Daily Archives: December 14, 2024

Authors: Dr. Kalpana Singh

Abstract: Artificial light at night (ALAN) has become the defining feature of the urban environment, transforming natural nocturnal landscapes by streetlights, commercial lighting, illuminated buildings and other artificial sources. While artificial lighting is essential for human activity (both safety and economic), it is not well-integrated in urban biodiversity management. This paper critically considers the role of artificial illumination as an underestimated factor in the decline of urban insects and its ecological impact through a review of existing literature and presents evidence on insect attraction and disorientation, circadian disturbances, impaired reproduction, feeding behaviour, and interactions between different species. This paper particularly focuses on ecological implications of these impacts for nocturnal pollination, trophic relationships, and urban ecosystems. There are existing studies that show that artificial lighting may affect nocturnal pollinator behaviour, community composition, and the abundance of insects beyond the immediate vicinity of light sources. Yet the scope and effects of artificial lighting are different in terms of insect habitats and species in terms of light intensity, spectral composition, habitat characteristics, and exposure duration. Artificial light is no longer a nuisance and should not be treated as just a nuisance to humans but rather as a potentially critical environmental threat that is also part and parcel of habitat loss and chemical pollution in relation to climate change. This report also highlights the need for biodiversity-sensitive lighting policies based on darkness if ecologically relevant, reduce unnecessary light, and incorporate nocturnal biodiversity in urban planning. By linking individual behaviour to more macro-economic and environmental impact, artificial light at night is a key but not well-understood aspect of urban environmental change.

DOI: https://doi.org/10.5281/zenodo.23362894

Published by: vikaspatanker

Detection of DDOS Attacks and Classification/strong>
Authors:-Gopi A G, Professor Dr. M Anand Kumar

Abstract-Distributed Denial of Service (DDoS) attacks are a significant threat to the stability and availability of network services, often resulting in financial and reputational damage to organizations. Detecting and mitigating these attacks is a complex task due to their large scale, diverse attack vectors, and evolving nature. This paper explores various methods for DDoS attack detection and classification, with a focus on leveraging machine learning and statistical techniques. The primary objective is to identify attack patterns in network traffic data and classify them in real-time to distinguish between legitimate and malicious activities. We review traditional methods such as signature-based detection and anomaly detection, alongside modern machine learning-based approaches, including supervised and unsupervised classification techniques. Machine learning algorithms, such as decision trees, support vector machines, and neural networks, are evaluated for their effectiveness in detecting various types of DDoS attacks, including volumetric, protocol, and application-layer attacks. Additionally, we discuss the challenges posed by high traffic volumes, the need for low-latency detection, and the impact of adversarial tactics on detection systems. Finally, the paper highlights the importance of developing robust, scalable, and adaptive classification models that can efficiently handle the evolving nature of DDoS attacks in dynamic network environments.

DOI: 10.61137/ijsret.vol.10.issue6.425

Published by: Kajal Tripathi