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- W3016872525 abstract "AbstractBegun in 1988, malware detection continues to be a challenging research topic in this epoch of technology. The exponential rise of IoT devices and its consumers have parallelly increased the number of security breaches in recent times, posing a major security concern. Research studies in malware detection analysis have proved that both dynamic and static analyses are time-consuming, inefficient, and ineffective to detect novel malware signatures. The cybercriminals make use of evasive techniques like polymorphism and code obfuscation to alter the malware behavior rapidly and bypass malware detection. To countermeasure the cyber-attacks, Machine Learning Algorithms (MLAs) have come into the picture. The feature learning technique used by MLAs to detect novel malware signatures turns out to be time-consuming. To bypass the feature engineering phase, we introduce the deep learning methodologies such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). We made use of the binary malware datasets to train the algorithms, and once the malwares are detected they are classified and categorized into their respective malware families by means of deep image processing techniques. The results obtained in this paper showcase the brightside of the deep learning architectures by outperforming the machine learning algorithms.KeywordsMalware detectionDeep learningMachine learningCybercrimeImage processing" @default.
- W3016872525 created "2020-04-24" @default.
- W3016872525 creator A5031734027 @default.
- W3016872525 date "2021-01-01" @default.
- W3016872525 modified "2023-10-16" @default.
- W3016872525 title "A Hybrid Deep Learning Approach for Detecting Zero-Day Malware Attacks" @default.
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- W3016872525 doi "https://doi.org/10.1007/978-981-33-4046-6_20" @default.
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