The Cost of Progress: Exploring Privacy Nightmares for AI in Precision Medicine

Ahmed; Noura

doi:https://doi.org/10.54216/JCIM.080205

Journal of Cybersecurity and Information Management | 2023

Managing Information Security Risks in the Age of IoT

The advent of the Internet of Things (IoT) has led to the proliferation of connected devices, creating numerous security challenges. With billions of devices generating vast amounts of data, managing information security risks in the age of IoT has become increasingly complex. Traditional security approaches are not sufficient to mitigate the risks posed by IoT devices. Machine learning (ML) provides a promising approach to enhance the security of IoT systems. This paper proposes a machine learning approach for managing information security risks in the age of IoT. The proposed approach utilizes ML algorithms to identify and mitigate security threats in IoT systems. The approach involves collecting and analyzing data from IoT devices, and applying ML algorithms to detect patterns and anomalies that may indicate security threats. The ML algorithms are trained using both supervised and unsupervised learning techniques to enable them to identify known and unknown threats. The paper describes a case study in which the proposed approach is applied to an IoT system for home security. The results demonstrate that the ML approach can effectively detect security threats in the IoT system and mitigate them in real-time.

groups

Abedallah Z. Abualkishik mail -

Rasha Almajed mail

link https://doi.org/10.54216/JCIM.110103

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2023

A Deep Learning Framework for Securing IoT Against Malwares

The proliferation of Internet of Things (IoT) devices has led to an increase in the number of malware attacks targeting these devices. Traditional security mechanisms such as firewalls and antivirus software are often inadequate in protecting IoT devices from malware attacks due to their limited resources and the heterogeneity of IoT networks. In this paper, we propose DeepSecureIoT, a deep learning-based framework for securing IoT against malware attacks. Our proposed framework uses a deep convolutional neural network (CNN) to extract features from network traffic and classify it as normal or malicious. The CNN is trained using a large dataset of network traffic to accurately identify malware attacks and reduce false positives. We evaluate the performance of DeepSecureIoT using a benchmark dataset of real-world IoT malware attacks. The results show that our proposed framework achieves an accuracy of 0.961 in detecting and classifying malware attacks, outperforming state-of-the-art intrusion detection systems. Moreover, DeepSecureIoT has low computational overhead and can be deployed on resource-constrained IoT devices.

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Mustafa El-Taie mail -

Aaras Y.Kraidi mail

link https://doi.org/10.54216/JCIM.110104

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2021

Guardians of the IoT Galaxy: Using Deep Learning to Secure IoT Networks Against Botnet Attacks

The Internet of Things (IoT) has transformed the way we live and work, with billions of interconnected devices continuously exchanging data. However, the increasing adoption of IoT devices has also made them an attractive target for cybercriminals. Botnets, a network of compromised devices that can be remotely controlled by attackers, are one of the most significant threats to IoT networks. Traditional security solutions are insufficient to combat this threat, as they often rely on signature-based detection methods that can be easily bypassed by attackers. This work proposes an applied deep learning-based approach to secure IoT networks against botnet attacks, based on residual learning architecture that combine convolutional neural network to analyze device behavior and identify abnormal activity patterns that may indicate botnet infection. Our approach is evaluated on real-world BotNet dataset and achieved a high detection rate of botnet activity, outperforming traditional detection methods. The empirical findings show that ours can be used as a tool for developing more advanced and adaptive security solutions to safeguard the IoT galaxy.

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Ahmed N. Al-Masri mail -

Hamam Mokayed mail

link https://doi.org/10.54216/JCIM.050102

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2020

Securing the IoT: An Efficient Intrusion Detection System Using Convolutional Network

The Internet of Things (IoT) is an ever-expanding network of interconnected devices that enables various applications, such as smart homes, smart cities, and industrial automation. However, with the proliferation of IoT devices, security risks have increased significantly, making it necessary to develop effective intrusion detection systems (IDS) for IoT networks. In this paper, we propose an efficient IDS for complex IoT environments based on convolutional neural networks (CNNs). Our approach uses IoT traffics as input to our CNN architecture to capture representational knowledge required to discriminate different forms of attacks. Our system achieves high accuracy and low false positive rates, even in the presence of complex and dynamic network traffic patterns. We evaluate the performance of our system using public datasets and compare it with other cutting-edge IDS approaches. Our results show that the proposed system outperforms the other approaches in terms of accuracy and false positive rates. The proposed IDS can enhance the security of IoT networks and protect them against various types of cyber-attacks.

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Harith Yas mail -

Manal M. Nasir mail

link https://doi.org/10.54216/JCIM.010105

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

Financial Technology and Innovation | 2026

Scaling Trustworthy Digital Finance in Asia and the Middle East: A Regional Evidence Review and Institutional Readiness Framework

Digital finance has made significant progress in Asia and the Middle East, but it is not one-size-fits-all in terms of performance in inclusion, productivity, or stability. The key limitation is now institutional, not technical: Payment rails, data-sharing structures, regulatory capability, market capacity, and protections need to be built up in lockstep. This article summarizes 34 peer-reviewed papers from 2020 to 2025 and sets up an institutional-readiness framework for trustworthy digital finance for the region. The synthesis brings together elements of structured evidence coding, comparative evidence analysis of East and Southeast Asian evidence, evidence from the Gulf Cooperation Council and broader Middle East, and cross-regional emerging-market evidence analysis. Five interdependent pillars emerge: digital public infrastructure; data governance and interoperability; adaptive regulation; market capability and inclusive adoption; and resilience with consumer protection. The evidence from Asia is more extensive with regard to outcomes at the household- and firm-level, platform-enabled inclusion, and digital infrastructure, whereas evidence from the Middle East is more focused on outcomes related to platform adoption by regulators, bank performance, and governance conditions. In both regions, positive results are undermined by asymmetric data access, weak markets, low consumer capability, and low innovation coupled with the lack of post-deployment supervisions. The article suggests an order of implementation for both national and cross-border initiatives, identifies the research gaps and proposes a research agenda for ASEAN-GCC digital-finance corridors. A core message is that trustworthy scale relies on institutional complementarity – innovation’s developmentally valuable only if it is supported by development of infrastructure, rules, capabilities and safeguards.

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Rhada Boujlil mail -

Saad Alsunbul mail

link https://doi.org/10.54216/FinTech-I.060104

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2021

Detecting In-Vehicle Attacks with Deep Learning: An Applied Approach

With the increasing number of connected vehicles on the road, the need for secure in-vehicle systems is more pressing than ever. In-vehicle attacks can compromise the safety and privacy of drivers and passengers, and the detection of such attacks is crucial to prevent potential harm. In this paper, we propose an applied deep learning approach for detecting in-vehicle attacks. Our approach is based on a gated recurrent unit (GRU) that is trained on a dataset of network traffic collected from in-vehicle communication systems. We evaluate our approach on a real-world dataset and demonstrate its effectiveness in detecting different types of in-vehicle attacks, including denial of service (DoS), remote replay attacks, and flooding attacks. Our results show that the proposed approach can achieve high accuracy in detecting in-vehicle attacks. We also compare our approach with traditional machine learning algorithms and show that our approach outperforms them in terms of accuracy. Our proposed approach can be used as a standalone system or as a complementary solution to existing in-vehicle security systems to enhance the overall cybersecurity of connected vehicles.

groups

Ahmed N. Al-Masri mail -

Hamam Mokayed mail

link https://doi.org/10.54216/JCIM.080203

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2023

A Study on Compact Operators in Locally K -Convex Spaces

In this paper we give an equivalent definition of continuous and compact linear operators by using orthogonal bases in non-archimedean locally K - convex spaces. We also show that if E is a  space and F is a semi-Montel  space, then every continuous linear operator T:E→F is compact.

groups

Karla Zayood mail

link https://doi.org/10.54216/GJMSA.050201

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2023

On a Novel Generalization of p-Quasi-λ-Nuclear Operators

In this paper we generalize the concept of 2-quasi -- nuclear operators between Normed spaces to -quasi--nuclear operators between locally convex spaces and we study the relationship between p-quasi-- nuclear, nuclear operators, -nuclear, quasi-nuclear and quasi-- nuclear. Also, we prove that the composition of two operators, one of them is a -quasi--nuclear, is again a p-quasi--nuclear operator.

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Othman Al-basheer mail

link https://doi.org/10.54216/GJMSA.050202

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2023

The Intersections Based on Joint Observables In Fuzzy Probability

“Fuzzy probability theory” appeared as a smooth extension of classical probability theory in 1995. It was expected that it will be of great importance in quantum mechanics, but the theory doesn’t keep its development as it was expected. This necessitates revising some of its fundamental basic concepts. We argue that if quantum probability theory should have less constrained than classical probability theory as can be seen in the case of joint random variables, we surely need to weaken the definition of the intersection operation. In this paper, discuss the definition validity in quantum probability theory and to discuss the consistency of the given definitions with the whole theory and the possibility to have a more suitable definition.

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Murat Ozcek mail

link https://doi.org/10.54216/GJMSA.050203

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2021

The Cost of Progress: Exploring Privacy Nightmares for AI in Precision Medicine

Precision medicine is an innovative approach to healthcare that relies on the use of genomic data, electronic health records, and other types of medical data to develop personalized prevention, diagnosis, and treatment strategies for patients. The use of artificial intelligence (AI) in precision medicine has the potential to improve patient outcomes and reduce healthcare costs, but it also raises significant privacy concerns. This paper provides a comprehensive review of the privacy nightmares associated with the use of AI in precision medicine. We examine the potential risks and threats to patient privacy, including the use of personal data for unintended purposes, the risk of data breaches and hacking, and the potential for discrimination and bias. We also analyze the legal and ethical implications of using AI in precision medicine, including issues related to informed consent and data ownership. Our investigation highlights the need for strong data protection regulations and ethical frameworks to safeguard patient privacy in the age of AI in precision medicine. As the use of AI in precision medicine continues to expand, the paper presents a road for future directions for protecting patient privacy, including the use of privacy-preserving machine learning algorithms and the adoption of privacy-enhancing technologies.

groups

Ahmed Aziz mail -

Noura Metawa mail

link https://doi.org/10.54216/JCIM.080205

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new