With the development of artificial intelligence algorithms like deep learning models and the successful applications in many different fields, further similar trails of deep learning technology have been made in cyber security area. It shows the preferable performance not only in academic security research but also in industry practices when dealing with part of cyber security issues by deep learning methods compared to those conventional rules. Especially for the malware detection and classification tasks, it saves generous time cost and promotes the accuracy for a total pipeline of malware detection system. In this paper, we construct special deep neural network, ie, MalDeepNet (TB-Malnet and IB-Malnet) for malware dynamic behavior classification tasks. Then we build the family clustering algorithm based on deep learning and fulfil related testing. Except that, we also design a novel malware prediction model which could detect the malware coming in future through the Mal Generative Adversarial Network (Mal-GAN) implementation. All those algorithms present fairly considerable value in related datasets afterwards.

翻译：随着深层学习模型等人工智能算法的开发以及许多不同领域的成功应用,在网络安全领域也取得了类似深层学习技术的足迹。它表明,与传统规则相比,通过深层学习方法处理部分网络安全问题不仅在学术安全研究方面,而且在行业做法方面表现较好。它节省了大量时间,提高了恶意软件检测系统总管道的准确性。在本文中,我们为恶意软件动态行为分类任务建造了特殊的深层神经网络(TB-Malnet 和 IB-Malnet ) 。然后,我们在深层学习和完成相关测试的基础上,建立了家庭组合算法。例外的是,我们还设计了一个新的恶意软件预测模型,可以探测未来通过马尔吉纳杜瓦里网络(Mal Genemental Adversarial Net) 实施的恶意软件。所有这些算法在随后的相关数据集中具有相当可观的价值。