基于贝叶斯正则化神经网络的P2P僵尸网络实时的检测框架
Real-time Peer-to-Peer Botnet Detection Framework based on Bayesian Regularized Neural Network
摘要：
在过去的十年中，网络空间已经看到越来越多的攻击来自僵尸网络使用对等（P2P）架构。对等僵尸网络使用分散的命令和控制架构。此外，大量这样的僵尸网络已经存在，并且几乎每年都会发现新的版本-与它们的母机器人明显不同。在这项工作中，作者提出并实现了一种新的混合框架检测P2P僵尸网络在实时网络流量集成神经网络与贝叶斯正则化。贝叶斯正则化有助于实现更好的数据集泛化，从而能够检测僵尸网络活动，即使是那些从未用于训练神经网络的机器人。因此，这样的框架适合于在网络的实时流量中检测更新的和看不见的僵尸网络。这通过在检测模块看不到的测试数据上测试框架来验证（使用未经训练的僵尸网络数据集），作者成功检测到这种活动，准确率为99.2%。
英文摘要：
Over the past decade, the Cyberspace has seen an increasing number of attacks coming from botnets using the Peer-to-Peer (P2P) architecture. Peer-to-Peer botnets use a decentralized Command & Control architecture. Moreover, a large number of such botnets already exist, and newer versions- which significantly differ from their parent bot- are also discovered practically every year. In this work, the authors propose and implement a novel hybrid framework for detecting P2P botnets in live network traffic by integrating Neural Networks with Bayesian Regularization. Bayesian Regularization helps in achieving better generalization of the dataset, thereby enabling the detection of botnet activity even of those bots which were never used in training the Neural Network. Hence such a framework is suitable for detection of newer and unseen botnets in live traffic of a network. This was verified by testing the Framework on test data unseen to the Detection module (using untrained botnet dataset), and the authors were successful in detecting this activity with an accuracy of 99.2 %.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
