摘要
近年来,工业互联网获得了飞速的发展.但是和传统互联网一样,工业互联网也面临着大量的网络攻击威胁和敏感信息泄露风险.而流量识别技术,特别是细粒度的应用动作识别技术,可以辅助网络管理者对异常行为进行检测和及早发现隐私泄露风险,保障工业互联网的安全.然而,现有动作识别技术依赖对流量数据中动作边界的预先分割,无法识别无边界的动作,难以应用于实际场景.为解决这一问题,提出一种无边界动作识别算法:首先构建基于自注意力机制的包级识别模型,对数据包进行动作分类;然后提出动作聚合算法,从数据包的分类结果中聚合出动作序列;最后,建立2种新指标来衡量识别结果的好坏.为验证算法的可行性,以微信为实例进行实验,结果表明该模型能够取得最高超过90%的序列识别精度.这一研究成果将有望极大推动应用动作识别技术的实用化.
In recent years,the industrial Internet has experienced a rapid development.However,like the traditional Internet,the industrial Internet also faces a large number of threats from cyber-attacks and sensitive information leakage risks.Traffic classification technology,especially fine-grained application action identification,can assist network managers in detecting abnormal behaviors and discovering privacy leakage risks.It provides the security of the industrial Internet.Whereas,the existing action identification technology relies on the pre-segmentation of the action bounds in the traffic.In this case,existing methods cannot identify actions without bounds,which are difficult to be used in real scenes.Therefore,an action identification algorithm without bounds is proposed.Firstly,we build a packet-level identification model based on self-attention mechanism to classify packets.Then we propose an action aggregation algorithm to acquire action sequence from the classification results of packets.Finally,we establish two new indicators to measure the quality of the identification result.To verify the feasibility of our algorithm,we take WeChat as an example to conduct experiments.The results show that the model can achieve a sequential precision of up to 90%.This research is expected to greatly promote the practical application of action identification technology.
作者
王冲
魏子令
陈曙晖
Wang Chong;Wei Ziling;Chen Shuhui(School of Computer,National University of Defense Technology,Changsha 410003)
出处
《计算机研究与发展》
EI
CSCD
北大核心
2022年第5期1092-1104,共13页
Journal of Computer Research and Development
基金
媒体融合内容感知与安全湖南省重点实验室项目
国家自然科学基金面上项目(61972412)
湖南省科技创新计划(2020RC2047)。
关键词
工业互联网
流量分类
动作识别
深度学习
自注意力
industrial Internet
traffic classification
action identification
deep learning
self-attention
