An Intelligent Framework for Malware Detection in Internet of Things (IoT) Ecosystem

Eboya, Osaretin; Juremi, Julia; Shahpasand, Maryam

doi:10.1109/r10-htc49770.2020.9356961

Cited by 4 publications

(26 citation statements)

References 12 publications

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“…This paper is an extension of the work originally presented in IEEE 8th R10 Humanitarian Technology Conference (R10-HTC) 2020 [1]. The Internet of Things (IoT) has in recent times emerged as ubiquitous technology to everyday lives especially with the advent of the Industrial Revolution (IR) 4.0 in the digital world.…”

Section: Introductionmentioning

confidence: 96%

“…The IoT technologies comprises of smart devices and objects interconnected in a heavily computerized network environment that constitutes the backbone of modern innovative critical infrastructures that supports fast-paced technological driven world. The IoT technologies has been disruptive in many industries such as: (1) IoT in Healthcare, (2) IoT in Manufacturing, (3) IoT in Transportation and Mobility, (4) IoT in Buildings, (5) IoT in Cities, (6) IoT in Agriculture, (7) IoT in Energy, (8) IoT in Retail and Marketing, (9) IoT in Logistics and Supply Chain, and (10) IoT in Industries across various sectors of the economy. Similarly, IoT technologies has brought diverse approaches in solving mundane tasks in these sectors by leveraging and amalgamating innovative and intelligent automation systems.…”

Section: Introductionmentioning

confidence: 99%

“…Particularly in the bountiful cyberattacks and cyber threats warfare in the modern cyber space clearly indicates the fragility and exposure of current interconnected critical infrastructures and its related network systems. This has essentially led to the theory that detection is a better approach to combating cyberattacks and cyber threats in a smart ecosystem of massively interconnected computer network systemsmalware detection [1]. Malwares are typically spread over an interconnected computer network system otherwise known as the internet, which is the core backbone of an IoT ecosystem.…”

Section: Introductionmentioning

confidence: 99%

“…The IoT ecosystem can be attacked, infected, and breached by different types of malicious software commonly known as malwares with categorized malware families such as: (a) Ransomwares, (b) Spywares, (c) Scareware, (d) Adware, (e) Viruses, (f) Worms, (g) Rootkits, (h) Botnets, (i) Trojan Horses that all threatens the integrity, privacy, and security of big data in a heavily computerized computer network system like an IoT ecosystemtampering of valuable assets; big dataset. One of the major problems [1] with these malware families and their nefarious activities combined with innovatory technologies is that they have become even harder to detect with traditional and automated malware detection tools and techniques in a heavily computerized network systempolymorphic malwares. If and when potent malwares go undetected in a network system such as an IoT ecosystem, this can cause massive damages and disruptions to critical computer network infrastructures [1], loss of personal information, and potentially loss of livelihood and lives in a modern technological-reliant societyobfuscation and evasion of malware detection.…”

Section: Introductionmentioning

confidence: 99%

“…One of the major problems [1] with these malware families and their nefarious activities combined with innovatory technologies is that they have become even harder to detect with traditional and automated malware detection tools and techniques in a heavily computerized network systempolymorphic malwares. If and when potent malwares go undetected in a network system such as an IoT ecosystem, this can cause massive damages and disruptions to critical computer network infrastructures [1], loss of personal information, and potentially loss of livelihood and lives in a modern technological-reliant societyobfuscation and evasion of malware detection.…”

Section: Introductionmentioning

confidence: 99%

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iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem

Eboya¹,

Juremi²

2021

Adv. sci. technol. eng. syst. j.

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The Internet of Things (IoT) is at a face paced growth in the advanced Industrial Revolution (IR) 4.0 in the modern digital world. Considering the current network security challenges and sophistication of attacks in the heavily computerized and interconnected systems, such as an IoT ecosystem, the need for an innovative, robust, intelligent and adaptive malware attacks and threats security solution is becoming predominant in the current cyberspace. An integrated and scalable IoT malware detection framework called iDRP framework with deep learning method was proposed as a solution to current IoT malware attacks that are largely obfuscated. The novel framework utilized systematic pre-processing and post-processing techniques and methods on the BoTNetIoT malware datasets that contains both benign and malicious IoT traffic data infected by modern day IoT attacks such as Mirai and Gafgyt etc. IoT malware variants in an IoT ecosystem. The raw IoT malware binaries were converted to image files (Gray-scaled) and computed statistically with synthesised sparsed and differential evolutionary hidden feature structures techniques, which were cyclically trained, tested, and cross-validated to establish empirical anomalies with precision in the detection, recognizing, and prediction of malware anomalies in a modern IoT ecosystem. Preliminary experiments were conducted with standardized image binary files such as the datasets as sound scientific exploratory experiments with profound results. The comparative results of the performance of our integrated techniques and methods on the BoTNetIoT IoT malware datasets achieved a 99.98% accuracy, 99.99% ROC/AUC, 99.95% precision, and 99.93 recall rate etc. utilizing the integrated iDRP framework mechanisms for effectively detecting IoT malware in an IoT ecosystem.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem

Eboya¹,

Juremi²

2021

Adv. sci. technol. eng. syst. j.

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Quantum Mayfly Optimization with Encoder-Decoder Driven LSTM Networks for Malware Detection and Classification Model

Alzubi

et al. 2023

Mobile Netw Appl

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Towards Secure IoT-Based Payments by Extension of Payment Card Industry Data Security Standard (PCI DSS)

Bhutta

Bhatia

Alojail

et al. 2022

Wireless Communications and Mobile Computing

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IoT emergence has given rise to a new digital experience of payment transactions where physical objects like refrigerators, cars, and wearables will make payments. These physical objects will be storing the cardholder credentials and will directly make payments with the vendors over insecure public networks. For such payment transactions, government regulations and standards organizations require to implement PCI DSS for adapting similar set of security measures at the global level. The current version of PCI DSS is not suitable for IoT-based payment systems due to characteristics of IoT such as resource-constrained nature of devices and updating software/firmware of so many physical devices. Also, there arises an emergent need of implementing PCI DSS requirements and assessments for security of all stakeholders that store or process the user credentials in a payment. This paper is an initial effort to bring the researcher’s attention to make upcoming versions of PCI DSS suitable for IoT and thus securing the new ways of IoT-based payment systems. The paper has reviewed the traditional payment process along with considerations for IoT-based payment systems to make recommendations to modify the PCI DSS in a suitable way for IoT.

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An Intelligent Framework for Malware Detection in Internet of Things (IoT) Ecosystem

Cited by 4 publications

References 12 publications

iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem

iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem

Quantum Mayfly Optimization with Encoder-Decoder Driven LSTM Networks for Malware Detection and Classification Model

Towards Secure IoT-Based Payments by Extension of Payment Card Industry Data Security Standard (PCI DSS)

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