Mal-Detect: An intelligent visualization approach for malware detection

Falana, Olorunjube James; Sodiya, Adesina S.; Onashoga, Saidat Adebukola; Badmus, B

doi:10.1016/j.jksuci.2022.02.026

Cited by 27 publications

(16 citation statements)

References 16 publications

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“…In phase 1, the authors follow the approaches of Falana et al (2022) to convert binaries into an image. Let B 1 , B 2 , …, B n and M 1 , M 2 , …, M n be the benign and malware binaries set, respectively.…”

Section: Methodsmentioning

confidence: 99%

“… Bensaoud & Kalita (2022) employed Malimg dataset for evaluating the CNN model. In another study, Falana et al (2022) developed a technique to convert malware binaries into an image to support the process of malware classification. A slight variation in an image assists CNN models in identifying critical malware.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Image-based MD can overcome the challenges and limitations of the existing MD models by minimizing the data loss ( Falana et al, 2022 ). Feature engineering is one of the recent developments in image-based MD.…”

Section: Introductionmentioning

confidence: 99%

“…DL allows for conducting a more in-depth network data analysis and spotting anomalies. Recent studies reveal the crucial role of DL in processing complex images ( Falana et al, 2022 ). Visualizing malware as a coloured image gives the benefit of differentiating various components of the malware binary ( Naeem et al, 2020 ; Jian et al, 2021 ; Falana et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies reveal the crucial role of DL in processing complex images ( Falana et al, 2022 ). Visualizing malware as a coloured image gives the benefit of differentiating various components of the malware binary ( Naeem et al, 2020 ; Jian et al, 2021 ; Falana et al, 2022 ). Malware programmers typically modify a small section of the malware codes to develop a new mutant.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Artificial intelligence-driven malware detection framework for internet of things environment

Alsubai

Dutta²,

Alnajim

et al. 2023

PeerJ Computer Science

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The Internet of Things (IoT) environment demands a malware detection (MD) framework for protecting sensitive data from unauthorized access. The study intends to develop an image-based MD framework. The authors apply image conversion and enhancement techniques to convert malware binaries into RGB images. You only look once (Yolo V7) is employed for extracting the key features from the malware images. Harris Hawks optimization is used to optimize the DenseNet161 model to classify images into malware and benign. IoT malware and Virusshare datasets are utilized to evaluate the proposed framework’s performance. The outcome reveals that the proposed framework outperforms the current MD framework. The framework generates the outcome at an accuracy and F1-score of 98.65 and 98.5 and 97.3 and 96.63 for IoT malware and Virusshare datasets, respectively. In addition, it achieves an area under the receiver operating characteristics and the precision-recall curve of 0.98 and 0.85 and 0.97 and 0.84 for IoT malware and Virusshare datasets, accordingly. The study’s outcome reveals that the proposed framework can be deployed in the IoT environment to protect the resources.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Artificial intelligence-driven malware detection framework for internet of things environment

Alsubai

Dutta²,

Alnajim

et al. 2023

PeerJ Computer Science

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An Efficient Deep Learning Framework for Malware Image Classification Using Gray-Level Co-Occurrence Matrix and Sparse Convolution

Priya,

Sofia

2024

Iran J Sci Technol Trans Electr Eng

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Empowering Network Security through Advanced Analysis of Malware Samples: Leveraging System Metrics and Network Log Data for Informed Decision-Making

Alharbi,

Kashyap

2024

Int J Netw Distrib Comput

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In the never-ending battle against rising malware threats, cybersecurity professionals were constantly challenged by malware researchers. Businesses and institutions that have fallen prey to these threats that have suffered significant financial losses and enormous disruption to countless lives. As a result, security approaches have evolved to include preemptive measures such as the widespread use of HoneyPots. However, data-driven decision-making was required to improve the effectiveness of such approaches. Therefore, this paper describes a quantitative analysis that assesses various malware samples using system metrics and network log data. The goal is to properly visualise this information and analyse if it can aid in decision-making processes, ultimately leading to the construction of more robust and secure networks. To help with this research, a dashboard application was created that allows the installation of virtual machines, the configuration of virtual networks, and the collection of system metric data from outside sources. The findings of this paper can help greatly improve network security and stay ahead of threats in the cat-and-mouse game.

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Mal-Detect: An intelligent visualization approach for malware detection

Cited by 27 publications

References 16 publications

Artificial intelligence-driven malware detection framework for internet of things environment

Artificial intelligence-driven malware detection framework for internet of things environment

An Efficient Deep Learning Framework for Malware Image Classification Using Gray-Level Co-Occurrence Matrix and Sparse Convolution

Empowering Network Security through Advanced Analysis of Malware Samples: Leveraging System Metrics and Network Log Data for Informed Decision-Making

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