Intelligent and behavioral-based detection of malware in IoT spectrum sensors

Celdrán, Alberto Huertas; Sánchez, Pedro Miguel Sánchez; Castillo, Miguel Azorín; Bovet, Gérôme; Pérez, Gregorio Martínez; Stiller, Burkhard

doi:10.1007/s10207-022-00602-w

Cited by 31 publications

(15 citation statements)

References 27 publications

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“…Behavioral analysis is a crucial approach in malware analysis and detection, focusing on how malware behaves when executed in a controlled environment [56][57][58][59]. This technique observes the dynamic actions and interactions of malware with the host system and network, allowing for the detection of malicious behavior that may not be evident through static analysis alone.…”

Section: Behavioral Analysis Toolsmentioning

confidence: 99%

Deep learning-powered malware detection in cyberspace: a contemporary review

Redhu,

Choudhary,

Srinivasan

et al. 2024

Front. Phys.

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This article explores deep learning models in the field of malware detection in cyberspace, aiming to provide insights into their relevance and contributions. The primary objective of the study is to investigate the practical applications and effectiveness of deep learning models in detecting malware. By carefully analyzing the characteristics of malware samples, these models gain the ability to accurately categorize them into distinct families or types, enabling security researchers to swiftly identify and counter emerging threats. The PRISMA 2020 guidelines were used for paper selection and the time range of review study is January 2015 to Dec 2023. In the review, various deep learning models such as Recurrent Neural Networks, Deep Autoencoders, LSTM, Deep Neural Networks, Deep Belief Networks, Deep Convolutional Neural Networks, Deep Generative Models, Deep Boltzmann Machines, Deep Reinforcement Learning, Extreme Learning Machine, and others are thoroughly evaluated. It highlights their individual strengths and real-world applications in the domain of malware detection in cyberspace. The review also emphasizes that deep learning algorithms consistently demonstrate exceptional performance, exhibiting high accuracy and low false positive rates in real-world scenarios. Thus, this article aims to contribute to a better understanding of the capabilities and potential of deep learning models in enhancing cybersecurity efforts.

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Section: Behavioral Analysis Toolsmentioning

confidence: 99%

Deep learning-powered malware detection in cyberspace: a contemporary review

Redhu,

Choudhary,

Srinivasan

et al. 2024

Front. Phys.

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“…Celdrán and Moon, in their respective works [20,21], present an evaluation of the impact of various techniques such as hash-coded string extraction, file format analysis, file fingerprinting, packer detection, and disassembly on the efficacy of static and dynamic analysis. The primary objective of this analysis was to yield two critical advantages.…”

Section: Literature Reviewmentioning

confidence: 99%

Securing IoT Devices Running PureOS from Ransomware Attacks: Leveraging Hybrid Machine Learning Techniques

et al. 2023

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Internet-enabled (IoT) devices are typically small, low-powered devices used for sensing and computing that enable remote monitoring and control of various environments through the Internet. Despite their usefulness in achieving a more connected cyber-physical world, these devices are vulnerable to ransomware attacks due to their limited resources and connectivity. To combat these threats, machine learning (ML) can be leveraged to identify and prevent ransomware attacks on IoT devices before they can cause significant damage. In this research paper, we explore the use of ML techniques to enhance ransomware defense in IoT devices running on the PureOS operating system. We have developed a ransomware detection framework using machine learning, which combines the XGBoost and ElasticNet algorithms in a hybrid approach. The design and implementation of our framework are based on the evaluation of various existing machine learning techniques. Our approach was tested using a dataset of real-world ransomware attacks on IoT devices and achieved high accuracy (90%) and low false-positive rates, demonstrating its effectiveness in detecting and preventing ransomware attacks on IoT devices running PureOS.

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“…Using similar hash mapping techniques, we reviewed and analysed tra c from additional les and classi ed it as benign or harmful. Hash buckets are collections of les with comparable hash values [19]. If a le is classed within a pure malware bucket area, we identify it as malware.…”

Section: Classi Cation and Predictionmentioning

confidence: 99%

Automated Malware Analysis in Internet of Things based Systems: A Deep Learning Approach

Khapre

Ganeshan

2022

Preprint

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Numerous potentials are presented by the Internet of Things, but there are a number of drawbacks as well. IoT devices have recently been more frequently the subject of malware assaults. Deep Learning is a popular technique that is used to identify and classify viruses. Researchers are working to strengthen the security of gadgets that are connected to the Internet in this respect. This approach used the behaviour of malware during run-time in the context of system calls to identify it. The real-time IoT malware samples were given by IOTPOT, a honeypot that replicates a variety of IoT device CPU architectures. From the malicious system calls that are generated, a deep learning algorithm extracts the necessary characteristics. To better understand malware activity, RGB photos were transformed and behavioural data was used to depict the samples. The retrieved system calls were divided into two groups—normal and malicious sequences—using VGG-19 (Visual Geometry Group – 19). The two classes were then assigned to each of the 15 subclasses of malware. The model is made lightweight and computationally efficient utilising a two-step feature extraction method that uses complete vector features for classification and lightweight dynamic features for weighting. The efficiency of deep learning is assessed using a range of performance criteria. In comparison to previously developed approaches, we were able to achieve an average classification accuracy of 97.75%, an increase of 3.7%.

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Intelligent and behavioral-based detection of malware in IoT spectrum sensors

Cited by 31 publications

References 27 publications

Deep learning-powered malware detection in cyberspace: a contemporary review

Deep learning-powered malware detection in cyberspace: a contemporary review

Securing IoT Devices Running PureOS from Ransomware Attacks: Leveraging Hybrid Machine Learning Techniques

Automated Malware Analysis in Internet of Things based Systems: A Deep Learning Approach

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