Rubina Akter

• PhD
• Post-doctoral Research Fellow at University of British Columbia

The heterogeneous connections in metaverse environments pose vulnerabilities to cyber-attacks. To prevent and mitigate malicious network activities in a distributed metaverse, conventional intrusion detection systems (IDS) have communication overhead and privacy concerns. Federated learning (FL) techniques are widely employed to develop IDS framewo...

This paper proposes a whale optimization algorithm (WOA)‐based partial transmit sequence (PTS) scheme called WOA‐PTS to reduce the high peak‐to‐average power ratio (PAPR) for universal filtered multi‐carrier (UFMC) systems. High PAPR is a prevalent challenge encountered in multi‐carrier systems. In the conventional PTS technique, the optimization o...

Unmanned aerial systems, namely drones, have greatly improved and expanded drastically over the years. Due to their efficiency and ease of use, drones have been utilized in a wide range of applications. Despite various potential uses, drones are also being utilized for illegal operations and exposing security threats to citizens. It is vital to ins...

Unauthorized users may attack centralized controllers as an attractive target in software-defined networking (SDN)-based industrial cyber-physical systems (CPS). Managing high-complexity deep learning (DL)-based intrusion classification to recognize and prevent attacks in the industrial Internet of Things (IIoT) networks with low-latency requiremen...

Malware is an envelope of various malicious software or programs which hackers use to harm and attack computer systems. Malware detection is the procedure of identifying the computer vulnerability and tracing the malicious files to discover the malware and, thus, secure the computing system. However, detecting and classifying the malware manually i...

The deployment of unmanned aerial vehicles (UAV) for logistics and other civil purposes is consistently disrupting airspace security. Consequently, there is a scarcity of datasets for the development of real-time systems that can checkmate the incessant deployment of UAVs in carrying out nefarious activities. This succinct work fills this void by p...

Software-Defined Networking (SDN)-based Industrial Internet of Things (IIoT) networks have a centralized controller that is a single attractive target for unauthorized users to attack. Cybersecurity in IIoT networks is becoming the most significant challenge, especially from increasingly sophisticated Distributed Denial-of-Service (DDoS) attacks. T...

The surging proliferation in the deployment of unmanned aerial vehicles (UAVs) in various domains has resulted into unsolicited intrusion into private properties and protected areas thereby posing threat to national security. This paper proposed an adaptive scenario-based approach for detecting drone invasion using enhanced YOLOv5 deep learning mod...

Unmanned aerial vehicle (UAV) contributes substan�tial strategic benefits on the Internet-of-military-things (IoMT). However, the untrusted party’s misuse of the UAV may violate the security and even demolish the critical operation in the IoMT system. In addition, data manipulation and falsification using unauthorized access are the significant cha...

In any warfare, success is a function of innovative tactics and strategic deployment of limited resource. This paper develops a lightweight deep neural network that can track and disarm illegal invasion of a territory by drones using radio frequency technology. The dataset consist of RF signals generated from 17 drones at different instances and so...

As most countries relax restrictions on lockdown and social activities returns due to massive response to COVID-19 vaccination, there is need to put in place a universally acceptable technological innovation that can checkmate and enforce compliance to avoid resurgence of another deadly wave as witnessed previously. Combining vaccination effort wit...

In recent years, the availability of commercial unmanned air vehicles (UAVs) has increased enormously because of device miniaturization and low cost. However, the abuse of UAVs needs to be investigated to prevent serious security threats for civilians. Therefore, this paper presents a convolutional neural network-based surveillance system for drone...

This paper provides a noble direction for guaranteeing safety of airspace for the use of drones in various transportation and logistics services in a smart city. Yolov5 deep learning model was used for visual drone detection and payload identification. The model showed 99.9% drone detection accuracy in less time but misidentified some of the attach...

This paper presents a convolution neural network (CNN)-based direction of arrival (DOA) estimation method for radio frequency (RF) signals acquired by a nonuniform linear antenna array (NULA) in unmanned aerial vehicle (UAV) localization systems. The proposed deep CNN, namely RFDOA-Net, is designed with three primary processing modules, such as col...

The building block of internet-of-things (IoT) follows the routing standardization of low power and lossy networks known as RPL for maintaining the routing constraints. The trickle timer algorithm is one of the major components of RPLthat controls and maintains the routing frequency through suppression and traffic frequency adaptation mechanism. Ho...

Solar irradiance prediction is an indispensable area of the photovoltaic (PV) power management system. However, PV management may be subject to severe penalties due to the unsteadiness pattern of PV output power that depends on solar radiation. A high-precision long short-term memory (LSTM)-based neural network model named SIPNet to predict solar i...