IoT-Scan: Network Reconnaissance for the Internet of Things

Gvozdenovic, Stefan; Becker, Johannes K; Mikulskis, John; Starobinski, David

doi:10.48550/arxiv.2204.02538

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…A network discovery attack is a type of cyber/Internet attack in which an attacker sends probe packets to a target network or system in order to gather information about the system's configuration, vulnerabilities, or available services. Network discovery attacks can be used to gather intelligence for more sophisticated attacks, such as DoS attacks or network intrusions [29]. Network discovery attacks can take many forms, including ping sweeps, port scans, and banner grabbing.…”

Section: Network Discovery/reconnaissance Attackmentioning

confidence: 99%

“…Network discovery attacks can be challenging to detect as the probe packets may not contain malicious payloads or raise alarms in traditional security systems. Therefore, it is important for organizations to implement a comprehensive security strategy that includes monitoring for probe packets and other indicators of potential network discovery activity [29].…”

Section: Network Discovery/reconnaissance Attackmentioning

confidence: 99%

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Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms

2023

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The Internet of Things (IoT) shares the idea of an autonomous system responsible for transforming physical computational devices into smart ones. Contrarily, storing and operating information and maintaining its confidentiality and security is a concerning issue in the IoT. Throughout the whole operational process, considering transparency in its privacy, data protection, and disaster recovery, it needs state-of-the-art systems and methods to tackle the evolving environment. This research aims to improve the security of IoT devices by investigating the likelihood of network attacks utilizing ordinary device network data and attack network data acquired from similar statistics. To achieve this, IoT devices dedicated to smart healthcare systems were utilized, and botnet attacks were conducted on them for data generation. The collected data were then analyzed using statistical measures, such as the Pearson coefficient and entropy, to extract relevant features. Machine learning algorithms were implemented to categorize normal and attack traffic with data preprocessing techniques to increase accuracy. One of the most popular datasets, known as BoT-IoT, was cross-evaluated with the generated dataset for authentication of the generated dataset. The research provides insight into the architecture of IoT devices, the behavior of normal and attack networks on these devices, and the prospects of machine learning approaches to improve IoT device security. Overall, the study adds to the growing body of knowledge on IoT device security and emphasizes the significance of adopting sophisticated strategies for detecting and mitigating network attacks.

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Section: Network Discovery/reconnaissance Attackmentioning

confidence: 99%

Section: Network Discovery/reconnaissance Attackmentioning

confidence: 99%

Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms

2023

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“…They further analyzed the results of the search and stated how these results can be utilized as a tool for attacking an IoT device. In addition, Gvozdenovic et al [9] introduced IoT-Scan as an alternative to NMap for scanning IoT devices. The IoT-Scan is based on software-defined radio and is capable of performing passive, active, multichannel, and multiprotocol scanning.…”

Section: Related Workmentioning

confidence: 99%

A Multimodel-Based Approach for Estimating Cause of Scanning Failure and Delay in IoT Wireless Network

Ojetunde

Egashira

Suzuki

et al. 2022

Network

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The rapid growth in the IoT network comes with a huge security threat. Network scanning is considered necessary to identify vulnerable IoT devices connected to IP networks. However, most existing network scanning tools or system do not consider the burden of scan packet traffic on the network, especially in the IoT network where resources are limited. It is necessary to know the status of the communication environment and the reason why network scanning failed. Therefore, this paper proposes a multimodel-based approach which can be utilized to estimate the cause of failure/delay of network scanning over wireless networks where a scan packet or its response may sometimes be dropped or delayed. Specifically, the factors that cause network scanning failure/delay were identified and categorized. Then, using a machine learning algorithm, we introduced a multimodel linear discriminant analysis (MM-LDA) to estimate the cause of scan failure/delay based on the results of network scanning. In addition, a one-to-many model and a training data filtering technique were adopted to ensure that the estimation error was drastically reduced. The goal of our proposed method was to correctly estimate the causes of scan failure/delay in IP-connected devices. The performance of the proposed method was evaluated using computer simulation assuming a cellular (LTE) network as the targeted IoT wireless network and using LTE-connected devices as the targeted IoT devices. The proposed MM-LDA correctly estimates the cause of failure/delay of the network scan at an average probability of 98% in various scenarios. In comparison to other conventional machine learning classifiers, the proposed MM-LDA outperforms various classification methods in the estimation of the cause of scan failure/delay.

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IoT-Scan: Network Reconnaissance for the Internet of Things

Cited by 2 publications

References 13 publications

Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms

Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms

A Multimodel-Based Approach for Estimating Cause of Scanning Failure and Delay in IoT Wireless Network

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