Efficient Security and AI Defence Mechanisms in Cyber Networks
Farhan Nisar, Baseer Ali Rehman, Sana Shafiq, Shum Yee Chan, Rabina Safi
Journal of Information Security Research, Vol. 16, No. 4, 2025.
DOI: https://doi.org/10.6025/jisr/2025/16/4/137-149
Link: https://www.dline.info/jisr/fulltext/v16n4/jisrv16n4_1.pdf
Abstract:
The document examines cloud computing security challenges, emphasizing that misconfigurations, weak
identity controls, insecure APIs, and DoS/DDoS attacks are among the most critical vulnerabilities in modern
cloud environments. It highlights that human error not platform flaws is the primary cause of breaches,
underscoring the need for robust policy based defenses. The study's main contribution involves analyzing
various DoS attack types (volumetric, protocol based, and application layer), enhancing edge router security
through ACLs, rate limiting, and deep packet inspection, and validating these measures in a GNS3 network
simulation environment. Key experiments demonstrate that disabling Cisco Discovery Protocol (CDP), enabling
DHCP snooping, and applying port security effectively mitigate ICMP floods, rogue DHCP servers, and
reconnaissance threats. The paper validates established best practices such as those from Cisco rather than
proposing novel cryptographic or architectural solutions. While results show 100% mitigation of specific
attacks under controlled conditions, limitations include the lack of real world deployment, the absence of AI
despite the title's implication, a narrow threat scope, and a simplified network topology. Future work
recommends testing in live multi cloud infrastructures, integrating AI driven anomaly detection for adaptive
policy enforcement, and developing context aware threat models for hybrid cloud ecosystems. Overall, the
research provides practical, simulation backed evidence that foundational Layer 2/3 security configurations
significantly improve resilience against common network layer threats in cloud infrastructures.