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Dhanna Ram Dhanna Ram

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Open access Jul 2026

Adaptive Load Balancing in Computer Networks Using AI

With the rapid growth of cloud computing, data centers, and high-speed networks, efficient load balancing has become a critical requirement for ensuring optimal network performance and quality of service. Traditional load balancing techniques such as round-robin and least-connection methods rely on static rules and fail to adapt to dynamic network conditions like fluctuating traffic, node failures, and varying resource availability. This often leads to congestion, increased latency, and underutilization of network resources. This work proposes an Adaptive Load Balancing approach using Artificial Intelligence (AI) to dynamically distribute network traffic across multiple servers or network nodes. By continuously learning from network conditions such as traffic load, response time, and bandwidth usage, the AI-based system makes intelligent routing decisions in real time. The proposed system is implemented using Python, simulating network environments and applying machine learning algorithms to achieve efficient, scalable, and self-adaptive load balancing in computer networks. Keywords— Adaptive Load Balancing, Artificial Intelligence in Networking, AI-Based Load Balancing,Computer Network Optimization

Pooja Verma Pooja Verma, Dhanna Ram Dhanna Ram, Mohit kumar saini Mohit kumar saini · 0 citations
Jul 2026

ML–Based Intrusion Detection System for Computer Networks

Modern computer networks face a persistent and growing range of cyber threats, including unauthorized access, malware infiltration, denial-of-service attacks, and data exfiltration. Conventional signature- and rule-based intrusion detection systems (IDS) struggle to identify zero-day attacks and evolving intrusion patterns as networks expand in scale and complexity, and they typically require frequent manual updates to remain effective. This paper presents a machine learning–based intrusion detection system capable of automatically analyzing network traffic and distinguishing malicious activity from legitimate behavior. By learning from historical traffic data, the proposed system adapts to emerging threats with greater accuracy than static, rule-driven approaches. The system is implemented in Python using established data-analytics and machine-learning libraries, and it evaluates several supervised classifiers—Random Forest, Support Vector Machine, Naïve Bayes, and Logistic Regression—to categorize network traffic as normal or intrusive. Experimental results show that the Random Forest classifier consistently achieves the strongest overall performance, and the integration of real-time packet capture with a visualization dashboard enables continuous, interpretable monitoring. The findings demonstrate that machine learning offers a scalable, adaptive, and largely automated foundation for securing contemporary network infrastructures.

Mohit kumar saini Mohit kumar saini, Dhanna Ram Dhanna Ram, Pooja Verma Pooja Verma · 0 citations