arXiv subjects

Muhammad Mubeen

Publications and source records attributed to Muhammad Mubeen.

4 recordsLinked to original sources

arXiv2026

Strategies to Avoid Illegal Data Access

For companies of all sizes, data security is a top priority. The chance of unauthorized data access increases as technology develops. To prevent unwanted access to their data, businesses must be proactive. This study examines technology solutions, personnel training, and policy enforcement as methods to prevent unauthorized data access. Data may be protected from illegal access using technological solutions like firewalls, intrusion detection systems, and encryption. Intrusion detection systems notify the administrator when suspicious behavior is found, while firewalls serve as a protective border between the internal network and the internet. Even if data is intercepted, encryption makes sure it is safe. Another effective method of avoiding unauthorized data access is employee education. Employees must be taught how to spot hazards like phishing emails and shady websites and react to them. Additionally, they should be taught the right way to utilize passwords and other security precautions. To secure data, organizations should create and implement policies. Policies should set out appropriate data and system use guidelines and provide repercussions for noncompliance. Policies should be evaluated regularly to ensure that they are current and useful. Businesses may prevent unwanted access to their data by installing technology solutions, training staff, and enforcing regulations. Organizations may reduce data breach risk and maintain regulatory compliance by taking these precautions. ...

cs.CR↗

arXiv2024

1D-CNN-IDS: 1D CNN-based Intrusion Detection System for IIoT

The demand of the Internet of Things (IoT) has witnessed exponential growth. These progresses are made possible by the technological advancements in artificial intelligence, cloud computing, and edge computing. However, these advancements exhibit multiple challenges, including cyber threats, security and privacy concerns, and the risk of potential financial losses. For this reason, this study developed a computationally inexpensive one-dimensional convolutional neural network (1DCNN) algorithm for cyber-attack classification. The proposed study achieved an accuracy of 99.90% to classify nine cyber-attacks. Multiple other performance metrices have been evaluated to validate the efficacy of the proposed scheme. In addition, comparison has been done with existing state-of-the-art schemes. The findings of the proposed study can significantly contribute to the development of secure intrusion detection for IIoT systems.

cs.CR↗

arXiv2024

A Single Channel-Based Neonatal Sleep-Wake Classification using Hjorth Parameters and Improved Gradient Boosting

Sleep plays a crucial role in neonatal development. Monitoring the sleep patterns in neonates in a Neonatal Intensive Care Unit (NICU) is imperative for understanding the maturation process. While polysomnography (PSG) is considered the best practice for sleep classification, its expense and reliance on human annotation pose challenges. Existing research often relies on multichannel EEG signals; however, concerns arise regarding the vulnerability of neonates and the potential impact on their sleep quality. This paper introduces a novel approach to neonatal sleep stage classification using a single-channel gradient boosting algorithm with Hjorth features. The gradient boosting parameters are fine-tuned using random search cross-validation (randomsearchCV), achieving an accuracy of 82.35% for neonatal sleep-wake classification. Validation is conducted through 5-fold cross-validation. The proposed algorithm not only enhances existing neonatal sleep algorithms but also opens avenues for broader applications.

cs.LG↗

arXiv2024

A Deep Features-Based Approach Using Modified ResNet50 and Gradient Boosting for Visual Sentiments Classification

The versatile nature of Visual Sentiment Analysis (VSA) is one reason for its rising profile. It isn't easy to efficiently manage social media data with visual information since previous research has concentrated on Sentiment Analysis (SA) of single modalities, like textual. In addition, most visual sentiment studies need to adequately classify sentiment because they are mainly focused on simply merging modal attributes without investigating their intricate relationships. This prompted the suggestion of developing a fusion of deep learning and machine learning algorithms. In this research, a deep feature-based method for multiclass classification has been used to extract deep features from modified ResNet50. Furthermore, gradient boosting algorithm has been used to classify photos containing emotional content. The approach is thoroughly evaluated on two benchmarked datasets, CrowdFlower and GAPED. Finally, cutting-edge deep learning and machine learning models were used to compare the proposed strategy. When compared to state-of-the-art approaches, the proposed method demonstrates exceptional performance on the datasets presented.

cs.CV↗