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Adaptive intrusion detection system with DBSCAN to enhance banking cybersecurity

10.11591/ijict.v15i1.pp247-256
Sathiyaseelan Periyasamy , Anubhav Kumar , Karupusamy Muthulakshmi , Thenmozhi Elumalai , Prabu Kaliyaperumal , Rajakumar Perumal
The accelerating pace of digital transformation in the banking sector has highlighted the critical need for comprehensive cybersecurity strategies capable of countering evolving cyber threats. This study introduces an innovative intrusion detection framework tailored for banking environments, leveraging the CICIDS2017 and CSECICIDS2018 datasets for evaluation and validation. The proposed framework integrates data preprocessing, feature reduction, and advanced attack detection methods to enhance detection accuracy. A basic autoencoder is utilized for dimensionality reduction, streamlining input data while preserving essential attributes. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is then applied for attack detection, enabling the detection of intricate attack patterns and their classification into specific attack groups. The proposed adaptive intrusion detection system (IDS) framework demonstrates outstanding performance, achieving precision, recall, F1-score, and accuracy rates exceeding 98%. Comparative evaluations against conventional techniques, such as support vector machines (SVM), long short-term memory (LSTM), and K-means, highlight its superiority in terms accuracy and computational efficiency. This research address key challenges, including high-dimensional datasets, class imbalance, and dynamic threat landscapes, offering a scalable and efficient solution to enhance the security of banking operations and enable proactive threat mitigation in the sector.
Volume: 15
Issue: 1
Page: 247-256
Publish at: 2026-03-01

Plant disease sensing using image processing (with CNN)

10.11591/ijict.v15i1.pp93-101
Haresh Rajkumar , Harry Jakin S. , Sudhakar Thirumalaivasal Devanathan , Booapthy Kannan
Plant disease is a significant challenge for agriculture, leading to reduced yield, economic loss, and environmental impact. Leveraging digital photos of plant leaves, convolutional neural networks (CNNs) have emerged as promising tools for disease detection. The methodology involves several steps, including image pre-processing, segmentation, feature extraction using CNNs. Crucially, a diverse dataset comprising images of both healthy and diseased leaves under varying conditions is necessary for training accurate models. Transfer learning, particularly with pre-trained models like ImageNet, can further enhance accuracy, allowing for better performance with fewer training samples. The proposed method demonstrates impressive results, achieving over 95% accuracy, outperforming existing state-of-the-art techniques. This system could serve as a valuable tool for farmers, facilitating timely disease identification and treatment, ultimately leading to increased agricultural yields, reduced financial losses, and the adoption of more sustainable farming practices. Additionally, beyond its practical applications, the proposed system holds promise for advancing sustainable agriculture by promoting environmentally friendly farming methods and contributing to the overall resilience and productivity of agricultural systems.
Volume: 15
Issue: 1
Page: 93-101
Publish at: 2026-03-01

NLP-based fraudulent biomedical news identification using LSTM-SGD deep learning algorithm

10.11591/ijict.v15i1.pp179-188
Siva Dhievaraj , Agusthiyar Ramu
Concern over bio medical fake news is rising, particularly as false information about illnesses, medical procedures, and public health regulations becomes more prevalent. It is essential to recognize such false information, and deep learning (DL) algorithms can offer a potent remedy, especially when paired with sophisticated natural language processing (NLP) methods. This technique improves the model's capacity to ignore frequently used but uninformative terms and concentrate on important terminology. The model's capacity to concentrate on the most pertinent phrases for fake news identification is enhanced by the use of chi-squared, a statistical test that ascertains the dependency between various variables and aids in the removal of unnecessary data. By reducing less significant characteristics to zero, the Lasso approach, a kind of regression, is used for feature selection, guaranteeing that the model only utilizes the most predictive features for classification. A crucial step in getting the data ready for DL models is feature extraction, which turns unprocessed text into numerical data. After the structured data has been analyzed, algorithms like as stochastic gradient descent (SGD), long short-term memory (LSTM) may determine whether or not an article is accurate. The authenticity and dependability of medical information provided across platforms may be ensured by effectively identifying biomedical fake news by fusing DL with sophisticated NLP techniques.
Volume: 15
Issue: 1
Page: 179-188
Publish at: 2026-03-01

Exploratory data analysis and forecasting of dengue outbreaks in Pangasinan using the ARIMA model

10.11591/ijict.v15i1.pp46-56
Patrick Mole , Thelma Palaoag
Dengue fever remains a critical public health concern in tropical countries like the Philippines, with Pangasinan frequently experiencing outbreaks due to favorable environmental conditions for mosquito breeding. Despite ongoing efforts to control the disease, the absence of a reliable forecasting tool limits the ability of health authorities to implement proactive measures. This study developed a forecasting model using the autoregressive integrated moving average (ARIMA) technique, following an initial exploratory data analysis (EDA) to identify trends and patterns in historical dengue case data from 2019 to 2024. The ARIMA model was trained and validated using historical data, capturing seasonal variations and projecting future dengue outbreaks. The evaluation metrics, including mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), indicated that the model achieved an accuracy of approximately 78.3%, suggesting reasonable predictive capability. Forecasts for the year 2025 indicate a potential rise in dengue cases, particularly during peak seasons, aligning with observed historical trends. These predictions offer valuable insights for local health authorities, enabling them to plan targeted interventions, allocate resources efficiently, and mitigate the impact of future outbreaks. The study demonstrates the practical application of time series analysis in public health forecasting and provides a proactive tool tailored for the needs of Pangasinan.
Volume: 15
Issue: 1
Page: 46-56
Publish at: 2026-03-01

Development of machine learning techniques for automatic modulation classification and performance analysis under AWGN and fading channels

10.11591/ijict.v15i1.pp287-301
P. G. Varna Kumar Reddy , M. Meena
Automatic modulation classification (AMC) is essential in modern wireless communication for optimizing spectrum usage and adaptive signal processing. This study explores the use of various machine learning (ML) methods for AMC, focusing on their performance in additive white Gaussian noise (AWGN) and fading channels. This study evaluates of ML classifiers such as support vector machines (SVM), K-nearest neighbors (KNN), decision trees (DT), and ensemble methods with a dataset spanning signalto-noise ratios (SNRs) from -30 dB to +30 dB. Higher-order statistical features including moments and cumulants are used to train the classifiers for AMC. Performance is measured in terms of classification accuracy and computational efficiency across different SNR levels. The findings show that linear SVM, fine KNN, and fine trees consistently achieved high classification accuracy, even at low SNRs. From the analysis, it is observed that linear SVM and fine KNN achieve over 96% accuracy at 0 dB SNR. These classifiers demonstrate significant robustness, maintaining performance in challenging noise conditions. The research highlights the promise of ML techniques in improving AMC, providing a detailed comparison of classifiers and their strengths.
Volume: 15
Issue: 1
Page: 287-301
Publish at: 2026-03-01

Towards efficient fog computing in smart cities: balancing energy consumption and delay

10.11591/ijict.v15i1.pp332-342
Ida Syafiza Md Isa , Nur latif Azyze Mohd Shaari Azyze , Haslinah Mohd Nasir , Vigneswara Rao Gannapathy , Ashwini Jayadevan Naidu
In this work, we propose fog-based energy-delay optimization (F-EDO) approach and benchmark its performance against the cloud-based energydelay optimization (C-EDO) method, focusing on energy consumption and delay. Unlike previous studies that optimize energy or delay separately, FEDO minimizes both metrics simultaneously, achieving up to 52.2% energy savings with near-zero delay. Additionally, increasing the number of users also leads to energy savings. This is due to the optimized placement of fog servers at the access layer which reduces network energy consumption compared to C-EDO. F-EDO also significantly reduces delay, with negligible delay compared to C-EDO due to fog servers are placed closer to the users which minimized the transmission distances. Besides, the results also show that the energy saving in F-EDO compared to the C-EDO increased as the processing capacity of the processing server increased while maintaining its minimal delay. Overall, F-EDO proves to be a more energyefficient and lower-delay solution for IoT networks, offering a better alternative to cloud-based offloading.
Volume: 15
Issue: 1
Page: 332-342
Publish at: 2026-03-01

DFIG integration with ReLIFT converter for grid-connected systems: ANFIS MPPT control

10.11591/ijict.v15i1.pp21-29
Aravindhan Karunanithy , Chidambararaj Natarajan , Sanjay Selvan Arul Manimaran Malathy , Siva Malavan Elantherayan Sharmila
Although dispersed generation and non-linear loads provide difficulties for contemporary power systems that depend on power electronics, renewable energy sources (RES) are essential for meeting the world’s energy demands. This paper provides a unique method for maximum power point tracking (MPPT) in doubly fed induction generators (DFIG) system using an Adaptive network based fuzzy inference system (ANFIS) inference system. The suggested ANFIS MPPT controller adaptively modifies discontinuous control gain to reduce chattering phenomena in the excitation system while preserving the resilience of the closed-loop system. Prior to using a DQ control theory controller for rotor magnitude adjustment to accomplish vector control of active and reactive power, the turbine and DFIG must be modeled. The converter maximizes output current while striving for unity power factor and allowable harmonic content.
Volume: 15
Issue: 1
Page: 21-29
Publish at: 2026-03-01

Integrating smart technologies for sustainable crop management in hydroponics

10.11591/ijict.v15i1.pp39-45
Jeyaprakash N. , Jayachandran M. , Poornavikash T.
Hydroponics has become a game-changing technique in agriculture's constantly changing terrain, upending traditional soil-based farming. The smart hydroponics management system, a cutting-edge method intended to maximize plant development and resource use, is presented in this study. The approach aims to push the limits of conventional farming, drawing inspiration from sustainable horticultural concepts as well as the principles described in Howard M. Resh's book on hydroponic production. This abstract integrates cuttingedge sensor technology and automation methodologies to capture the core of the smart hydroponics management system. It presents the system as a complete answer to the problems facing modern agriculture, rather than just a technique of cultivation. By drawing comparisons with seminal works in computer vision, the unique character of the system is highlighted, demonstrating a dedication to advanced and flexible agricultural techniques.
Volume: 15
Issue: 1
Page: 39-45
Publish at: 2026-03-01

A high linearity low noise amplifier with modified differential inductor for bluetooth profiles

10.11591/ijict.v15i1.pp323-331
Ghattamaneni Usharani , Sourirajan Varadarajan
In today’s rapidly evolving communication landscape, electronic devices rely heavily on high-performance components to ensure seamless connectivity. A low-noise amplifier (LNA) is a critical front-end element in any receiver chain, where its performance significantly influences the overall system efficiency. As integrated circuits continue to shrink with advancements in technology, challenges such as linearity degradation have become increasingly prominent. This work presents a modified derivative (MD) narrowband common source low-noise amplifier (CSLNA) designed using 0.13 µm CMOS technology, offering improved linearity and frequency characteristics. The proposed design adopts a hybrid architecture, combining a folded cascode gain stage with a common-gate configuration. An optimized modified differential inductor is employed at the input for effective impedance matching and reduced noise figure (NF). The implemented LNA achieves a gain of 25.81 dB, an input return loss of –24.86 dB, and maintains a low NF of 0.3 dB at an operating frequency of 2.4 GHz. Furthermore, the linearity metrics-third-order input intercept point (IIP3) and 1 dB compression pointare significantly improved to –16.70 dBm and –21.89 dBm, respectively. These results highlight the LNA's suitability for Bluetooth and other shortrange wireless communication applications.
Volume: 15
Issue: 1
Page: 323-331
Publish at: 2026-03-01

Exploring diverse perspectives: enhancing black box testing through machine learning techniques

10.11591/ijict.v15i1.pp238-246
Heba Nafez Jalal , Aysh Alhroob , Ameen Shaheen , Wael Alzyadat
Black box testing plays a crucial role in software development, ensuring system reliability and functionality. However, its effectiveness is often hindered by the sheer volume and complexity of big data, making it difficult to prioritize critical test cases efficiently. Traditional testing methods struggle with scalability, leading to excessive resource consumption and prolonged testing cycles. This study presents an AI-driven test case prioritization (TCP) approach, integrating decision trees and genetic algorithms (GA) to optimize selection, eliminate redundancy, and enhance computational efficiency. Experimental results demonstrate a 96% accuracy rate and a 90% success rate in identifying relevant test cases, significantly improving testing efficiency. These findings contribute to advancing automated software testing methodologies, offering a scalable and efficient solution for handling large-scale, data-intensive testing environments.
Volume: 15
Issue: 1
Page: 238-246
Publish at: 2026-03-01

Securing Defi: a comprehensive review of ML approaches for detecting smart contract vulnerabilities and threats

10.11591/ijict.v15i1.pp438-446
Dhivyalakshmi Venkatraman , Manikandan Kuppusamy
The rapid evolution of decentralized finance (DeFi) has brought revolutionary innovations to global financial systems; however, it has also revealed some major security vulnerabilities, especially of smart contracts. Traditional auditing methods and static analysis tools are prone to fail in identifying sophisticated threats, including reentrancy attacks, front-running, oracle manipulation, and honeypots. This review discusses the growing role of machine learning (ML) in enhancing the security of DeFi systems. It provides a comprehensive overview of modern ML-based methods related to the detection of smart contract vulnerabilities, transaction-level fraud detection, and oracle trust assessment. The paper also provides publicly available datasets, necessary toolkits, and architectural designs used for developing and testing these models. Additionally, it provides future directions like federated learning, explainable AI, real-time mempool inspection, and cross-chain intelligence sharing. While it is full of promise, the application of ML in DeFi security is plagued by issues like data scarcity, interoperability, and explainability. This paper concludes by highlighting the need for standardised benchmarks, shared data initiatives, and the integration of ML into development pipelines to deliver secure, scalable, and reliable DeFi ecosystems.
Volume: 15
Issue: 1
Page: 438-446
Publish at: 2026-03-01

Privacy-preserving fitness recommendation system using modified seagull monarch butterfly optimized deep learning model

10.11591/ijict.v15i1.pp393-404
Esmita Gupta , Shilpa Shinde
This paper presents a novel modified seagull monarch butterfly optimization (MSMBO) algorithm, with a multi-objective focus on privacy and personalization in the fitness recommender system using a refined three-tier deep learning structure. The method is divided into three phases. In the first phase, fitness data from wearable devices undergoes preprocessing to eliminate noise and standardize features. The second phase incorporates improved elliptic curve cryptography (IECC) alongside the MSMBO to encrypt user data securely, ensuring privacy in cloud storage. This phase also enhances neural network performance by optimizing weights and hyperparameters through feature selection, effectively reducing data complexity while boosting accuracy. In the third phase, ConvCaps extracts spatial data features, while Bi-LSTM identifies temporal dependencies. The proposed system balances multiple objectives like novelty, accuracy, and precision, while safeguarding user data through robust encryption. With the experimental findings, our suggested method performs better than current existing models, especially in heart rate prediction and fitness pattern identification. The overall outcome makes the system ideal for privacyconscious, personalized fitness recommendations. The model’s shows significant improvement in mean squared error (MSE), normalized mean squared error (NMSE), and mean absolute percentage error (MAPE), thus verifying its effectiveness in secure, real-time fitness tracking.
Volume: 15
Issue: 1
Page: 393-404
Publish at: 2026-03-01

Fuzzy logic-based driver fatigue prediction system for safe and eco-friendly driving

10.11591/ijict.v15i1.pp84-92
Raghavan Sheeja , Chidambaranathan Bibin , Selvaraj Vanaja , Shakeela Joy Arul Dhas , Alex Arockia Abins , Padmavathi Balasubramaniam
The advancement of intelligent car systems in recent years has been significantly influenced by developments in information technology. Driver fatigue is a dominant problem in car accidents. The goal of advanced driving assistance is to develop an advanced driving assistance system (ADAS) a eco-friendly model which focuses on the detection of drowsy driver, to notify drivers of their fatigued condition to prevent accidents on the roads. With relation to driving, the driver mustn’t be distracted by alarms when they are not tired. The answer to this unanswered question is provided by 60- second photograph sequences that were taken when the subject’s face was visible. To reduce false positives, two alternative solutions for determining whether the driver is drowsy have been developed. To extract numerical data from photos and feed it into a fuzzy logic-based system, convolutional network is applied initially; later deep learning technique is followed. The fuzzy logic-based solution avoids the false alarm of the system.
Volume: 15
Issue: 1
Page: 84-92
Publish at: 2026-03-01

A comparative study and design investigation: scalable magnitude comparators across technology nodes

10.11591/ijict.v15i1.pp13-20
Anitha Juliette Albert , Umamaheswari Ramalingam , Ashlin Leon A. S. , Sinthia Panneer Selvam , Sripriya Thiagarajan , Arunkumar Kuppusamy
In recent times, the convergence of innovative design technologies such as very large-scale integration (VLSI), cadence design systems, and fieldprogrammable gate array (FPGA) has become crucial to address the growing demand for enhanced efficiency, scalability, and reduced power consumption in electronic designs. This paper introduces a novel approach to designing non-pipelined and pipelined scalable magnitude comparators (MCs), which integrates 4-bit MCs. The frontend implementation of the MCs is achieved using quartus prime, an FPGA board. The backend implementation is done using cadence design system, evaluated across the three distinct CMOS technology nodes. The literature review highlights the influence of technology scaling on area, power consumption, and propagation delay, analyzing various comparator designs and their associated trade-offs. The results provide valuable insights into the design and optimization of MCs for future applications in image processing and nano computing.
Volume: 15
Issue: 1
Page: 13-20
Publish at: 2026-03-01

Practice-based teaching using an AI platform to strengthen faculty competency

10.11591/ijict.v15i1.pp171-178
Angsana Phonsuk , Phakharach Plirdpring
This research aimed to i) analyze faculty members’ knowledge, understanding, and skills in using AI for practice-based teaching enhancement, ii) evaluate factors affecting faculty readiness in integrating AI into teaching processes, and iii) design and develop an AI platform to enhance faculty competency in practice-based teaching. The questionnaire, validated by five experts, was administered to 200 respondents divided into two groups: 100 faculty members from public universities and 100 from private universities. Comparative analysis revealed that public university faculty and private university faculty statistically significant differences in challenges and concerns at the 05 level, with public university faculty expressing higher concerns. Significant differences were found in AI experience and skills, attitudes toward AI use, and challenges and concerns. However, no significant differences were observed in three other areas: AI knowledge and understanding, AI readiness, and belief in AI’s effectiveness for practice-based learning enhancement. Data from both groups were utilized in designing and developing the AI platform to enhance practicebased teaching competency in higher education. Expert evaluation of the platform’s suitability showed high levels of demand for the AI platform and high appropriateness of the technology used in platform development.
Volume: 15
Issue: 1
Page: 171-178
Publish at: 2026-03-01
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