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Using Auxiliary Inputs in Deep Learning Models for Detecting DGA-based Domain Names

机译：在深学习模型中使用辅助输入来检测基于DGA的域名

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摘要

Command-and-Control (C&C) servers use Domain Generation Algorithms (DGAs) to communicate with bots for uploading malware and coordinating attacks. Manual detection methods and sinkholing fail to work against these algorithms, which can generate thousands of domain names within a short period. This creates a need for an automated and intelligent system that can detect such malicious domains. LSTM (Long Short Term Memory) is one of the most popularly used deep learning architectures for DGA detection, but it performs poorly against Dictionary Domain Generation Algorithms. This work explores the application of various machine learning techniques to this problem, including specialized approaches such as Auxiliary Loss Optimization for Hypothesis Augmentation (ALOHA), with a particular focus on their performance against Dictionary Domain Generation Algorithms. The ALOHA-LSTM model improves the accuracy of Dictionary Domain Generation Algorithms compared to the state of the art LSTM model. Improvements were observed in the case of word-based DGAs as well. Addressing this issue is of paramount importance, as they are used extensively in carrying out Distributed Denial-of-Service (DDoS) attacks. DDoS and its variants comprise one of the most significant and damaging cyber-attacks that have been carried out in the past.
机译：命令和控制（C＆C）服务器使用域生成算法（DGA）与用于上传恶意软件和协调攻击的机器人通信。手动检测方法和insholing无法对这些算法工作，这可以在短时间内生成数千个域名。这需要一种可以检测此类恶意域的自动和智能系统。 LSTM（长期内存）是DGA检测最普遍使用的深度学习架构之一，但它对字典域生成算法表现不佳。这项工作探讨了各种机器学习技术对该问题的应用，包括特殊方法，例如假设增强（Aloha）的辅助损耗优化，特别侧重于其对字典域生成算法的性能。与现有技术LSTM模型相比，Aloha-LSTM模型提高了字典域生成算法的准确性。在基于词的DGAS的情况下，观察到改进。解决此问题的重要性是至关重要的，因为它们是广泛用于执行分布式拒绝服务（DDOS）攻击。 DDOS及其变体包括过去已经进行的最重要和最有害的网络攻击之一。

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