Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Malware classification

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    Light-Weight Deep Learning Models for Visual Malware Classification
    (Springer Science and Business Media Deutschland GmbH, 2023) Akshay Kumar, E.; Ramalingam, J.
    Malware attacks are on the rise every day in the Internet-based digital world. Regular Internet users are at risk due to the evolution of new infections. In recent years, the use of machine learning algorithms to identify malware has gained popularity because numerous studies have demonstrated its efficacy. This work provides two deep learning models to categorize the malware turned into images. Our method uses fewer resources and takes less time to accomplish the same performance as state-of-the-art results. The primary advantage of malware images is that no additional feature engineering is required. Our models for categorizing image-based malware are less complex and can be used in computational systems with limited computational capabilities, such as Android devices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Malware Classification Using XGBoost and Genetic Algorithm for Hyperparameter Tuning
    (Institute of Electrical and Electronics Engineers Inc., 2024) Divakarla, U.; Chandrasekaran, K.; Harish, S.V.; Kanal, P.G.; Shalini, C.
    All human activities are being moved into the virtual world due to technological advancements. Since so much of our data is stored on computers and networks, the frequency of cyberattacks has sharply increased. Understanding the many types of malware, their danger level, defense strategies, and potential methods of infecting computers and other devices requires the ability to identify and classify them. In this research, we propose a malware categorization model. Our proposed model is based on XGBoost and uses a Genetic Algorithm for hyperparameter tuning. The system achieved high accuracy with the help of two different malware datasets used for testing and training: Malevis and Malimg. © 2024 IEEE.