routing protocol based on cellular learning automata. The network lifetime is improved by a performance‐based adaptive reward and fine parameters. Nodes can vote on the reliability of their neighbors, achieving network reliability and a reasonable level of security. Overall, the proposed method balances the security and reliability with the energy consumption of the network.

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A Novel Approach towards the Design and Implementation of Virtual Network Based on Controller in Future IoT Applications

Cited by 16 publications

References 50 publications

Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1—A New IoT Dataset

Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1—A New IoT Dataset

An IoT-Enabled Information System for Smart Navigation in Museums

A new method to detect attacks on the Internet of Things (IoT) using adaptive learning based on cellular learning automata

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