Netinfo Security ›› 2024, Vol. 24 ›› Issue (2): 167-178.doi: 10.3969/j.issn.1671-1122.2024.02.001
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FENG Guangsheng, JIANG Shunpeng(), HU Xianlang, MA Mingyu
Received:
2023-12-12
Online:
2024-02-10
Published:
2024-03-06
Contact:
JIANG Shunpeng
E-mail:2545451677@qq.com
CLC Number:
FENG Guangsheng, JIANG Shunpeng, HU Xianlang, MA Mingyu. New Research Progress on Intrusion Detection Techniques for the Internet of Things[J]. Netinfo Security, 2024, 24(2): 167-178.
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URL: http://netinfo-security.org/EN/10.3969/j.issn.1671-1122.2024.02.001
方案 | 数据集 | 机器学习方法 | 评价指标 |
---|---|---|---|
MANHAS[ | KDD99 | KNN、SVM、DT、朴素贝叶斯 | 准确率、灵敏度、精确度、F1分数 |
VERMA[ | CIDDS-001、UNSWNB15、NSL-KDD | 随机森林、AdaBoost | 准确率、灵敏度、AUC、 检测率 |
ABBAS[ | CICIDS2017 | 集成学习 | 准确率 |
ABDALJABAR[ | DoH20 | KNN | 准确率、 检测率 |
GUEZZAZ[ | NSL-KDD、CICIDS2017 | DT | 准确率、检测率、误报率 |
KASONGO[ | UNSWNB15 | GA-RF | 准确率、AUC |
KESERWANI[ | KDDCup99、NSL-KDD、CICIDS-2017 | GWO-PSO-RF | 准确率 |
GU[ | UNSWNB15、CICIDS2017、NSL-KDD、Kyoto 2006+ | NB-SVM | 准确率、检测率、虚警率 |
HAZMAN[ | BoT-IoT、IoT-23 | 集成学习 | 准确率、精确度、F1分数、误报率 |
AHMAD[ | UNSWNB15 | RF、SVM、ANN | 准确率 |
OKEY[ | CICIDS2017、CSE-CIC-IDS2018 | BoostedEnML | 准确率、精确度、AUC、F1分数 |
周杰英[ | UNSW-NB15 | RF-GBDT | 检测率、虚警率、F1分数、AUC |
方案 | 特点 | 评价指标 |
---|---|---|
MOHY-EDDINE[ | 采用PCA、单变量统计检验和遗传算法进行特征选择,以提高数据质量并选出k个表现最好的特征 | 准确率、检测率、虚警率、预测时间和误报率 |
NASIR[ | 采用SpiderMonkey(SM)、PCA、信息增益和相关属性评价进行特征选择 | 准确率、F1分数、AUC-ROC值 |
WAHAB[ | 利用PCA法分析数据流中特征方差的变化 | 准确率 |
VADIGI[ | 所有智能体可通过注意力加权模型聚合过程从其他智能体可用数据的分布和模式中获益 | 准确率、精确度、假阳性率和AUC |
RAMANA[ | 基于深度Q网络的深度神经网络集成强化学习,用于边缘云基础架构的入侵检测 | 准确率、精确度和 召回率 |
方案 | 特点 | 数据集 | 评价指标 |
---|---|---|---|
CHEN[ | 多目标进化CNN,运行在物联网雾计算的雾节点上 | AWID、CIC-IDS2107 | 精确度、准确率、召回率、F1分数 |
ALJUMAH[ | 时间CNN,整合了CNN和通用卷积的物联网入侵检测智能模型,解决了数据集不平衡问题 | Bot-IoT | 精确度、准确率、召回率、F1分数、 日志损失 |
KAN[ | 自适应粒子群优化CNN,用于物联网入侵检测 | 开源数据集[ | 精确度、准确率、召回率、F1分数 |
ROOPAK[ | 融合跳跃基因适应NSGA-II多目标优化方法和集成LSTM的CNN,用于数据降维和深度学习 | CICIDS2017 | 精确度、准确率、召回率、F1分数 |
ALMIANI[ | 基于深度RNN的物联网IDS | NSL-KDD | 准确率、精确度、Mathew相关性、Cohen的Kappa系数 |
SARAVANAN[ | 结合区块链的BbAB方案和优化的RNN | MED | 准确率、精确度、召回率、F1分数、检测率 |
SAHEED[ | 基于DRNN和监督机器学习模型的IDS | CICIDS | 精确度、召回率、F1分数 |
ALIMI[ | RLSTM模型 | CICIDS-2017、NSL-KDS | 精确度、召回率、F1分数 |
IMRANA[ | BiDLSTM模型 | NSL-KDD | 精确度、准确率、召回率、F1分数 |
HANAFI[ | 基于改进的二进制Golden Jackal优化算法和LSTM网络的IDS | NSL-KDD、CICIDS2017 | 精确度、准确率、召回率、F1分数 |
DEORE[ | 基于黑猩猩鸡群优化的深度LSTM,强调CNN的特征提取和LSTM的检测性能 | NSL-KDD、BoT-IoT | 准确率、敏感度 |
方案 | 针对问题 | 评价指标 | 数据集 |
---|---|---|---|
MUHAMMAD[ 等人 | 入侵者冒充有效服务提供商的网络 攻击问题 | 准确率 | KDDCUP99、NSL-KDD、aegean WiFi入侵数据集 |
ABOELWAFA[ 等人 | 物联网中的虚假数据注入(FDI) 攻击 | 准确率 | — |
SINGH[ | 增加IDS的可靠性,减少IDS的 负担 | 分类准确率、云服务器的延迟、工作负载 | — |
ELMASRY[ | 因IDS训练数据冗余和不相关特征导致检测率低的问题 | 检测率、 误报率 | — |
LUNARDI[ | 异构IP连接设备数量和流量增加导致信息漏洞的问题 | 准确率 | — |
CHEN[ | 基于机器学习的NBAD方法对网络行为分类不灵活、准确率低的问题 | 准确率 | — |
WANG[ | 云计算环境下网络流量大规模、高维度、高冗余的问题 | 检测性能 | KDDCUP99、 NSL-KDD |
LU[ | 入侵检测方法误报率高、检测准确率低的问题 | 准确率 | — |
方案 | 面向问题 | 特点 | 数据集 |
---|---|---|---|
EGBAD | 证明在非图像层面GAN表现良好 | — | KDDCUP99 |
GIDS | — | 实现实际部署 | 车载网 |
具有精细损失函数的半监督IDS | GAN-IDS不适合处理离散特征 | — | KDDCUP99 |
MAD-GAN | 现实情况中攻击具有高动态复杂性 | 对多元时间序列进行攻击检测 | SWaT、WADI |
MAGNETO | 恶意流量与正常流量相比存在不平衡问题 | 具有较高鲁棒性 | 基准数据集 |
过采样IDS | 数据不平衡 | 利用WGAN-GP进行建模,通过方差分析进行降维 | NSL-KDD、UNSW-NB15、CICIDS-2017 |
BiCirGAN | 在离散高维特征的不平衡数据集上检测精确度低和泛化能力差 | — | KDD99、UNSW-NB15、WSN_DS |
MemFGAN | 在异常样本有限时存在精确度低和容易产生过拟合问题 | 为生成器和判别器设计新的训练目标 | MAWILab、ISCX2012、IDS2017、IDS2018 |
安全联邦蒸馏GAN-IDS | 敏感信息保密必要性导致的数据孤岛问题 | 基于Wasserstein距离、EC-GAN | WSTS、AWID |
TMG-GAN | — | 多生成器结构 | CICIDS2017、UNSW-NB15 |
改进CGAN-IDS | CGAN中两类样本重叠导致生成器梯度消失的不稳定问题 | 构建WCGAN-SVM工控系统入侵检测模型 | UCI、SWaT |
方案 | 面向问题 | 使用的方法 | 评价指标 |
---|---|---|---|
HSU[ | 具有自我更新的能力,检测异常网络流量 | 基于DRL的异常网络IDS | 准确率、召回率和精确度 |
PRIYA[ | 自动化和智能的网络ID策略,应对高级攻击者 | 基于二进制蝙蝠算法的特征选择和DRL(BBAFS-DRL)系统 | ROC、呈现率 |
ALAVIZADEH[ | 持续自动学习,检测不同类型 网络入侵 | 结合基于Q学习的强化学习与深度前馈神经网络方法的网络入侵检测 | 准确率 |
李贝贝[ | 有效检测工业物联网多种类型 网络攻击 | 基于DRL算法的工业物联网IDS | 准确率、精确度、召回率、F1分数 |
李自若[ | 针对IEC 61850和物联网协议的异常检测 | 构建智能变电站自动化网络架构,明确制造报文规范、GOOSE协议、SV报文、MQTT协议和CoAP协议的使用范围 | 误报率 |
WANG[ | 高连接性和海量设备导致的广泛安全威胁、漏洞和隐私问题 | 基于DRL的入侵检测策略 | 准确率、精确度、召回率、F1分数 |
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