Cyberprotection in IoT environments: A dynamic rule-based solution to defend smart devices

Nespoli, Pantaleone; Díaz-López, Daniel; Mármol, Félix Gómez

doi:10.1016/j.jisa.2021.102878

Cited by 18 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the downside, such a change also implies the existence of ill-motivated entities that constantly try to attack connected systems to damage the confidentiality, integrity, or availability of the provided online services. Such threat entities use increasingly advanced techniques, for example, based on malware campaigns [45] or threats addressed to a specific technology [46].…”

Section: Discussionmentioning

confidence: 99%

On the Way to Automatic Exploitation of Vulnerabilities and Validation of Systems Security through Security Chaos Engineering

Chavarro

Nespoli

Díaz-López

et al. 2022

BDCC

View full text Add to dashboard Cite

Software is behind the technological solutions that deliver many services to our society, which means that software security should not be considered a desirable feature anymore but more of a necessity. Protection of software is an endless labor that includes the improvement of security controls but also the understanding of the sources that induce incidents, which in many cases are due to bad implementation or assumptions of controls. As traditional methods may not be efficient in detecting those security assumptions, novel alternatives must be attempted. In this sense, Security Chaos Engineering (SCE) becomes an innovative methodology based on the definition of a steady state, a hypothesis, experiments, and metrics, which allow to identify failing components and ultimately protect assets under cyber risk scenarios. As an extension of a previous work, this paper presents ChaosXploit, an SCE-powered framework that employs a knowledge database, composed of attack trees, to expose vulnerabilities that exist in a software solution that has been previously defined as a target. The use of ChaosXploit may be part of a defensive security strategy to detect and correct software misconfigurations at an early stage. Finally, different experiments are described and executed to validate the feasibility of ChaosXploit in terms of auditing the security of cloud-managed services, i.e., Amazon buckets, which may be prone to misconfigurations and, consequently, targeted by potential cyberattacks.

show abstract

Section: Discussionmentioning

confidence: 99%

On the Way to Automatic Exploitation of Vulnerabilities and Validation of Systems Security through Security Chaos Engineering

Chavarro

Nespoli

Díaz-López

et al. 2022

BDCC

View full text Add to dashboard Cite

show abstract

“…To defend smart devices in IoT environments Classifying different denial-of-service attacks in cloud computing An automating network security analysis at packet-level A rule-based engine for security alert correlation A belief rule-based anomaly detection Nespoli et al 101 Khorshed et al 102 Holkovivc et al 103 Kenaza et al 104 Ul et al 106 Fuzzy Logic-based Approach…”

Section: Rule-based Modeling and Decision-makingmentioning

confidence: 99%

“…Another rule with many security features may be "IF flag value is SF, service is ftb, and duration <= 4, THEN the outcome is anomaly," which could be created using the tree shown in Figure 7. 32 Nespoli et al 102 provide a dynamic rule-based system for cyberprotection in IoT contexts. Using rule-based learning, Khorshed et al 103 propose a method for classifying various denial-of-service threats in cloud computing.…”

Section: Rule-based Modeling and Decision-makingmentioning

confidence: 99%

Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview

Sarker

2023

Security and Privacy

View full text Add to dashboard Cite

Due to the rising dependency on digital technology, cybersecurity has emerged as a more prominent field of research and application that typically focuses on securing devices, networks, systems, data and other resources from various cyber-attacks, threats, risks, damages, or unauthorized access. Artificial Intelligence (AI), also referred to as a crucial technology of the current Fourth Industrial Revolution (Industry 4.0 or 4IR), could be the key to intelligently dealing with these cyber issues. Various forms of AI methodologies, such as analytical, functional, interactive, textual as well as visual AI can be employed to get the desired cyber solutions according to their computational capabilities. However, the dynamic nature and complexity of real-world situations and data gathered from various cyber sources make it challenging nowadays to build an effective AI-based security model. Moreover, defending robustly against adversarial attacks is still an open question in the area. In this paper, we provide a comprehensive view on "Cybersecurity Intelligence and Robustness", emphasizing multi-aspects AI-based modeling and adversarial learning that could lead to addressing diverse issues in various cyber applications areas such as detecting malware or intrusions, zero-day attacks, phishing, data breach, cyberbullying and other cybercrimes. Thus the eventual security modeling process could be automated, intelligent, and robust compared to traditional security systems. We also emphasize and draw attention to the future aspects of cybersecurity intelligence and robustness along with the research direction within the context of our study. Overall, our goal is not only to explore AI-based modeling and pertinent methodologies but also to focus on the resulting model's applicability for securing our digital systems and society.

show abstract

“…Other areas of research in Edge AI for IoT include integrating image compression algorithms in IoT data from agriculture and agricultural industries, modeling collaborative and distributed heterogeneous operating systems, and deploying AI on devices in a smart home context [63,87,88]. There is also an interest in performing experiments on real fish farms, improving classification accuracies and deploying the model on ESP32 while integrating connectivity media such as LoRaWan and SIM Card Router [47].…”

Section: Edge Ai For Increasing Autonomy Of Iot Systemsmentioning

confidence: 99%

Combining Multiagent Systems with IoT forSmarter Buildings and Cities: a literature review

Luzolo

Galland

Elrawashdeh

et al. 2023

Preprint

View full text Add to dashboard Cite

The Internet of Things (IoT) and Artificial Intelligence (AI) are two technologiesthat have long been applied to the development of smart systems. These systemscover various areas, such as smart cities, energy management, autonomous cars,etc. Intelligence, autonomy, and real-time monitoring are the fundamental ele-ments that characterize these application areas. The convergence of multi-agentsystems (MAS), IoT, and AI has opened up new possibilities for creating smarterbuildings and cities. This integration leverages the power of MAS to enable intelli-gent communication and collaboration among various entities, while IoT providesa vast network of interconnected sensors and devices that collect and transmitreal-time data. On the other hand, AI algorithms process and analyze this data toderive valuable insights and make informed decisions. Multi-agent systems tech-nology is one of the most used for the implementation of such intelligent systems.This article aims to focus on multi-agent systems, although it also covers otherrelated topics by carrying out a Systematic Literature Review (SLR) on relevantresearch works in the previous ten years. Eleven research questions are consideredat the beginning of the review, including typical research topics and applicationdomains. From the SLR results the research directions are: (i) Development ofa methodology that shows how to integrate the different applications indepen-dent of the scenarios that are deployed in. Additionally, elaboration of the toolsused in the integration process; (ii) Deployment of an agent in a microprocessor; (iii) How to implement and connect Multi-agent systems (MAS) technology andInternet of Things (IoT) devices (processors, controllers, sensors, and actuators).

show abstract

Cyberprotection in IoT environments: A dynamic rule-based solution to defend smart devices

Cited by 18 publications

References 8 publications

On the Way to Automatic Exploitation of Vulnerabilities and Validation of Systems Security through Security Chaos Engineering

On the Way to Automatic Exploitation of Vulnerabilities and Validation of Systems Security through Security Chaos Engineering

Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview

Combining Multiagent Systems with IoT forSmarter Buildings and Cities: a literature review

Contact Info

Product

Resources

About