multiple layers of fuzzy logic to quantify vulnerabilies in iot

Thesis Deployment Optimization of IoT Devices through Attack Graph Analysis

The Internet of things (IoT) has become an integral part of our life at both work and home. However, these IoT devices are prone to vulnerability exploits due to their low cost, low resources, the diversity of vendors, and proprietary firmware. Moreover, short range communication protocols (e.g., Bluetooth or ZigBee) open additional opportunities for the lateral movement of an attacker within an organization. Thus, the type and location of IoT devices may significantly change the level of network security of the organizational network. In this work, we quantify the level of network security ba…

AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble Inference Models against Adversarial Volumetric Attacks on IoT Networks

Machine Learning-based techniques have shown success in cyber intelligence. However, they are increasingly becoming targets of sophisticated data-driven adversarial attacks resulting in misprediction, eroding their ability to detect threats on network devices. In this paper, we present AdIoTack, a system that highlights vulnerabilities of decision trees against adversarial attacks, helping cybersecurity teams quantify and refine the resilience of their trained models for monitoring IoT networks. To assess the model for the worst-case scenario, AdIoTack performs white-box adversarial learning t…

Quantifying and Managing Impacts of Concept Drifts on IoT Traffic Inference in Residential ISP Networks

Millions of vulnerable consumer IoT devices in home networks are the enabler for cyber crimes putting user privacy and Internet security at risk. Internet service providers (ISPs) are best poised to play key roles in mitigating risks by automatically inferring active IoT devices per household and notifying users of vulnerable ones. Developing a scalable inference method that can perform robustly across thousands of home networks is a non-trivial task. This paper focuses on the challenges of developing and applying data-driven inference models when labeled data of device behaviors is limited an…

REAL-IoT：在实际对抗攻击下表征GNN入侵检测的鲁棒性

REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack

提出REAL-IoT框架，通过统一数据集和物理测试床评估GNN网络入侵检测在分布漂移与实际攻击下的鲁棒性，并利用LLM过滤对抗样本以增强系统韧性。

Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems, cyber risk at the edge

The Internet of Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state of the art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analy…

面向公共利益的PUF：在操作系统层面增强嵌入式安全性

PUF for the Commons: Enhancing Embedded Security on the OS Level

本文提出将物理不可克隆函数（PUFs）集成到支持约250个平台的物联网操作系统RIOT中，利用未初始化SRAM作为设备数字指纹，基于700多个真实老化节点的测量量化偏差与老化效应，实验证明其能提供256位随机种子和128位密钥安全性，显著增强了低端IoT设备的安全性。

L2AI：基于区块链的医疗物联网环境中轻量级三因素认证与授权方案

L2AI: lightweight three-factor authentication and authorization in IOMT blockchain-based environment

针对医疗物联网中不安全信道带来的安全挑战，提出L2AI轻量级三因素认证方案，利用伪身份和动态索引增强匿名性，并通过ROR模型和Proverif工具验证安全性，支持高效注册与可扩展性。

企业网中基于态势感知的风险自适应访问控制

Situational Awareness based Risk-Adapatable Access Control in Enterprise Networks

随着计算环境向物联网（IoT）等分布式架构发展，企业正在从传统的基于边界的安全模型转向所谓的零信任网络（ZTN）模型，该模型将内联网和互联网视为同样不可信。此类安全模型将动态和情景因素（例如设备位置和安全风险级别风险）引起的风险纳入访问控制决策中。研究人员已经开发了许多风险模型，如RAdAC（风险自适应访问控制）来处理动态上下文，这些模型已应用于医疗和其他场景。在这份立场文件中，我们描述了我们正在进行的工作，将RAdAC应用于ZTN。我们开发了一个政策管理框架，FURZE，以促进模糊风险评估，也定义了如何适应动态变化的环境。我们还考虑了如何将企业安全态势感知（SSA）--根据当前威胁和受威胁信息资产的相对重要性来描述对组织任务的潜在影响--纳入RAdAC方案

Battery draining attacks against edge computing nodes in IoT networks

Many IoT devices, especially those deployed at the network edge have limited power resources. A number of attacks aim to exhaust these resources and drain the batteries of such edge nodes. In this work, we study the effects of a variety of battery draining attacks against edge nodes. Through simulation, we clarify the extent to which such attacks are able to increase the usage and hence waste the power resources of edge nodes. Specifically, we implement hello flooding, packet flooding, selective forwarding, rank attack, and versioning attack in ContikiOS and simulate them in the Cooja simulato…

面向物联网漏洞检测的反编译可执行性与程序等价性统计分析

Statistical Analysis of Executability and Program Equivalence in Decompilation for IoT Vulnerability Detection

该研究针对物联网固件漏洞检测的反编译问题，提出九维质量评估指标，通过统计分析证明其可有效评估反编译质量，为物联网设备缺陷及黑盒生成模型质量评估提供基础。