A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system

Gaurav, Akshat; Gupta, Brij B.; Panigrahi, Prabin Kumar

doi:10.1080/17517575.2021.2023764

Cited by 102 publications

(45 citation statements)

References 130 publications

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“…Smart mobile IoT architecture along with different security mechanisms was presented in [ 28 ]. On the other hand, machine learning based intrusion detection solutions were demonstrated in [ 27 , 43 , 44 ]. However, none of them reviewed the IoT attacks taxonomy, attack surfaces, security mechanisms, secure data communication method, etc., as we did in this research.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things: Security and Solutions Survey

Sadhu

Yanambaka

Abdelgawad

2022

Sensors

110

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The overwhelming acceptance and growing need for Internet of Things (IoT) products in each aspect of everyday living is creating a promising prospect for the involvement of humans, data, and procedures. The vast areas create opportunities from home to industry to make an automated lifecycle. Human life is involved in enormous applications such as intelligent transportation, intelligent healthcare, smart grid, smart city, etc. A thriving surface is created that can affect society, the economy, the environment, politics, and health through diverse security threats. Generally, IoT devices are susceptible to security breaches, and the development of industrial systems could pose devastating security vulnerabilities. To build a reliable security shield, the challenges encountered must be embraced. Therefore, this survey paper is primarily aimed to assist researchers by classifying attacks/vulnerabilities based on objects. The method of attacks and relevant countermeasures are provided for each kind of attack in this work. Case studies of the most important applications of the IoT are highlighted concerning security solutions. The survey of security solutions is not limited to traditional secret key-based cryptographic solutions, moreover physical unclonable functions (PUF)-based solutions and blockchain are illustrated. The pros and cons of each security solution are also discussed here. Furthermore, challenges and recommendations are presented in this work.

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Section: Related Workmentioning

confidence: 99%

Internet of Things: Security and Solutions Survey

Sadhu

Yanambaka

Abdelgawad

2022

Sensors

110

View full text Add to dashboard Cite

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“…The most prevalent solutions are rule-based, logic-based, ontology-based, supervised, unsupervised, and reinforcement algorithms to improve performance [222]. It is possible to utilize a combination of hybrid machine learning approaches, such as rule-based and ensemble-based algorithms, to provide a better management framework and more advanced reasoning capabilities to address the management challenges [223]. Connecting each IoT device to a power source is not always possible.…”

Section: A Network Function Virtualizationmentioning

confidence: 99%

Toward Software-Defined Networking-Based IoT Frameworks: A Systematic Literature Review, Taxonomy, Open Challenges and Prospects

et al. 2022

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Internet of Things (IoT) is characterized as one of the leading actors for the next evolutionary stage in the computing world. IoT-based applications have already produced a plethora of novel services and are improving the living standard by enabling innovative and smart solutions. However, along with its rapid adoption, IoT technology also creates complex challenges regarding the management of IoT networks due to its resource limitations (computational power, energy, and security). Hence, it is urgently needed to refine the IoT-based application's architectures to robustly manage the overall IoT infrastructure. Software-defined networking (SDN) has emerged as a paradigm that offers software-based controllers to manage hardware infrastructure and traffic flow on a network effectively. SDN architecture has the potential to provide efficient and reliable IoT network management. This research provides a comprehensive survey investigating the published studies on SDN-based frameworks to address IoT management issues in the dimensions of fault tolerance, energy management, scalability, load balancing, and security service provisioning within the IoT networks. We conducted a Systematic Literature Review (SLR) on the research studies (published from 2010 to 2022) focusing on SDN-based IoT management frameworks. We provide an extensive discussion on various aspects of SDN-based IoT solutions and architectures. We elaborate a taxonomy of the existing SDN-based IoT frameworks and solutions by classifying them into categories such as network function virtualization, middleware, OpenFlow adaptation, and blockchain-based management. We present the research gaps by identifying and analyzing the key architectural requirements and management issues in IoT infrastructures. Finally, we highlight various challenges and a range of promising opportunities for future research to provide a roadmap for addressing the weaknesses and identifying the benefits from the potentials offered by SDN-based IoT solutions.

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“…Due to the widespread usage of Android OS-based smart devices (70% market share in the mobile OS industry), they have become a prime target for malware developers. As a result, the research community has expressed a significant interest in securing Android devices against malicious attacks 43 , 44 . Many researchers have demonstrated machine learning as the core element of Android malware detection.…”

Section: Related Workmentioning

confidence: 99%

AndroMalPack: enhancing the ML-based malware classification by detection and removal of repacked apps for Android systems

Rafiq

Aslam

Aleem

et al. 2022

Sci Rep

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Due to the widespread usage of Android smartphones in the present era, Android malware has become a grave security concern. The research community relies on publicly available datasets to keep pace with evolving malware. However, a plethora of apps in those datasets are mere clones of previously identified malware. The reason is that instead of creating novel versions, malware authors generally repack existing malicious applications to create malware clones with minimal effort and expense. This paper investigates three benchmark Android malware datasets to quantify repacked malware using package names-based similarity. We consider 5560 apps from the Drebin dataset, 24,533 apps from the AMD and 695,470 apps from the AndroZoo dataset for analysis. Our analysis reveals that 52.3% apps in Drebin, 29.8% apps in the AMD and 42.3% apps in the AndroZoo dataset are repacked malware. Furthermore, we present AndroMalPack, an Android malware detector trained on clones-free datasets and optimized using Nature-inspired algorithms. Although trained on a reduced version of datasets, AndroMalPack classifies novel and repacked malware with a remarkable detection accuracy of up to 98.2% and meagre false-positive rates. Finally, we publish a dataset of cloned apps in Drebin, AMD, and AndrooZoo to foster research in the repacked malware analysis domain.

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A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system

Cited by 102 publications

References 130 publications

Internet of Things: Security and Solutions Survey

Internet of Things: Security and Solutions Survey

Toward Software-Defined Networking-Based IoT Frameworks: A Systematic Literature Review, Taxonomy, Open Challenges and Prospects

AndroMalPack: enhancing the ML-based malware classification by detection and removal of repacked apps for Android systems

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