ORCID Profile
0000-0002-0414-5448
Current Organisations
Assiut University
,
Majmaah University
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Publisher: Mary Ann Liebert Inc
Date: 23-05-2023
Publisher: Springer Science and Business Media LLC
Date: 11-10-2021
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2022
Publisher: Association for Computing Machinery (ACM)
Date: 04-01-2023
DOI: 10.1145/3578363
Abstract: The applications and services offered by the Internet of Things (IoT) have grown significantly during the past few years. Device makers and corporate suppliers have taken notice of this, which has led to a sudden inflow of new-age firms. Confidential data and information are involved as IoT device use rises. IoT device security has emerged as a major issue and is becoming more and more significant. Appropriate security measures are needed to prevent dangers and hazards associated with the adoption of smart technology in smart cities and houses that run IoT devices, according to security evaluations. In order to safeguard the smart home environment, our research focuses on IoT device firmware. The security methodology presented in this research may be used to analyze and investigate IoT firmware, revealing sensitive data and hardcoded user IDs and passwords that can be used in future attacks and breach of IoT devices. The authors put out an idea for how real-time datasets produced by IoT search engines may be analyzed using keywords according to different device kinds, locations, and manufacturers. The results showed that it took device owners 11–13 months to upgrade the firmware. Only HP and Cisco routinely provided firmware updates to protect IoT devices among IoT device makers.
Publisher: Springer Science and Business Media LLC
Date: 06-09-2021
Publisher: Wiley
Date: 10-07-2019
DOI: 10.1111/COIN.12230
Publisher: PeerJ
Date: 07-04-2021
DOI: 10.7717/PEERJ-CS.437
Abstract: In today’s cyber world, the demand for the internet is increasing day by day, increasing the concern of network security. The aim of an Intrusion Detection System (IDS) is to provide approaches against many fast-growing network attacks (e.g., DDoS attack, Ransomware attack, Botnet attack, etc.), as it blocks the harmful activities occurring in the network system. In this work, three different classification machine learning algorithms—Naïve Bayes (NB), Support Vector Machine (SVM), and K-nearest neighbor (KNN)—were used to detect the accuracy and reducing the processing time of an algorithm on the UNSW-NB15 dataset and to find the best-suited algorithm which can efficiently learn the pattern of the suspicious network activities. The data gathered from the feature set comparison was then applied as input to IDS as data feeds to train the system for future intrusion behavior prediction and analysis using the best-fit algorithm chosen from the above three algorithms based on the performance metrics found. Also, the classification reports (Precision, Recall, and F1-score) and confusion matrix were generated and compared to finalize the support-validation status found throughout the testing phase of the model used in this approach.
No related grants have been discovered for Ahmed Mohamed.