A framework for cloud forensics evidence collection and analysis using security information and event management

Irfan, Muhammad; Abbas, Haider; Sun, Yunchuan; Sajid, Anam; Pasha, Maruf

doi:10.1002/sec.1538

Cited by 21 publications

(10 citation statements)

References 17 publications

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“…It is often used to execute untrusted programs or code, possibly from unverified suppliers, users, websites, or untrusted third parties. -Security information and event management (SIEM) [49] is a combination of security information management (SIM) and security event management (SEM) that provides real-time analysis of device and network hardware security alerts. -Cryptography [50] is a popular method used for protecting data or information that uses the secret keys, e.g., secret-key, public key, and hash function, to encrypt and decrypt data for communication.…”

Section: Cybersecurity Defense Strategiesmentioning

confidence: 99%

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

Furhad²,

2021

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Artificial intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (or Industry 4.0), which can be used for the protection of Internet-connected systems from cyber threats, attacks, damage, or unauthorized access. To intelligently solve today's various cybersecurity issues, popular AI techniques involving machine learning and deep learning methods, the concept of natural language processing, knowledge representation and reasoning, as well as the concept of knowledge or rule-based expert systems modeling can be used. Based on these AI methods, in this paper, we present a comprehensive view on "AI-driven Cybersecurity" that can play an important role for intelligent cybersecurity services and management. The security intelligence modeling based on such AI methods can make the cybersecurity computing process automated and intelligent than the conventional security systems. We also highlight several research directions within the scope of our study, which can help researchers do future research in the area. Overall, this paper's ultimate objective is to serve as a reference point and guidelines for cybersecurity researchers as well as industry professionals in the area, especially from an intelligent computing or AI-based technical point of view.

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Section: Cybersecurity Defense Strategiesmentioning

confidence: 99%

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

Furhad²,

2021

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“…security information and event management (SIEM) [88], or cryptography [89], are typically used to mitigate the issues. However, data science modeling is demanding security intelligence as well through its capability of extracting more actionable insights from the data to intelligently manage these issues.…”

Section: Smart City Services and Data-driven Decision-makingmentioning

confidence: 99%

Smart City Data Science: Towards data-driven smart cities with open research issues

Sarker

2022

Internet of Things

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“…This could result in scenarios where a malicious user injects fake data to manipulate the correct execution of a given system workflow. In addition, the need of evaluating the trustworthiness of collected data clearly emerged in the context of forensics science and has been initially dealt with by defining a systematic and reliable methodology for data collection and analysis [10]. Some solutions based on blockchain have been also proposed to guarantee availability, integrity, and verifiability of collected data (e.g., [5,16,19]).…”

Section: Problem Statement and Related Workmentioning

confidence: 99%

From Trustworthy Data to Trustworthy IoT

Ardagna

Asal

Damiani

et al. 2020

ACM Trans. Cyber-Phys. Syst.

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Internet of Things (IoT) is composed of physical devices, communication networks, and services provided by edge systems and over-the-top applications. IoT connects billions of devices that collect data from the physical environment, which are pre-processed at the edge and then forwarded to processing services at the core of the infrastructure, on top of which cloud-based applications are built and provided to mobile end users. IoT comes with important advantages in terms of applications and added value for its users, making their world smarter and simpler. These advantages, however, are mitigated by the difficulty of guaranteeing IoT trustworthiness, which is still in its infancy. IoT trustworthiness is a must especially in critical domains (e.g., health, transportation) where humans become new components of an IoT system and their life is put at risk by system malfunctioning or breaches. In this article, we put forward the idea that trust in IoT can be boosted if and only if its automation and adaptation processes are based on trustworthy data. We therefore depart from a scenario that considers the quality of a single decision as the main goal of an IoT system and consider the trustworthiness of collected data as a fundamental requirement at the basis of a trustworthy IoT environment. We therefore define a methodology for data collection that filters untrusted data out according to trust rules evaluating the status of the devices collecting data and the collected data themselves. Our approach is based on blockchain and smart contracts and collects data whose trustworthiness and integrity are proven over time. The methodology balances trustworthiness and privacy and is experimentally evaluated in real-world and simulated scenarios using Hyperledger fabric blockchain.

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A framework for cloud forensics evidence collection and analysis using security information and event management

Cited by 21 publications

References 17 publications

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

Smart City Data Science: Towards data-driven smart cities with open research issues

From Trustworthy Data to Trustworthy IoT

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