Journal of Information Security Reserach ›› 2024, Vol. 10 ›› Issue (E2): 134-.

Online:2024-11-22 Published:2024-11-23

基于LSTM的充电桩恶意流量入侵检测

卢轩1,2吴建华1,2龚一轩3施天宇1,2

1(上海市计算机软件评测重点实验室上海201112)
2(上海计算机软件技术开发中心上海201112)
3(上海科技大学上海201210)

通讯作者: 卢轩主要研究方向为网络安全. lx@sscenter.sh.cn
作者简介:卢轩主要研究方向为网络安全. lx@sscenter.sh.cn 吴建华硕士.主要研究方向为信息安全、测试技术、软件工程质量保障. wjh@sscenter.sh.cn 龚一轩硕士，高级工程师.主要研究方向为网络信息安全、人工智能安全. gongyx@shanghaitech.edu.cn 施天宇主要研究方向为密码安全. sty@sscenter.sh.cn

Abstract

摘要： 随着我国新能源汽车数量的增多，汽车充电桩数量的增长速度也逐步加快.当前大量汽车充电桩存在防护能力弱、防御反应速度慢等特点，导致现有大量充电桩设备成为攻击者入侵的主要目标.入侵一旦得手，充电桩的可用性、新能源汽车用户数据的完整性和保密性将受到威胁，进而无法满足新能源汽车用户的充电需求，或造成新能源汽车用户数据泄露.为解决上述问题，提出充电桩入侵检测模型CPIDS，即根据公开的充电桩恶意流量数据构建真实有效的充电桩入侵检测数据集，利用LSTM进行训练，并采用数据增强机制解决数据类别不平衡问题.实验结果表明，与基准模型相比，CPIDS能够有效提升模型检测精度，并能自动快速地对流量数据样本进行检测，为充电桩入侵检测提供了高效检测的新思路.

关键词: 深度学习, 数据处理, 入侵检测, 特征提取, 充电桩安全

CLC Number:

TP391

卢轩, 吴建华, 龚一轩, 施天宇, . 基于LSTM的充电桩恶意流量入侵检测[J]. 信息安全研究, 2024, 10(E2): 134-.

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基于LSTM的充电桩恶意流量入侵检测

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