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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Jaganathan, Suresh
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Computer Science & IT Engineering

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Adaptive Bayesian contextual hyperband: A novel hyperparameter optimization approach Swaminatha Rao, Lakshmi Priya; Jaganathan, Suresh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp775-785

Abstract

Hyperparameter tuning plays a significant role when building a machine learning or a deep learning model. The tuning process aims to find the optimal hyperparameter setting for a model or algorithm from a pre-defined search space of the hyperparameters configurations. Several tuning algorithms have been proposed in recent years and there is scope for improvement in achieving a better exploration-exploitation tradeoff of the search space. In this paper, we present a novel hyperparameter tuning algorithm named adaptive Bayesian contextual hyperband (Adaptive BCHB) that incorporates a new sampling approach to identify best regions of the search space and exploit those configurations that produce minimum validation loss by dynamically updating the threshold in every iteration. The proposed algorithm is assessed using benchmark models and datasets on traditional machine learning tasks. The proposed Adaptive BCHB algorithm shows a significant improvement in terms of accuracy and computational time for different types of hyperparameters when compared with state-of-the-art tuning algorithms.
Ledger on internet of things: a blockchain framework for resource-constrained devices Jaganathan, Suresh; Veeramani, Karthika
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2506-2518

Abstract

The increasing use of resource-constrained devices such as the internet of things (IoT) in various applications has led to the need for an optimized blockchain framework for these devices. Blockchain-based IoT networks allow businesses to access and share IoT data within their organization without centralized authority. However, existing frameworks are not designed for IoT applications and lack features like decentralization, scalability, and network overhead. To overcome these limitations, a new blockchain framework is proposed: ledger on internet of things (LIoT), which has a new consensus-based leader election algorithm to address the challenges of existing algorithms with high block creation time and communication overhead. Moreover, a novel data structure has been developed to reduce the storage size of the ledger effectively. The proposed framework also employs a docker for deployment, which provides an efficient and easy setup of blockchain nodes without requiring the individual configuration of each machine, increases the efficiency of the consensus process, and enables convenient deployment and management of the blockchain framework on resource-constrained devices. Furthermore, the performance of the proposed consensus method is analyzed using various performance parameters, including CPU usage, memory usage, transaction execution time, and block generation time.
Design and analysis of reinforcement learning models for automated penetration testing Jaganathan, Suresh; Latha, Mrithula Kesavan; Dharanikota, Krithika
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4061-4073

Abstract

Our paper proposes a framework to automate penetration testing by utilizing reinforcement learning (RL) capabilities. The framework aims to identify and prioritize vulnerable paths within a network by dynamically learning and adapting strategies for vulnerability assessment by acquiring the network data obtained from a comprehensive network scanner. The study evaluates three RL algorithms: deep Q-network (DQN), deep deterministic policy gradient (DDPG), and asynchronous episodic deep deterministic policy gradient (AE-DDPG) in order to compare their effectiveness for this task. DQN uses a learned model of the environment to make decisions and is hence called model-based RL, while DDPG and AE-DDPG learn directly from interactions with the network environment and are called model-free RL. By dynamically adapting its strategies, the framework can identify and focus on the most critical vulnerabilities within the network infrastructure. Our work is to check how well the RL technique picked security vulnerabilities. The identified vulnerable paths are tested using Metasploit, which also confirmed the accuracy of the RL approach's results. The tabulated findings show that RL promises to automate penetration testing tasks.
Co-Authors Dharanikota, Krithika Latha, Mrithula Kesavan Swaminatha Rao, Lakshmi Priya Veeramani, Karthika