Md Faisal Ahmed

Tahsin Ahmed, Arjita Saha, Arian Nuhan et al.

The recent surge in security concerns for IoT devices highlights the increasing threat of cryptographic vulnerabilities. These weaknesses can lead to unauthorized access, data breaches, and manipulation of device functions, compromising the privacy and security of both the devices and their users. Given the limited computational power of IoT devices, especially when handling large amounts of data, encrypting and transmitting data over insecure networks poses significant challenges. This situation not only heightens security risks and prolongs runtime, but also degrades performance and consumes more resources. To address these issues, a novel cryptographic system named HELO (Hybrid Encryption Lightweight Optimization) is proposed. It is hybridized and gives solid security against cryptographic cyberattacks. However, the research objective is to enhance the security level of IoT devices without decreasing their performance. This system is ideal for resource-constrained gadgets due to its lightweight mechanism. Finally, it offers top-level cryptographic security for IoT gadgets by guaranteeing confidentiality, integrity, and availability while doing P2P data transmission.

Md Faisal Ahmed, Zalish Mahmud, Zarin Tasnim Biash et al.

Cyberbullying or Online harassment detection on social media for various major languages is currently being given a good amount of focus by researchers worldwide. Being the seventh most speaking language in the world and increasing usage of online platform among the Bengali speaking people urge to find effective detection technique to handle the online harassment. In this paper, we have proposed binary and multiclass classification model using hybrid neural network for bully expression detection in Bengali language. We have used 44,001 users comments from popular public Facebook pages, which fall into five classes - Non-bully, Sexual, Threat, Troll and Religious. We have examined the performance of our proposed models from different perspective. Our binary classification model gives 87.91% accuracy, whereas introducing ensemble technique after neural network for multiclass classification, we got 85% accuracy.

Md Faisal Ahmed, Zalish Mahmud, Zarin Tasnim Biash et al.

Being the seventh most spoken language in the world, the use of the Bangla language online has increased in recent times. Hence, it has become very important to analyze Bangla text data to maintain a safe and harassment-free online place. The data that has been made accessible in this article has been gathered and marked from the comments of people in public posts by celebrities, government officials, athletes on Facebook. The total amount of collected comments is 44001. The dataset is compiled with the aim of developing the ability of machines to differentiate whether a comment is a bully expression or not with the help of Natural Language Processing and to what extent it is improper if it is an inappropriate comment. The comments are labeled with different categories of harassment. Exploratory analysis from different perspectives is also included in this paper to have a detailed overview. Due to the scarcity of data collection of categorized Bengali language comments, this dataset can have a significant role for research in detecting bully words, identifying inappropriate comments, detecting different categories of Bengali bullies, etc. The dataset is publicly available at https://data.mendeley.com/datasets/9xjx8twk8p.