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.

该研究使用深度学习模型、LSTM 网络和 GAN 来提高恶意软件检测的准确性和速度，通过 VirusShare 数据集的训练和评估，表明深度学习在主动网络安全防御中发挥着决定性的作用。此外，研究还探讨了集成学习和模型融合方法对减少偏差和提高模型复杂性的影响。