Abstract
The malware booming is a cyberspace equal to the effect of climate change to ecosystems in terms of danger. In the case of significant investments in cybersecurity technologies and staff training, the global community has become locked up in the eternal war with cyber security threats. The multi-form and changing faces of malware are continuously pushing the boundaries of the cybersecurity practitioners employ various approaches like detection and mitigate in coping with this issue. Some old mannerisms like signature-based detection and behavioral analysis are slow to adapt to the speedy evolution of malware types. Consequently, this paper proposes the utilization of the Deep Learning Model, LSTM networks, and GANs to amplify malware detection accuracy and speed. A fast-growing, state-of-the-art technology that leverages raw bytestream-based data and deep learning architectures, the AI technology provides better accuracy and performance than the traditional methods. Integration of LSTM and GAN model is the technique that is used for the synthetic generation of data, leading to the expansion of the training datasets, and as a result, the detection accuracy is improved. The paper uses the VirusShare dataset which has more than one million unique samples of the malware as the training and evaluation set for the presented models. Through thorough data preparation including tokenization, augmentation, as well as model training, the LSTM and GAN models convey the better performance in the tasks compared to straight classifiers. The research outcomes come out with 98% accuracy that shows the efficiency of deep learning plays a decisive role in proactive cybersecurity defense. Aside from that, the paper studies the output of ensemble learning and model fusion methods as a way to reduce biases and lift model complexity.
Abstract (translated)
恶意软件爆炸是一种与气候变化对生态系统的影响相等的网络空间。在大量投入网络安全技术和员工培训的情况下，全球社区已经陷入了与网络安全威胁的永恒战争中。恶意软件的多形态和不断变化的面孔不断推动网络安全实践者采用各种检测和减轻方法应对这一问题。一些老方法如基于签名的检测和行为分析在应对恶意软件类型快速演变方面较慢。因此，本文提出了使用深度学习模型、LSTM网络和GANs来提高恶意软件检测精度和速度。一种利用原始字节流数据和深度学习架构快速生长的最先进技术，人工智能技术提供了比传统方法更好的准确性和性能。LSTM和GAN模型的集成是用于数据合成技术的方法，导致训练数据集的扩展，从而提高了检测精度。本文使用VirusShare数据集，该数据集有超过一百万个独特的恶意软件样本作为训练和评估集，通过包括分词、增强以及模型训练等彻底的数据准备，LSTM和GAN模型在任务表现上比直接分类器更好。通过研究结果呈现了98%的准确度，这表明在主动网络安全防御中，深度学习的有效性具有决定性的作用。此外，本文研究了集成学习方法和模型融合方法的结果，以减少偏见和提高模型复杂性。
URL
https://arxiv.org/abs/2405.04373
