IoT-AMLHP: Aligned multimodal learning of header-payload representations for resource-efficient malicious IoT traffic classification

IF 4.4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Fengyuan Nie , Guangjie Liu , Weiwei Liu , Jianan Huang , Bo Gao
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引用次数: 0

Abstract

Traffic classification is crucial for securing Internet of Things (IoT) networks. Deep learning-based methods can autonomously extract latent patterns from massive network traffic, demonstrating significant potential for IoT traffic classification tasks. However, the limited computational and spatial resources of IoT devices pose challenges for deploying more complex deep learning models. Existing methods rely heavily on either flow-level features or raw packet byte features. Flow-level features often require inspecting entire or most of the traffic flow, leading to excessive resource consumption, while raw packet byte features fail to distinguish between headers and payloads, overlooking semantic differences and introducing noise from feature misalignment. Therefore, this paper proposes IoT-AMLHP, an aligned multimodal learning framework for resource-efficient malicious IoT traffic classification. Firstly, the framework constructs a packet-wise header-payload representation by parsing packet headers and payload bytes, resulting in an aligned and standardized multimodal traffic representation that enhances the characterization of heterogeneous IoT traffic. Subsequently, the traffic representation is fed into a resource-efficient neural network comprising a multimodal feature extraction module and a multimodal fusion module. The extraction module employs efficient depthwise separable convolutions to capture multi-scale features from different modalities while maintaining a lightweight architecture. The fusion module adaptively captures complementary features from different modalities and effectively fuses multimodal features. Extensive experiments on three public IoT traffic datasets demonstrate that the proposed IoT-AMLHP outperforms state-of-the-art methods in classification accuracy while significantly reducing computational and spatial resource overhead, making it highly suitable for deployment in resource-constrained IoT environments.
IoT- amlhp:针对资源高效恶意物联网流量分类的报头-有效负载表示的对齐多模态学习
流量分类对于保护物联网(IoT)网络至关重要。基于深度学习的方法可以从大量网络流量中自主提取潜在模式,在物联网流量分类任务中显示出巨大的潜力。然而,物联网设备有限的计算和空间资源为部署更复杂的深度学习模型带来了挑战。现有的方法严重依赖于流级特征或原始数据包字节特征。流级特征通常需要检查整个或大部分流量流,导致过度的资源消耗,而原始数据包字节特征无法区分报头和有效负载,忽略了语义差异,并引入了特征偏差带来的噪声。因此,本文提出了IoT- amlhp,这是一种针对资源高效的恶意物联网流量分类的对齐多模式学习框架。首先,该框架通过解析数据包头和有效载荷字节,构建了一个基于数据包的报头-有效载荷表示,从而形成了一个统一和标准化的多模式流量表示,增强了异构物联网流量的表征。随后,将交通表示输入到一个资源高效的神经网络中,该神经网络包括多模态特征提取模块和多模态融合模块。提取模块采用高效的深度可分离卷积来捕获来自不同模态的多尺度特征,同时保持轻量级架构。融合模块自适应地捕获不同模态的互补特征,有效地融合多模态特征。在三个公共物联网流量数据集上进行的大量实验表明,所提出的IoT- amlhp在分类精度方面优于最先进的方法,同时显着降低了计算和空间资源开销,使其非常适合在资源受限的物联网环境中部署。
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来源期刊
Ad Hoc Networks
Ad Hoc Networks 工程技术-电信学
CiteScore
10.20
自引率
4.20%
发文量
131
审稿时长
4.8 months
期刊介绍: The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to: Mobile and Wireless Ad Hoc Networks Sensor Networks Wireless Local and Personal Area Networks Home Networks Ad Hoc Networks of Autonomous Intelligent Systems Novel Architectures for Ad Hoc and Sensor Networks Self-organizing Network Architectures and Protocols Transport Layer Protocols Routing protocols (unicast, multicast, geocast, etc.) Media Access Control Techniques Error Control Schemes Power-Aware, Low-Power and Energy-Efficient Designs Synchronization and Scheduling Issues Mobility Management Mobility-Tolerant Communication Protocols Location Tracking and Location-based Services Resource and Information Management Security and Fault-Tolerance Issues Hardware and Software Platforms, Systems, and Testbeds Experimental and Prototype Results Quality-of-Service Issues Cross-Layer Interactions Scalability Issues Performance Analysis and Simulation of Protocols.
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