Detection of Cache Pollution Attack Based on Ensemble Learning in ICN-Based VANET
Detection of Cache Pollution Attack Based on Ensemble Learning in ICN-Based VANET
复制标题
DOI：
10.1109/tdsc.2022.3196109
复制
发表时间：
2023-07
影响因子：
7.3
通讯作者：
Lin Yao;Zhaolong Zheng;X. Wang;Yujie Zeng;Guowei Wu
中科院分区：
文献类型：
--
作者：
Lin Yao;Zhaolong Zheng;X. Wang;Yujie Zeng;Guowei Wu
内容中心网络（Content Centric Network， CCN）可以通过扩展有效、可靠地支持内容分发，解决车辆自组网（Vehicle Ad hoc Network， VANET）的动态拓扑和间歇性连接导致的网络性能下降问题。然而，车载内容中心网络（VCCN）的网内缓存机制容易受到缓存污染攻击（CPA）的攻击，攻击者通过发布虚假请求，将不受欢迎的内容填满缓存空间。在CPA下，不可避免地降低了合法用户内容请求的缓存命中率，增加了内容检索延迟。因此，检测和减轻CPA至关重要。目前针对静态VCCN的解决方案不能直接应用于动态VCCN。在本文中，我们提出了一种基于混合异构多分类器集成学习的检测方案，其中CPA由多辆车的合作决定。在我们的方案中，每辆车可以建立或加入一个集群，该集群的头部与其他成员具有更多共同的位置、速度和方向运动属性。此外，簇头作为基础学习器负责训练自己的分类器，对请求和命中率进行相应的统计。具体来说，将单个分类器构建集成分类器的问题表述为一个线性优化问题，其目标是最小化检测CPA的错误率。集成学习的泛化能力可以对CPA进行非常准确的预测。通过比较，我们的检测方案在检测率、命中率、检索延迟等方面都优于现有的检测方案。此外，仿真结果表明，采用单一基学习算法的过拟合问题可以得到缓解。
Content Centric Network (CCN) can be extended to efficiently and reliably support content delivery and solve the network performance degradation caused by dynamic topology and intermittent connectivity of Vehicle Ad hoc NETwork (VANET). However, the in-network caching mechanism of Vehicular Content Centric Network (VCCN) is vulnerable against Cache Pollution Attack (CPA), where attackers aim to fill the buffer space with non-popular contents by releasing fake requests. Unavoidably, the cache hit ratio of content requests from legal users is degraded and the content retrieval latency is increased under CPA. Hence, it is critical to detect and mitigate CPA. The current solutions for static CCN cannot be directly applied into dynamic VCCN. In this article, we propose a detection scheme based on hybrid heterogeneous multi-classifier ensemble learning, where CPA is determined by the cooperation of multiple vehicles. In our scheme, each vehicle can build or join a cluster whose head possesses more common moving attributes of position, speed and direction with other members. Besides, the cluster head as a base learner is responsible for training its own classifier by making some relevant statistics on requests and hit ratio. Specifically, the problem of ensemble classifier making from the individual classifiers is formulated as a linear optimization problem, with the goal of minimizing the false ratio of detecting CPA. The generalization ability of ensemble learning can make very accurate predictions on CPA. By comparison, our detection scheme outperforms the existing schemes in terms of detection ratio, hit ratio, retrieval delay. Besides, simulations have proved that the overfitting problem of adopting a singe base learning algorithm can be alleviated in our scheme.