Multistage Signaling Game-Based Optimal Detection Strategies for Suppressing Malware Diffusion in Fog-Cloud-Based IoT Networks

Shen, Shigen; Huang, Longjun; Zhou, Haiping; Yu, Shui; Fan, En; Cao, Qiying

doi:10.1109/jiot.2018.2795549

Cited by 110 publications

(40 citation statements)

References 57 publications

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“…Future research could draw more attention to further automate current processes in waste management, smart lightening and pest controlling subverticals by reducing existing drawbacks since it has received the least research attention in the considered period. Fog computing, as an innovation with cross over any barrier between remote data centres and IoT devices, should be considered in future IoT analysis [63], [64], [65], [66], [67]. While IoT has solved many issues related to agriculture and farming there are limitations that we need to consider.…”

Section: Discussionmentioning

confidence: 99%

Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

Madushanki¹,

Halgamuge²,

Wirasagoda³

et al. 2019

IJACSA

157

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It is essential to increase the productivity of agricultural and farming processes to improve yields and costeffectiveness with new technology such as the Internet of Things (IoT). In particular, IoT can make agricultural and farming industry processes more efficient by reducing human intervention through automation. In this study, the aim to analyze recently developed IoT applications in the agriculture and farming industries to provide an overview of sensor data collections, technologies, and sub-verticals such as water management and crop management. In this review, data is extracted from 60 peer-reviewed scientific publications (2016-2018) with a focus on IoT sub-verticals and sensor data collection for measurements to make accurate decisions. Our results from the reported studies show water management is the highest subvertical (28.08%) followed by crop management (14.60%) then smart farming (10.11%). From the data collection, livestock management and irrigation management resulted in the same percentage (5.61%). In regard to sensor data collection, the highest result was for the measurement of environmental temperature (24.87%) and environmental humidity (19.79%). There are also some other sensor data regarding soil moisture (15.73%) and soil pH (7.61%). Research indicates that of the technologies used in IoT application development, Wi-Fi is the most frequently used (30.27%) followed by mobile technology (21.10%). As per our review of the research, we can conclude that the agricultural sector (76.1%) is researched considerably more than compared to the farming sector (23.8%). This study should be used as a reference for members of the agricultural industry to improve and develop the use of IoT to enhance agricultural production efficiencies. This study also provides recommendations for future research to include IoT systems' scalability, heterogeneity aspects, IoT system architecture, data analysis methods, size or scale of the observed land or agricultural domain, IoT security and threat solutions/protocols, operational technology, data storage, cloud platform, and power supplies.12 | P a g e www.ijacsa.thesai.orgThis review provides an overall picture of currently developed IoT applications in agriculture and farming between 2016 and 2018.

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Section: Discussionmentioning

confidence: 99%

Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

Madushanki¹,

Halgamuge²,

Wirasagoda³

et al. 2019

IJACSA

157

View full text Add to dashboard Cite

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“…This made it possible to design safety awareness incentives. Shen et al [20] addressed an intrusion detection system (IDS) based on cloud and fog computing to detect malware in IoT. For reducing the privacy of smart devices, they computed Perfect Bayesian Equilibrium of the game in theory and then they provided strategies for improving the probability that malware will be detected.…”

Section: Related Workmentioning

confidence: 99%

Dynamics of Complex Networks: Malware Propagation Modeling and Analysis in Industrial Internet of Things

Cui

Zhang

et al. 2020

IEEE Access

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While cyber physics system (CPS) provides forward-looking and personalized services for users, it also creates conditions for the spread of malware. Therefore, it's critical for us to analyse affecting factors and study the propagation characteristics of malware. On the basis of the difference of intelligent device's dissemination capacity and discriminant ability, a dynamic malware propagation model (Disseminate&Discriminate-Spread-Exposed-Ignorant-Recover, DDSEIR) is proposed. First, intelligent devices are classified into different groups according to the level of dissemination capability by hierarchical mechanism, which takes the networks topology into account. And subsequently, intelligent device's discriminant ability can be evaluated by sender's identity and information attributes. Then, the mean field equation is constructed to analyze the dynamic characteristics of DDSEIR and the factors that influence malware propagation, which is used to further derive the malware propagation scale and threshold value of diffusion. Finally, this paper uses Live Journal dataset to verify the effectiveness of DDSEIR. The experiments illustrate that intelligent device's dissemination capacity and discriminant ability have significant influences on malware propagation.

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“…The third and fourth stages involve the IoT network, including the attack paths, which are visualized (i.e., security visualization) and analyzed (i.e., security analysis), respectively. In [25], the authors proposed a method to detect and restrain malware diffusion in an IoT network. The solution is based on fog computing, which can simultaneously maximize malware detection and minimize the possibility of privacy breach.…”

Section: Work In Iot Securitymentioning

confidence: 99%

“…The solution is based on fog computing, which can simultaneously maximize malware detection and minimize the possibility of privacy breach. The proposed malware detection system was constructed using an IDS, and the deployment was accomplished with cloud and fog computing to avoid the restrictions on IDS deployment in smart objects [25]. Furthermore, a framework was proposed to show the possible application of malware diffusion restriction in IoT networks.…”

Section: Work In Iot Securitymentioning

confidence: 99%

A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing

et al. 2019

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Fog computing is a paradigm that extends cloud computing and services to the edge of the network in order to address the inherent problems of the cloud, such as latency and lack of mobility support and location-awareness. The fog is a decentralized platform capable of operating and processing data locally and can be installed in heterogeneous hardware which makes it ideal for Internet of Things (IoT) applications. Intrusion Detection Systems (IDSs) are an integral part of any security system for fog and IoT networks to ensure the quality of service. Due to the resource limitations of fog and IoT devices, lightweight IDS is highly desirable. In this paper, we present a lightweight IDS based on a vector space representation using a Multilayer Perceptron (MLP) model. We evaluated the presented IDS against the Australian Defense Force Academy Linux Dataset (ADFA-LD) and Australian Defense Force Academy Windows Dataset (ADFA-WD), which are new generation system calls datasets that contain exploits and attacks on various applications. The simulation shows that by using a single hidden layer and a small number of nodes, we are able to achieve a 94% Accuracy, 95% Recall, and 92% F1-Measure in ADFA-LD and 74% Accuracy, 74% Recall, and 74% F1-Measure in ADFA-WD. The performance is evaluated using a Raspberry Pi.

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Multistage Signaling Game-Based Optimal Detection Strategies for Suppressing Malware Diffusion in Fog-Cloud-Based IoT Networks

Cited by 110 publications

References 57 publications

Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

Dynamics of Complex Networks: Malware Propagation Modeling and Analysis in Industrial Internet of Things

A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing

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