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p-Index From 2021 - 2026
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All Journal International Journal of Electrical and Computer Engineering
Nur, Fernaz Narin
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Computer Science & IT Electrical & Electronics Engineering

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IDPS: A machine learning framework for real-time intrusion detection and protection system for malicious internet activity Fabiha, Raisa; Reberio, Stein Joachim; Farazi, Zubayer; Nur, Fernaz Narin; Sultana, Shaheena; Islam, A. H. M. Saiful
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp437-449

Abstract

With the increasing frequency and complexity of cyber threats, there is a pressing need for effective real-time solutions to detect and prevent malicious activities. This study introduces a novel machine learning-based architecture for real-time cybersecurity to enhance accurate identification and prevention of malicious cyber activities. The proposed framework combines advanced machine learning algorithms with Wireshark network traffic analysis to effectively detect and classify a wide range of cyberattacks, providing timely and actionable insights to cybersecurity professionals. A core component of this system is a prototype blocker, which is seamlessly integrated with Cisco infrastructure, enabling proactive intervention by blocking suspicious IP addresses in real-time. In addition, a user-friendly web application enhances system operability by offering intuitive data visualization and analytical tools, enabling rapid and informed decision-making. This comprehensive approach not only strengthens network security and protects digital assets but also equips defenders with the capability to respond effectively to the dynamic landscape of cyber threats.
Sentiment analysis on Bangla conversation using machine learning approach Hassan, Mahmudul; Shakil, Shahriar; Moon, Nazmun Nessa; Islam, Mohammad Monirul; Hossain, Refath Ara; Mariam, Asma; Nur, Fernaz Narin
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5562-5572

Abstract

Nowadays, online communication is more convenient and popular than face-to-face conversation. Therefore, people prefer online communication over face-to-face meetings. Enormous people use online chatting systems to speak with their loved ones at any given time throughout the world. People create massive quantities of conversation every second because of their online engagement. People's feelings during the conversation period can be gleaned as useful information from these conversations. Text analysis and conclusion of any material as summarization can be done using sentiment analysis by natural language processing. The use of communication for customer service portals in various e-commerce platforms and crime investigations based on digital evidence is increasing the need for sentiment analysis of a conversation. Other languages, such as English, have well-developed libraries and resources for natural language processing, yet there are few studies conducted on Bangla. It is more challenging to extract sentiments from Bangla conversational data due to the language's grammatical complexity. As a result, it opens vast study opportunities. So, support vector machine, multinomial naïve Bayes, k-nearest neighbors, logistic regression, decision tree, and random forest was used. From the dataset, extracted information was labeled as positive and negative.
Predicting the mental health of rural Bangladeshi children in coronavirus disease 2019 Moon, Nazmun Nessa; Hossain, Refath Ara; Jahan, Israt; Shakil, Shahriar; Uddin, Shihab; Hassan, Mahmudul; Nur, Fernaz Narin
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5501-5510

Abstract

The novel coronavirus disease 2019 (COVID-19) current pandemic is a worldwide health emergency like no other. It is not the only COVID-19 infection in infants, children, and adolescents that is causing concern among their families and professionals; there are also other serious issues that must be carefully detected and addressed. Major things are identified due to COVID-19, some elements are affecting children’s healthcare in direct or indirect ways, affecting them not just from a medical standpoint but also from social, psychological, economic, and educational perspectives. All these factors may have affected children’s mental development, particularly in rural settings. As Bangladesh faces a major challenge such as a lack of public mental health facilities, especially in rural areas. So, we discovered a method to predict the mental development condition of rural children that they are facing at this time of COVID-19 using machine learning technology. This research work can predict whether a rural child is mentally developed or mentally hampered in Bangladesh and this prediction gives nice feedback.
Co-Authors Fabiha, Raisa Farazi, Zubayer Hassan, Mahmudul Hossain, Refath Ara Islam, A. H. M. Saiful Islam, Mohammad Monirul Jahan, Israt Mariam, Asma Moon, Nazmun Nessa Reberio, Stein Joachim Shakil, Shahriar Sultana, Shaheena Uddin, Shihab