Cyber Security and the Role of Intelligent Systems in Addressing its Challenges

Harel, Yaniv; Gal, Irad Ben; Elovici, Yuval

doi:10.1145/3057729

Cited by 28 publications

(15 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Interestingly, several cybersecurity tasks can be performed using machine learning. These tasks include anomaly detection, spam filtering, user monitoring, risk analysis, and zero-day exploit identification [16]. Machine learning algorithms have been used widely in developing intrusion detection systems for IoT networks.…”

Section: B Intrusion Detection Systems In the Internet Of Things (Iot)mentioning

confidence: 99%

A Hybrid Intrusion Detection Model for Identification of Threats in Internet of Things Environment

Owoh¹,

Singh²,

Zaaba³

2021

IJACSA

View full text Add to dashboard Cite

Internet of Things (IoT) has transcended from its application in traditional sensing networks such as wireless sensing and radio frequency identification to life-changing and critical applications. However, IoT networks are still vulnerable to threats, attacks, intrusions, and other malicious activities. Intrusion Detection Systems (IDS) that employ unsupervised learning techniques are used to secure sensitive data transmitted on IoT networks and preserve privacy. This paper proposes a hybrid model for intrusion detection that relies on a dimension reduction algorithm, an unsupervised learning algorithm, and a classifier. The proposed model employs Principal Component Analysis (PCA) to reduce the number of features in a dataset. The K-means algorithm generates clusters that serve as class labels for the Support Vector Machine (SVM) classifier. Experimental results using the NSL-KDD and the UNSW-NB15 datasets justify the effectiveness of our proposed model in detecting malicious activities in IoT networks. The proposed model, when trained, identifies benign and malicious behaviours using an unlabelled dataset.

show abstract

Section: B Intrusion Detection Systems In the Internet Of Things (Iot)mentioning

confidence: 99%

A Hybrid Intrusion Detection Model for Identification of Threats in Internet of Things Environment

Owoh¹,

Singh²,

Zaaba³

2021

IJACSA

View full text Add to dashboard Cite

show abstract

“…AI is the process of recognizing something it has never seen before and predict the future, by extracting patterns in the past (Harel, Gal & Elovici, 2017;Bali, Garg & Bali, 2019). AI deals with the study and design of intelligent based agents which maintains the environment, takes actions which increases the chance for occurring success.…”

Section: Ai and Multi Agent Based Systemsmentioning

confidence: 99%

Artificial intelligence and multi agent based distributed ledger system for better privacy and security of electronic healthcare records

Alruwaili

2020

PeerJ Computer Science

View full text Add to dashboard Cite

Background Application of Artificial Intelligence (AI) and the use of agent-based systems in the healthcare system have attracted various researchers to improve the efficiency and utility in the Electronic Health Records (EHR). Nowadays, one of the most important and creative developments is the integration of AI and Blockchain that is, Distributed Ledger Technology (DLT) to enable better and decentralized governance. Privacy and security is a critical piece in EHR implementation and/or adoption. Health records are updated every time a patient visits a doctor as they contain important information about the health and wellbeing of the patient and describes the history of care received during the past and to date. Therefore, such records are critical to research, hospitals, emergency rooms, healthcare laboratories, and even health insurance providers. Methods In this article, a platform employing the AI and the use of multi-agent based systems along with the DLT technology for privacy preservation is proposed. The emphasis of security and privacy is highlighted during the process of collecting, managing and distributing EHR data. Results This article aims to ensure privacy, integrity and security metrics of the electronic health records are met when such copies are not only immutable but also distributed. The findings of this work will help guide the development of further techniques using the combination of AI and multi-agent based systems backed by DLT technology for secure and effective handling EHR data. This proposed architecture uses various AI-based intelligent based agents and blockchain for providing privacy and security in EHR. Future enhancement in this work can be the addition of the biometric based systems for improved security.

show abstract

“…Framework was designed by [24] using fuzzy logic. [37] Highlight the role of intelligent system and artificial intelligences in addressing the challenges of cyber security but they didn't illustrate the framework on how to implement the system. In [33] they presented a general review on the malware detection in mobile devices based on parallel and distributed network.…”

Section: Machine Learning Algorithms Applied To System Securitymentioning

confidence: 99%