SeArch: A Collaborative and Intelligent NIDS Architecture for SDN-Based Cloud IoT Networks

Nguyen, Thi Thanh Huyen; Phan, Trung V.; Nguyen, Binh T.; So-In, Chakchai; Baig, Zubair A.; Sanguanpong, Surasak

doi:10.1109/access.2019.2932438

Cited by 102 publications

(46 citation statements)

References 41 publications

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“…In contrast, Nguyen et al [98] proposed the use of software-defined networking (SDN), network function virtualization (NFV), and machine learning-based detection for implementing IDS solutions, due to their performance advantages. SeArch, a Network-based Intrusion Detection System for a SDN-based cloud IoT environment, comprises of tiered layers of nodes that collaborate to detect irregularities and devise mitigation policies for the IoT gateway devices to stop intrusions.…”

Section: Ids For Iot Networkmentioning

confidence: 99%

Bluetooth Low Energy Mesh Networks: Survey of Communication and Security Protocols

Ghori

Wan

Sodhy

2020

Sensors

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Bluetooth Low Energy (BLE) Mesh Networks enable flexible and reliable communications for low-power Internet of Things (IoT) devices. Most BLE-based mesh protocols are implemented as overlays on top of the standard Bluetooth star topologies while using piconets and scatternets. Nonetheless, mesh topology support has increased the vulnerability of BLE to security threats, since a larger number of devices can participate in a BLE Mesh network. To address these concerns, BLE version 5 enhanced existing BLE security features to deal with various authenticity, integrity, and confidentiality issues. However, there is still a lack of detailed studies related to these new security features. This survey examines the most recent BLE-based mesh network protocols and related security issues. In the first part, the latest BLE-based mesh communication protocols are discussed. The analysis shows that the implementation of BLE pure mesh protocols remains an open research issue. Moreover, there is a lack of auto-configuration mechanisms in order to support bootstrapping of BLE pure mesh networks. In the second part, recent BLE-related security issues and vulnerabilities are highlighted. Strong Intrusion Detection Systems (IDS) are essential for detecting security breaches in order to protect against zero-day exploits. Nonetheless, viable IDS solutions for BLE Mesh networks remain a nascent research area. Consequently, a comparative survey of IDS approaches for related low-power wireless protocols was used to map out potential approaches for enhancing IDS solutions for BLE Mesh networks.

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Section: Ids For Iot Networkmentioning

confidence: 99%

Bluetooth Low Energy Mesh Networks: Survey of Communication and Security Protocols

Ghori

Wan

Sodhy

2020

Sensors

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“…Elrawy, Awad, and Hamed (2018) Fahim and Sillitti (2019) presents a comprehensive survey of various attacks in IoT‐based smart environments, recent IoT paradigm, various IDS architectures for IoT systems and several recent security challenges in IoT‐based smart environments. Nguyen et al (2019) proposed a new collaborative and intelligent NIDS architecture for anomaly detection in SDN‐based cloud IoT networks which obtained better detection accuracy results for DDoS attacks. Gunupudi et al (2017) Kumar, Mangathayaru, Narsimha, and Cheruvu (2018) Mangathayaru, Kumar, and Narsimha (2016) Kumar, Mangathayaru, Narsimha, and Reddy (2017) propose self‐constructing feature clustering technique for anomaly detection using Gaussian‐based approach.…”

Section: Literature Surveymentioning

confidence: 99%

A machine learning approach for imputation and anomaly detection in IoT environment

Radhakrishna

Kumar

Puligadda³

et al. 2020

Expert Systems

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The problem of anomaly and attack detection in IoT environment is one of the prime challenges in the domain of internet of things that requires an immediate concern. For example, anomalies and attacks in IoT environment such as scan, malicious operation, denial of service, spying, data type probing, wrong setup, malicious control can lead to failure of an IoT system. Datasets generated in an IoT environment usually have missing values. The presence of missing values makes the classifier unsuitable for classification task. This article introduces (a) a novel imputation technique for imputation of missing data values (b) a classifier which is based on feature transformation to perform classification (c) imputation measure for similarity computation between any two instances that can also be used as similarity measure. The performance of proposed classifier is studied by using imputed datasets obtained through applying Kmeans, F-Kmeans and proposed imputation methods. Experiments are also conducted by applying existing and proposed classifiers on the imputed dataset obtained using proposed imputation technique. For experimental study in this article, we have used an open source dataset named distributed smart space orchestration system publicly available from Kaggle. Experiment results obtained are also validated using Wilcoxon non-parametric statistical test. It is proved that the performance of proposed approach is better when compared to existing classifiers when the imputation process is performed using F-Kmeans and K-Means imputation techniques. It is also observed that accuracies for attack classes scan, malicious operation, denial of service, spying, data type probing, wrong setup are 100% while it is 99% for malicious control attack class when the proposed imputation and classification technique are applied.

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“…SDN based networks are not only limited to provide the QoS but also live VM management in which the server can interrupt or exploit the information from one end to another. It is also believed that if SDN is implemented in the cloud it can offer new opportunities for network security to be enhanced since cloud-based SDN provides faster reaction and greater flexibility when the condition is getting revised [55].…”

Section: Sdn Enabled Cloud Computingmentioning

confidence: 99%

“…SDN-empowered Cloud Computing (SDN-cloud or SDN-empowered clouds) specified to SDN-DC, as well as inter-cloud organizing that consumes the SDN utilization over various data centres and WAN. In this way, SDN benefits can be connected to internetworking domains [54,55].…”

Section: Need For Software Defined Cloud Data Centres (Sdn-dc)mentioning

confidence: 99%

“…SDN empowered architecture is separated into three layers. Figure 5 explains the abstract architecture of cloud computing empowered by SDN, based on the literature [46,[53][54][55]. The SDN controller controls the system related capacities, which are associated with the director of the cloud through APIs that are north-bound.…”

Section: Sdn-enabled Cloud Computing Architecturementioning

confidence: 99%

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A Review on Software-Defined Networking Enabled Iot Cloud Computing

Badotra¹,

Panda²

2019

IIUMEJ

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Making use of Internet of Things (IoT) is becoming the necessity of today’s life. The data collected and stored through IoT devices comes from the cloud and therefore cloud computing is acting as a backbone for supporting IoT. But it is easy to forget that the cloud is not completely digital in some areas of the world and there is a need for a data centre where data storage can be achieved. Cloud data centres are facing many difficulties and issues because they are using traditional methods of networking. This is where Software Defined Networking (SDN) has come into view; where it has changed the way traditional networks are operated. For example, the separation of the intelligence of the network devices within the data path can be useful in making networks agile and manageable. This paper aims to provide problem areas in the networking used by the cloud data centres and the role of SDN to overcome these issues. Platforms for providing the experimental setup for collaboration between SDN and cloud will ultimately be beneficial in setting up the SDN- enabled IoT cloud, and this is also discussed with the open research problems. ABSTRAK: Penggunaan Internet Benda (IoT) menjadi keperluan kepada kehendak hari ini. Data yang dikumpul dan disimpan melalui peranti IoT berasal dari awan dan oleh itu pengkomputeran awan bertindak sebagai tulang belakang bagi menyokong IoT. Tetapi, satu perkara yang dilupakan adalah bahawa awan itu tidak sepenuhnya digital, di suatu tempat dalam dunia ini, terdapat keperluan pada pusat data di mana penyimpanan data boleh dicapai. Pusat data awan menghadapi banyak masalah dan isu kerana ianya menggunakan kaedah rangkaian tradisional. Di sinilah Perisian Definisi Perangkaian (SDN) dipandang; ia telah mengubah cara rangkaian tradisional dikendalikan. Sebagai contoh, pemisahan kecerdasan peranti rangkaian dengan laluan data berguna bagi membuat rangkaian pintar dan boleh diurus. Kajian ini bertujuan bagi menyediakan ruang masalah dalam rangkaian yang digunakan oleh pusat Data Awan dan peranan SDN dalam mengatasi masalah ini. Platform persediaan eksperimen bagi kerjasama SDN dan awan akhirnya bermanfaat dalam menubuhkan pengaktifan-SDN awan IOT, juga turut dibincangkan dalam kajian ini berkenaan masalah penyelidikan terbuka.

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SeArch: A Collaborative and Intelligent NIDS Architecture for SDN-Based Cloud IoT Networks

Cited by 102 publications

References 41 publications

Bluetooth Low Energy Mesh Networks: Survey of Communication and Security Protocols

Bluetooth Low Energy Mesh Networks: Survey of Communication and Security Protocols

A machine learning approach for imputation and anomaly detection in IoT environment

A Review on Software-Defined Networking Enabled Iot Cloud Computing

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