论文标题
Seqnet：用于自动恶意软件检测的有效神经网络
SeqNet: An Efficient Neural Network for Automatic Malware Detection
论文作者
论文摘要
恶意软件继续迅速发展，每天捕获超过450,000个新样本，这使手动恶意软件分析不切实际。但是，现有的深度学习检测模型需要手动功能工程或需要高计算开销来进行长时间的培训过程，这可能很费力地选择功能空间，并且很难重新训练以减轻模型衰老。因此，对检测器的关键要求是实现自动和有效的检测。在本文中，我们提出了一种称为Seqnet的轻型恶意软件检测模型，该模型可以在原始二进制文件上以低记忆进行高速训练。通过避免上下文混乱并减少语义损失，Seqnet在将参数数量减少到仅为136K时保持检测准确性。我们在实验中证明了我们方法的有效性以及Seqnet的低训练成本要求。此外，我们将数据集和代码公开以刺激进一步的学术研究。
Malware continues to evolve rapidly, and more than 450,000 new samples are captured every day, which makes manual malware analysis impractical. However, existing deep learning detection models need manual feature engineering or require high computational overhead for long training processes, which might be laborious to select feature space and difficult to retrain for mitigating model aging. Therefore, a crucial requirement for a detector is to realize automatic and efficient detection. In this paper, we propose a lightweight malware detection model called SeqNet which could be trained at high speed with low memory required on the raw binaries. By avoiding contextual confusion and reducing semantic loss, SeqNet maintains the detection accuracy when reducing the number of parameters to only 136K. We demonstrate the effectiveness of our methods and the low training cost requirement of SeqNet in our experiments. Besides, we make our datasets and codes public to stimulate further academic research.
