Real Time DDoS Intrusion Detection and Monitoring Framework in 6LoWPAN for Internet of Things

Bediya, Arun Kumar; Kumar, Rajendra

doi:10.1109/gucon48875.2020.9231139

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…Mehta and Parma [31] This work determined a gap that conceivable attacks in OF should be perceptible IDS procedures along with FS Sheikhan and Bostani [63] This work declined to detect obscure attacks utilizing preferred characteristics for misuse-based detections Mayzaud et al [65,66] Even though authors believe their research is an appropriate mechanism for IoT-based anomaly detection, they have no compelling reason to detect attacks beyond DODAG Lee et al [36] This study does not evaluate the two objective functions OF0 and MRHOF according to EC and network flow metrics to detecting malicious actions Sousa et al [67] This research explained the various routing metrics and CAOF and OF-FL objective functions but did not consider them in the simulations Napiah et al [64] This work has eliminated the EC metric to ensure the effectiveness of the ML algorithms used and reduced the features to 5 features of 77 Datasets and classifiers related to ML Haq [68] This research surveys 49 relevant works and focuses discussions when creating ML-based IDS. The author also considers ML-based techniques, classifier mechanisms, efficient algorithms, datasets, also FS Nannan et al [69] In this paper, classification mechanisms, datasets, FS, ML-based solution, and efficient algorithms are [75], are used in IoT networks [76]. On the other hand, the papers are based on intrusion detection systems for IoT-related components.…”

Section: Iot Methodologiesmentioning

confidence: 99%

A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

Seyfollahi

Ghaffari

2021

Wireless Communications and Mobile Computing

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IPv6 routing protocol for low-power and lossy networks (RPL) has been developed as a routing agent in low-power and lossy networks (LLN), where nodes’ resource constraint nature is challenging. This protocol operates at the network layer and can create routing and optimally distribute routing information between nodes. RPL is a low-power, high-throughput IPv6 routing protocol that uses distance vectors. Each sensor-to-wire network router has a collection of fixed parents and a preferred parent on the path to the Destination-oriented directed acyclic graph (DODAG) graph’s root in steady-state. Each router part of the graph sends DODAG information object (DIO) control messages and specifies its rank within the graph, indicating its position within the network relative to the root. When a node receives a DIO message, it determines its network rank, which must be higher than all its parents’ rank, and then continues sending DIO messages using the trickle timer. As a result, DODAG begins at the root and eventually extends to encompass the whole network. This paper is the first review to study intrusion detection systems in the RPL protocol based on machine learning (ML) techniques to the best of our knowledge. The complexity of the new attack models identified for RPL and the efficiency of ML in intelligent and collaborative threats detection, and the issues of deploying ML in challenging LLN environments underscore the importance of research in this area. The analysis is done using research sources of “Google Scholar,” “Crossref,” “Scopus,” and “Web of Science” resources. The evaluations are assessed for studies from 2016 to 2021. The results are illustrated with tables and figures.

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Section: Iot Methodologiesmentioning

confidence: 99%

A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

Seyfollahi

Ghaffari

2021

Wireless Communications and Mobile Computing

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“…These attacks cause massive data theft and system susceptibility [8]. Moreover, an increase in connected IoT devices and their insecure nature gives adversaries more options to gain access to the devices and use them to launch further large-scale catastrophic attacks like DDoS [9]. The popularity of RPL in IoT applications renders the security of this protocol of paramount importance.…”

Section: Figure 1 Applications Of the Internet Of Thingsmentioning

confidence: 99%

High-Level Defence Model against Routing Attacks on the Internet-of-Things

Sejaphala,

Malele,

Lugayizi

2024

ijcs

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This paper is part of the doctoral work that aims to answer the following research question: “To what extent can an intelligent security model effectively defend against routing attacks in RPL-based Internet of Things (IoT) with a demonstration of less network resource consumption, high detection rate, and minimal false negatives?” To answer this question, this paper proposes a high-level conceptual framework to defend the IoT against routing attacks. In recent works, mitigation techniques have been proposed to act against routing attacks, however conceptual defence or mitigation framework is not presented as a set of steps to follow to develop an effective and robust intelligent security model. This paper aims to present a high-level conceptual defence framework against routing attacks; specifically, sinkhole, rank, DIS-Flooding, and worst parent. The four mentioned routing attacks are capable of disturbing IoT network functions and operations, and consuming network resources such as memory and power.

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“…[30] extracted indicators from IEEE 802.15.4, 6LoWPAN, IPv6 and COAP protocols parameters for detecting invalid CoAP request DoS attacks. Similarly, Granjal et al [35] investigated DDoS against CoAP and other protocols in the 6LoWPAN network.…”

Section: Related Workmentioning

confidence: 99%

A threat intelligence framework for protecting smart satellite-based healthcare networks

Al-Hawawreh

Moustafa

Slay

2021

Neural Comput & Applic

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Human-to-machine (H2M) communication is an important evolution in the industrial internet of health things (IIoHT), where many H2M interfaces are remotely interacting with industrial and medical assets. Lightweight protocols, such as constrained application protocol (CoAP), have been widely utilised in transferring sensing data of medical devices to end-users in smart satellite-based healthcare IIoT networks (SmartSat-IIoHT). However, such protocols are extensively deployed without appropriate security configurations, making attackers’ mission easier for abusing these protocols to launch advanced cyber threats. This paper, therefore, presents a new threat intelligence framework to examine and model CoAP protocol’s attacks in these systems. We present a ransom denial of service (RDoS) as a new threat that would exploit this protocol’s vulnerabilities. We propose many RDoS attack’s techniques to understand the attack indicators and analyse their behaviour on systems. Moreover, we present a real-time discovery of attacks’ network behaviours using deep learning. The experiment results demonstrate that this proposed discovery model obtains a better performance in revealing RDoS than other conventional machine learning algorithms and accomplishing high fidelity of protecting SmartSat-IIoHT networks.

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Real Time DDoS Intrusion Detection and Monitoring Framework in 6LoWPAN for Internet of Things

Cited by 5 publications

References 17 publications

A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

High-Level Defence Model against Routing Attacks on the Internet-of-Things

A threat intelligence framework for protecting smart satellite-based healthcare networks

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