- Volume 11
- Issue 2
- (5) Articles
Rabah Scharif
,
Ossama Embarak
As the digital workplace becomes more prevalent, organizations are faced with the challenge of balancing security and information management. On one hand, there is a need to protect sensitive data and prevent cyberattacks, while on the other hand, organizations must enable employees to collaborate and share information effectively. Machine learning (ML) is a promising technology that can help organizations address this challenge. By analyzing data patterns and identifying potential security threats, ML algorithms can enhance security measures and mitigate risks. At the same time, ML can also facilitate information management by automating routine tasks and improving the accuracy of data analysis. In this paper, we explore the role of ML in balancing security and information management in the digital workplace. We propose a hybrid ML model that integrates autoencoder and convolutional subnetworks in unified architecture to help capturing and security threats in the digital workplace, without compromising the information management tasks. We also present a case study of a real-world implementation of ML in a digital workplace setting, highlighting the benefits and limitations of this approach. Our findings suggest that ML can be a valuable tool for achieving a balance between security and information management in the digital workplace, but its successful implementation requires careful consideration of organizational context and stakeholder needs.
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Doi: https://doi.org/10.54216/JCIM.110201
Vol. 11
Issue. 2
PP. 08-16, (2023)
Ahmed Abdelaziz
,
Alia N. Mahmoud
Effective management of patient data is critical for improving the quality of care and patient outcomes in healthcare systems. However, ensuring the confidentiality, integrity, and availability of patient data while complying with regulatory requirements can be challenging. To address this challenge, this work proposes an artificial intelligence (AI)-enabled framework that integrates information security (IS) and information management (IM) capabilities into a unified solution for improving the overall functionality of healthcare systems. The proposed framework leverages AI algorithms to automate managerial transactions of healthcare systems, while ensuring they are secure against possible threats. By automating these tasks, the framework can reduce the burden on healthcare staff, improve the accuracy and speed of information processing, and reduce the risk of human error. Our framework provides accurate and timely information to healthcare providers, enabling them to make informed decisions and provide better care to patients.
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Doi: https://doi.org/10.54216/JCIM.110202
Vol. 11
Issue. 2
PP. 17-26, (2023)
Ahmed Hatip
,
Karla Zayood
,
Rabah Scharif
Recently, there has been a significant increase in the use of collaborative environments for managing and sharing information. However, these environments often present significant security risks due to the potential for unauthorized access, data leakage, and other security breaches. To address these risks, machine learning (ML) techniques have been increasingly used to secure information management in collaborative environments. We propose a novel ML approach to be applied to detect and prevent security threats in collaborative environments. Our approach integrates temporal convolution to detect and prevent security threats by analyzing spatial-temporal patterns in data from various sources, such as network traffic, system logs, and user behavior. Furthermore, we present a case study demonstrating the effectiveness of our model in securing collaborative information management. The case study involves the development of our system for detecting insider threats in a collaborative environment. Extensive experimentation on this case study demonstrates the efficiency and effectiveness of the proposed system for securing information management and promoting further developments.
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Doi: https://doi.org/10.54216/JCIM.110203
Vol. 11
Issue. 2
PP. 27-35, (2023)
Taif Khalid Shakir
,
Rabah Scharif
,
Manal M. Nasir
As technology continues to evolve, the importance of information security and management becomes more crucial than ever. Blockchain and machine learning (ML) are two technologies that are gaining increasing attention in this field. Blockchain provides a secure and decentralized platform for storing and sharing information, while ML can help detect patterns and anomalies in data to identify potential security threats. This paper proposes a blockchain-based ML system for securing information management by providing an automated service for detecting anomalies in Ethereum transactions. The system utilizes a blockchain network to securely store and manage data, and ML algorithms to analyze and detect potential security threats. We present a case study using the Ethereum Fraud Detection Dataset to demonstrate the effectiveness of our proposed system in detecting fraudulent transactions. Our results show that our system outperforms traditional ML algorithms in terms of accuracy (99.55%), and F1-score (99.98%), highlighting the potential of blockchain-based ML for improving information security and management in various industries.
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Doi: https://doi.org/10.54216/JCIM.110204
Vol. 11
Issue. 2
PP. 36-46, (2023)
Amit Kumar Chandanan
Among the many uses for WSN, which is an ad hoc wireless system, are conveyance, calamity administration, industrialized observing, health observing, and so on. Intrusion Detection System (IDS) is a top-tier network security measure. In order to prevent cross-layer attacks, IDS detection rates must be high. Using a technique known as the "Rule of Thumb" or ELM (Extreme Learning Machine) algorithm, WSN is able to predict the future with a great grade of accurateness. The projected RELM provides a comprehensive overview of both the attacks and the rules for detecting them. The rules can identify threats at the different layers. If the rule-founded IDS were deployed at the sensor nodes, less data would need to be transmitted over the network, saving power. Relative to the SVM (Support Vector Machine) and BPN (Back Propagation Neural Network) on the NSL-KDD dataset, RELM evaluates ELM's detection rate. Because of its superior detection rate, ELM has been used as the foundation of the IDS deployed at the BS to protect it against intrusion. If the criteria were combined with the ELM algorithm, the resulting system would have a higher detection rate than any currently available alternative.
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Doi: https://doi.org/10.54216/JCIM.110205
Vol. 11
Issue. 2
PP. 47-56, (2023)
