A Deep Learning Approach to Detect Zero-Day Attacks Within Iot Networks (Published)

IoT networks contend with emerging threats where signature evasion zero-day attacks emerge faster than traditional methods can keep up with, and where traditional IDSes treat known-attack-classification and unknown-attack-detection as two disjoint problems at the expense of doubling memory footprint, latency, and maintenance overhead on resource-limited IoT gateways. In this work we introduce CBLA, a single deep-learning pipeline leveraging a CNN–BiLSTM–Attention encoder pretrained with an autoencoder that simultaneously addresses both tasks with a softmax attack-classification head and attack-reconstruction decoder fed from shared latent encoder activations in a single forward pass. We benchmark CBLA on the 34-class split of the CIC-IoT-2023 dataset which evenly divides its 19 known-attack and 15 zero-day attack classes across four high-level attack families designed to have maximally different structural characteristics (reconnaissance, web injection, malware, MITM). Following an unsupervised autoencoder pretraining stage, CBLA is jointly fine-tuned in a supervised manner, and zero-day alerts are raised based on reconstruction-error using a Youden-J ROC-optimal threshold determined on a held-out validation dataset. CBLA achieves 99.89% classification accuracy, and outperforms all existing single-pipeline solutions with a zero-day AUC-ROC of 0.972 and true-positive rate of 94.22%. We further ablate four model variants of CBLA to determine individual contributions of each architectural choice.

Keywords: CNN-BiLSTM, anomaly detection, autoencoder, cic-IoT-2023, deep learning, internet of things, intrusion detection system, network security, self-attention, zero-day attack detection

The Transformative Impact of IoT on the Insurance Industry (Published)

The Internet of Things (IoT) is fundamentally transforming the insurance industry by enabling real-time data collection through connected devices, including telematics, wearables, and smart home systems. This technological integration is shifting the insurance paradigm from a reactive, transaction-based model focused on loss compensation to a proactive partnership centered on risk prevention and ongoing customer engagement. The paper examines how IoT enhances three critical dimensions of insurance operations: risk assessment, claims processing, and customer engagement. In risk assessment, IoT provides granular behavioral data that enables personalized pricing and encourages safer practices. For claims processing, connected devices deliver immediate, objective incident data that accelerates verification, reduces fraud, and streamlines settlement. In customer engagement, IoT creates unprecedented opportunities for continuous interaction through personalized guidance, proactive risk alerts, and incentive programs that reward risk-reducing behaviors. Despite significant implementation challenges related to data privacy, system integration, the digital divide, and regulatory compliance, the transformative potential of IoT in insurance is substantial. The convergence of IoT with artificial intelligence promises to revolutionize the industry, enabling increasingly sophisticated risk modeling, automated operations, and entirely new insurance products tailored to specific use cases and risk profiles.

Keywords: Behavioral Pricing, Claims Automation, Customer Engagement, Risk Assessment, internet of things

Review on LoRa Communication Technology, Its Issues, Challenges and Applications in Healthcare System (Published)

The Internet of Things (IoT) has transformed various industries by enabling interconnected devices to collect, share, and analyze data in real-time. A crucial component of this transformation is Long Range (LoRa) technology, designed for low-power, wide-area networks (LPWAN). LoRa enables long-distance communication with minimal energy consumption, making it ideal for IoT applications in sectors like healthcare, agriculture, and smart cities. Specifically, in healthcare, LoRa facilitates remote monitoring through the Internet of Medical Things (IoMT), where patient data, such as vital signs, can be transmitted efficiently over vast distances. This paper discusses the significance of LoRa in medical applications, its advantages, challenges, and solutions, and reviews existing literature on its implementation. Key challenges include low data rates, packet loss, and latency issues, while solutions such as adaptive data rate mechanisms and multi-hop networks offer potential improvements for medical IoT systems. Also based on the given comparison among communication technologies, the consumer can make his decision on chosen the right technology for his application.

Keywords: internet of things, lora, spreading factor

A Smart Campus Internet-of-Things (IoT) Model for Smart Classroom Conditioning Using a Hybridized Technique (Published)

This research study presents Smart Campus (SC) Internet-of-Things (IoTs) enabled systems model that will support end-user and automatic functions for proper air conditioning of SC classrooms environment. It consists of a hybrid data learning predictor system using an emerging variant of Artificial Neural Network (ANN) called Neuronal Auditory Machine Intelligence (NeuroAMI) and a Linear Regressor (LR) of polynomial-order-of-1.The system was initially applied separately to the automated coordination of a smart bed in a laboratory sized classroom environment at a University Campus, and simulated using the high-level programming language – MATLAB, while end user interaction model was developed in the Java2ME programming language. Simulations results considering several trial runs showed that the ANN predictor generally performed better than the LR model with over 80% classification accuracy. While considering limited training data points, the LR predictor was found to be superior at one of the simulation trial runs. At 20% data point, the LR was activated while the NeuroAMI remains inactive, but above the 20% level, the NeuroAMI performed better. One advantage of this proposed hybrid system is the ability to deal with continuous data; exactly the same way human brains functions. This feat has not been possible in conventional ANN systems, especially in this area dealing with small data points.

Keywords: artificial neural network (ANN), internet of things, linear regressor, neuronal auditory machine intelligence, predictor system, smart campus

A Review on Distributed Denial-of-Service Attacks on Internet of Things (Published)

The term IoT (Internet of Things) refers to physical things or objects having different types of sensors, ability to process, software and other technologies that helps to connect and exchange data with other systems over the internet. Whether it comes to simple coffee machine or big things like car or health care, agriculture, smart cities etc., IoT has developed a person’s living with his minimal involvement. Since, these IoT devices and other components used with it are having less memory, less computational capability makes them vulnerable to many types of attacks. The most common type of attack that takes place on it is DoS/DDoS, where an authorized user is restricted from accessing some service on internet. This paper focuses on security requirements at different IoT layers, issues related to DDoS attack and provides review on its countermeasures.

Keywords: DDoS, DoS, Security, internet of things