International Journal of Innovative Technology and Interdisciplinary Sciences

Mapping ISP Malware Trends in Albania: Clustering for Smarter Cyber Defences

2025-09-03T23:52:17+02:00

Cybersecurity plays a vital role in protecting digital infrastructure, with Internet Service Providers (ISPs) standing at the core of this ecosystem. This research takes an exploratory perspective, given the limitations of both the dataset and the number of available features. The analysis draws on malware detection data from Albania, reported through the Shadow Server platform, covering a 15-month period across seven ISPs. By applying time-series clustering alongside statistical methods, the study groups ISPs according to their security patterns. The time-series analysis points to three distinct periods of heightened malware activity, while the characteristics-based approach identifies three groups of ISPs that differ in their vulnerability profiles. Taken together, these results underline the need for customized cybersecurity strategies and stronger cooperation among ISPs. Despite the constraints of a relatively small dataset, clustering techniques prove useful for optimizing resources, supporting regulatory compliance, and informing strategic decisions aimed at more effective threat prevention and mitigation.

Fusion of Blockchain, IoT, Artificial Intelligence, and Robotics for Efficient Waste Management in Smart Cities

2025-09-08T16:54:41+02:00

Rapid urbanization and population growth are accelerating waste generation in cities worldwide, posing serious environmental and socio-economic challenges. Traditional waste management systems, often centralized and infrastructure-deficient, struggle with inefficiencies, unscheduled collection, and a lack of real-time data. These limitations hinder progress toward smart and sustainable urban environments. Blockchain, the Internet of Things (IoT), Artificial Intelligence (AI), and Robotics are reshaping waste collection, sorting, and recycling. This review examines how these technologies integrate to create secure, efficient, and sustainable waste management in smart cities. An analysis of 184 studies published between January 2022 and July 2025 reveals key shortcomings in conventional waste management systems and showcases the benefits of smart waste management solutions. The results showed that cities are already using IoT-enabled smart bins, AI-driven route optimization, Blockchain for waste tracking, and robotic sorting. However, challenges such as data privacy concerns, limited Blockchain scalability, system interoperability gaps, sensor reliability issues, and high computational demands limit broader adoption. The review outlines future research priorities, including AI-powered waste forecasting, swarm robotics, real-time edge computing, and enhanced cybersecurity. By providing a roadmap for technological innovation and integration, this study supports policymakers, urban planners, and industry leaders in developing intelligent, cost-effective, and environmentally resilient waste management systems.

SCP-IoT: Enhancing IoT Communication Security Against Routing Attacks

2025-09-09T20:45:11+02:00

The Internet of Things (IoT) needs to be protected while in transmission. Insecure Internet of Things equipment connectivity can direct to security breaches. As a result, third parties can get access and make changes in order to cause problems for things connected in the system. In order to address these difficulties, the IoT communication security needs to be addressed. A new strategy named "secure communication utilising cryptographic approaches for IoT" was presented in this research to deal with this problem. There are three parts to the model, which is called the "safe communication protocol for IoT." First, the initiator sends a connection request to the respondent with the source identification and a true cryptography nonce to initiate the communication. Secondly, the responder examines the nonce's freshness and the source's identity when it receives a request. After that, the responder uses KDH to compute and deliver the MAC result for the SRC ID as part of the Finish message to the initiator. Few current strategies, including developing constrain fuzzy routing principles, were evaluated and compared to the proposed model. Prior to this study, the most important metrics were the MLR and MDR ratios, the spectrum utilisation rate, the network lifetime, and the utilisation rate.

Developing a Conceptual Framework for Soil Property Analysis and Crop Yield Prediction Using Machine Learning Techniques

2025-09-12T22:45:08+02:00

The most important single factor is soil fertility which influence crop sustainability and agricultural productivity. The necessity to use data-driven approaches to assess the health of the soil and propose the crops that should be grown in it has become a crucial issue because the accuracy of agriculture is required increasingly frequently. Based on the dataset of the Soil Health Card (SHC) of the Government of India, the presented study provides a conceptual framework that involves the application of the machine learning approaches to analyse soil characteristics and predict its agricultural productivity. The framework is based on twelve important soil parameters: sulphur (S), nitrogen (N), zinc (Zn), phosphorus (P), electrical conductivity (EC), potassium (K), manganese (Mn), copper (Cu), boron (B), iron (Fe), organic carbon (OC), and pH to cluster soil samples into the categories of low, medium, and high soil fertility by using the K-means algorithm. To suggest the correct crops that must be grown in each of the fertility categories, the Random Forest Classifier is then trained after the clustering. The model is checked by K-Fold cross-validation (k=5) and Holdout (80/20 split) to make sure that in unseen data strong generalization will be achieved. An average performance of 91 percent in K-Fold, and zero in holdout validation showing no inaccuracies in dividing the test set and an RMSE and MAE also zero, results indicate high performance and no mistakes in classification. Also, the proposed methodology enhances the agronomic decision-making with the help of AI-based crop proposals targeting each of the fertility classes. This study is an indication of the efficiency of the integration of supervised and unsupervised methods in agricultural informatics. It attracts interest in how intelligent models can high-grade the use of resources, encourage sustainable agriculture and endow growers with useful information based on real-life DO data.