Ensembling Sparse Autoencoders for Network Covert Channel Detection in IoT Ecosystems

被引：4

作者：

Cassavia, Nunziato ^{[1
]}

Caviglione, Luca ^{[2
]}

Guarascio, Massimo ^{[1
]}

Liguori, Angelica ^{[3
]}

Zuppelli, Marco ^{[2
]}

机构：

[1] Inst High Performance Comp & Networking, Via Pietro Bucci 8-9C, I-87036 Arcavacata Di Rende, Italy

[2] Inst Appl Math & Informat Technol, Via Marini 6, I-16149 Genoa, Italy

[3] Univ Calabria, Via Pietro Bucci, Arcavacata Di Rende, Italy

来源：

FOUNDATIONS OF INTELLIGENT SYSTEMS (ISMIS 2022) | 2022年 / 13515卷

基金：

欧盟地平线“2020”;

关键词：

Deep autoencoder; Ensemble method; Covert channel; Intelligent cyber attack detection system;

D O I：

10.1007/978-3-031-16564-1_20

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Network covert channels are becoming exploited by a wide-range of threats to avoid detection. Such offensive schemes are expected to be also used against IoT deployments, for instance to exfiltrate data or to covertly orchestrate botnets composed of simple devices. Therefore, we illustrate a solution based on Deep Learning for the detection of covert channels targeting the TTL field of IPv4 datagrams. To this aim, we take advantage of an Autoencoder ensemble to reveal anomalous traffic behaviors. An experimentation on realistic traffic traces demonstrates the effectiveness of our approach.

引用

页码：209 / 218

页数：10

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