Cyber-crimes have become a multi-billion-dollar industry in the recent years. Most cybercrimes/attacks involve deploying some type of malware. Malware that viciously targets every industry, every sector, every enterprise and even individuals has shown its capabilities to take entire business organizations offline and cause significant financial damage in billions of dollars annually. Malware authors are constantly evolving in their attack strategies and sophistication and are developing malware that is difficult to detect and can lay dormant in the background for quite some time in order to evade security controls. Given the above argument, Traditional approaches to malware detection are no longer effective. As a result, deep learning models have become an emerging trend to detect and classify malware. This paper proposes a new convolutional deep learning neural network to accurately and effectively detect malware with high precision. This paper is different than most other papers in the literature in that it uses an expert data science approach by developing a convolutional neural network from scratch to establish a baseline of the performance model first, explores and implements an improvement model from the baseline model, and finally it evaluates the performance of the final model. The baseline model initially achieves 98% accurate rate but after increasing the depth of the CNN model, its accuracy reaches 99.183 which outperforms most of the CNN models in the literature. Finally, to further solidify the effectiveness of this CNN model, we use the improved model to make predictions on new malware samples within our dataset.

翻译：网络犯罪近年来已成为一个价值数十亿美元的产业。大多数网络犯罪/攻击都涉及部署某种类型的恶意软件。恶意软件恶意针对各个行业、领域、企业乃至个人，已展现出其能够使整个企业组织离线，并造成每年数十亿美元的巨额财务损失。恶意软件作者不断演变其攻击策略和复杂程度，开发难以检测、可在后台潜伏相当长一段时间以规避安全控制的恶意软件。鉴于上述论点，传统的恶意软件检测方法已不再有效。因此，深度学习模型已成为检测和分类恶意软件的新兴趋势。本文提出了一种新的卷积深度学习神经网络，以高精度准确有效地检测恶意软件。本文与文献中大多数其他论文的不同之处在于，它采用了一种专业的数据科学方法：首先从头开发一个卷积神经网络来建立性能模型的基线，然后从基线模型探索并实现一个改进模型，最后评估最终模型的性能。基线模型最初达到了98%的准确率，但在增加CNN模型的深度后，其准确率达到了99.183%，优于文献中的大多数CNN模型。最后，为进一步巩固该CNN模型的效力，我们使用改进后的模型对数据集中的新恶意软件样本进行了预测。