Using honeypots to model botnet attacks on the internet of medical things

Wang, Huanran; He, Hui; Zhang, Weizhe; Wen-mao, Liu; Liu, Peng; Javadpour, Amir

doi:10.1016/j.compeleceng.2022.108212

Cited by 15 publications

(4 citation statements)

References 8 publications

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“…Valence refers to the combining power of an image, especially as measured by the number of atomic regions that can displace or combine with other regions; possible vulnerability is potential for image breaches which properly expose private image data of responsible persons to undesirable doers; virtualization tries to create a virtual representation of real-world entities or processes; visualization shows image through visualization tools in challenging due image volume and considerable variety and sufficient velocity; vicinity expresses the main area or region of a valuable image is free, available, or unoccupied; vitality indicates the state of being a strong and active state of the image area for processing or growth; social value remains comfortably an intellectual ability to typically produce helpful image results like better insights; and key decisions resulting in improved customer service, better products, more considerable revenue, reduced cost or managed risk. Also, a new approach binding distributed local data processing due to using local agents and intelligence negotiations in an agent-based model or global knowledge extraction by transferring intelligence bots in a bot-oriented model for IoMT in smart cities (SC) is a critical decision [30].…”

Section: Big Image Data Dimensionsmentioning

confidence: 99%

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Mahabadi,

Abdolazimi

2024

Preprint

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The classification of large image data using traditional methods has been based on changes in image gray features, extraction of edge and contour feature information, or conversion between image coordinate sets. However, due to the growth of new image size and big image data (BID) in real-time multimedia communication and future online big data (BD) applications, these methods have become increasingly complex and resulting in poor real-time performance. These complex methods suffer from complex algorithms, massive data communication, slow processing speed, unintelligent predictive modeling, weak data classification, limited accuracy, uncleaned usage data, combined destructive artificial and natural noise, exchanging data values over time, and exhausting updating data for accurate operation of media storage. In the era of the Internet of Multimedia Things (IoMT), most modern devices can accurately capture vast amounts of observational data in the form of valuable images, text, and acoustic recordings. This reliable data must be kept valid for local data processing at different times and for globally extracting knowledge from information at various data levels to address forthcoming challenges related to big image data. The challenges of secure communication require accurate, reliable, fast, and effective information gathering for local decision-making and global knowledge mining of BID to support cyber-physical systems for speedy data virtualization of smart city devices. Complex and uncontrollable problems persist on the outskirts of grids, fogs, and cloud networks regarding data cleansing and privacy protection for intelligent data ocean management. This study developed a new 15Vs model empirically to examine distributed big image data processing based on a modern BID connectivity approach. The model accurately extracts textured features from visible data images and overcomes a unique set of key challenges. The model introduces a strategic analyzer, uses intelligent local agents, and recruits clever global bots to provide a suitable platform for generous support of bot-oriented BD processing. This instantly forms a hierarchical data level for better data acquisition and cleansing, safe privacy protection, suitable information diffusion, and speedy knowledge extraction. Our study presents a novel definition of BID to address the ultimate challenges of data management in a high-risk environment for further big image data modeling research needs.

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Section: Big Image Data Dimensionsmentioning

confidence: 99%

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Mahabadi,

Abdolazimi

2024

Preprint

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“…A honeypot is a deceptive trap in the network that pretends to be a regular host. When the malicious nodes communicate with a honeypot, they are closely monitored and detected [20]. The behavior of the Proof and a honeypot is similar.…”

Section: Proposed Detection Strategiesmentioning

confidence: 99%

Cybersecurity Fusion: Leveraging Mafia Game Tactics and Reinforcement Learning for Botnet Detection

Javadpour,

Ja'fari,

Taleb

et al. 2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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Mafia, also known as Werewolf, is a game of uncertainty between two teams, which aims to eliminate the other team's players from the game. The similarities between detecting the Mafia members in this game and botnet detection in a computer network motivate us to solve the botnet detection problem using this game's winning strategies. None of the stateof-the-art researches have used the Mafia game strategies to detect the network's malicious nodes. In this paper, we first propose the Mafia detection strategies, which are applied using linear relation and reinforcement learning techniques. We then use the suggested strategies in a network infected by the Mirai botnet, using Mininet, to evaluate the performance of botnet detection. The average results show that the suggested strategies are 11% more accurate than the existing ones for the Mafia game. Additionally, the true positive and true negative detection rates of a network modeled by the proposed Mafia game are 71% and 91%, respectively.

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“…Wang et al [ 13 ] found a correlation between the pandemic timeline and the attacks generated among IoT networks. This work analyzed critical security problems of the IoT environment through honeypot principles.…”

Section: Introductionmentioning

confidence: 99%

Interleaved Honeypot-Framing Model with Secure MAC Policies for Wireless Sensor Networks

Rajasoundaran

Maheswar

Muthuramalingam

et al. 2022

Sensors

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The Wireless Medium Access Control (WMAC) protocol functions by handling various data frames in order to forward them to neighbor sensor nodes. Under this circumstance, WMAC policies need secure data communication rules and intrusion detection procedures to safeguard the data from attackers. The existing secure Medium Access Control (MAC) policies provide expected and predictable practices against channel attackers. These security policies can be easily breached by any intelligent attacks or malicious actions. The proposed Wireless Interleaved Honeypot-Framing Model (WIHFM) newly implements distributed honeypot-based security mechanisms in each sensor node to act reactively against various attackers. The proposed WIHFM creates an optimal Wireless Sensor Network (WSN) channel model, Wireless Interleaved Honeypot Frames (WIHFs), secure hash-based random frame-interleaving principles, node-centric honeypot engines, and channel-covering techniques. Compared to various existing MAC security policies, the proposed model transforms unpredictable IHFs into legitimate frame sequences against channel attackers. Additionally, introducing WIHFs is a new-fangled approach for distributed WSNs. The successful development of the proposed WIHFM ensures resilient security standards and neighbor-based intrusion alert procedures for protecting MAC frames. Particularly, the proposed wireless honeypot methodology creates a novel idea of using honeypot frame traps against open wireless channel attacks. The development of a novel wireless honeypot traps deals with various challenges such as distributed honeypot management principles (node-centric honeypot, secretly interleaved-framing principles, and interleaving/de-interleaving procedures), dynamic network backbone management principles (On Demand Acyclic Connectivity model), and distributed attack isolation policies. This effort provides an effective wireless attack-trapping solution in dynamic WSNs. The simulation results show the advantage of the proposed WIHFM over the existing techniques such as Secure Zebra MAC (SZ-MAC), Blockchain-Assisted Secure-Routing Mechanism (BASR), and the Trust-Based Node Evaluation (TBNE) procedure. The experimental section confirms the proposed model attains a 10% to 14% superior performance compared to the existing techniques.

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Using honeypots to model botnet attacks on the internet of medical things

Cited by 15 publications

References 8 publications

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Cybersecurity Fusion: Leveraging Mafia Game Tactics and Reinforcement Learning for Botnet Detection

Interleaved Honeypot-Framing Model with Secure MAC Policies for Wireless Sensor Networks

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