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Intrusion Detection in NSL-KDD Dataset Using Hybrid Self-Organizing Map Model

computer Modeling in Engineering & sciences 2025年第4期143卷 639-671页

作者： Noveela Iftikhar Mujeeb Ur Rehman Mumtaz Ali Shah Mohammed J.F.Alenazi Jehad Ali Knowledge Unit of Systems and Technology University of Management and TechnologySialkot51310Pakistan Department of Computer Science University ofWahWah Cantt47010Pakistan Department of Computer Engineering College of Computer and Information Sciences(CCIS)King Saud UniversityRiyadh11451Saudi Arabia Department of AI Convergence Network Ajou UniversitySuwon16499Republic of Korea

Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few *** devices are now easy targets for hackers because of their built-in security *** a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion *** proposed model is evaluated on the NSL-KDD *** hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world ***,this paper proposes a highly efficient deployment strategy for resource-constrained network *** results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)*** particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively.

关键词： Intrusion detection self-organizing map Internet of Things dimensionality reduction

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Autonomous embodied navigation task generation from natural language dialogues

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science China(information sciences) 2025年第5期68卷 119-132页

作者： Haifeng XU Yongchang LI Lumeng MA Chunwen LI Yanzhi DONG Xiaohu YUAN Huaping LIU Department of Automation Tsinghua University School of Physics and Electronic Information Yantai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Computer Science and Technology Tsinghua University

Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.

关键词： proactive robot robot navigation service robot large language model

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Hybrid Sampling Technique for Imbalanced Android Malware Family Classification

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SN computer science 2025年第3期6卷 1-16页

作者： Chauhan, Kshamta Gandotra, Ekta Department of Computer Science and Engineering and Information Technology Jaypee University of Information Technology Waknaghat Himachal Pradesh Solan India

The rapid explosion of Android-based devices has led to a disturbing surge in the volume and sophistication of Android malware. Effective classification of these malicious applications is essential for safeguarding user security and privacy. However, the imbalanced nature of the Android malware data, where some of the families outnumber the other ones, poses a significant challenge to traditional machine learning algorithms. The issue of imbalanced data can be handled by oversampling and undersampling techniques. Oversampling raises the number of instances in the minority class and undersampling decreases the number of instances in the majority class in order to balance the class distribution. Oversampling and undersampling can lead to overfitting and loss of information. To combine the strengths of both techniques, hybrid sampling is used. This paper proposes hybrid sampling techniques that combine oversampling and undersampling methods. Two oversampling techniques, i.e., synthetic minority oversampling technique (SMOTE), and random oversampling (ROS), and three undersampling techniques, i.e., tomek's links (TOMEK), edited nearest neighbours (ENN), and neighborhood cleaning rule (NCR), are used. The proposed hybrid sampling methods are obtained after combining each oversampling technique with an undersampling one. All the hybrid sampling techniques are evaluated on a self-created malware dataset that consists of multiple imbalanced classes of malware families. Four machine learning models, i.e., support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbours (K-NN), are trained after applying the hybrid sampling techniques. From the experimental results, it is concluded that ROS-NCR outperforms other methods in handling imbalanced Android malware families. It achieves 95.81% accuracy and an F-measure of 0.95 using RF. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Android malware Hybrid sampling Imbalanced data Machine learning Multi-class classification

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Secure probe-based latency measurement using NTP and PTP for optimized gateway placement in LoRaWAN

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International Journal of information technology (Singapore) 2025年 1-10页

作者： Bhatnagar, Kumar Vaibhav Kushwah, Rashmi Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida 201304 India

The paper presents a novel framework for optimizing LoRaWAN gateway placement to enhance network performance and reliability. By integrating Network Time Protocol (NTP) for global synchronization and Precision Time Protocol (PTP) for local precision, the approach ensures accurate and secure latency measurements. A robust hash algorithm is employed to safeguard the integrity of latency data. The Performance Efficiency Index (PEI) is introduced as a comprehensive metric to evaluate key performance indicators such as latency, throughput, energy efficiency, and packet delivery ratio (PDR). The model incorporates factors like geographical distribution, signal strength, and gateway density to achieve optimal placement. Simulation results demonstrate the framework’s effectiveness in improving network latency, coverage, energy efficiency, and security, providing actionable insights for LoRaWAN-based IoT network design. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Gateway placement LoRaWAN Network performance NTP PTP Timestamp

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Generative Adversarial Networks for Motor Imagery Classification using Wavelet Packet Decomposition and Complex Morlet Transform

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Multimedia Tools and Applications 2025年 1-24页

作者： Shirodkar, Vaishali Edla, Damodar Reddy Kumari, Annu Department of Computer Science and Engineering National Institute of Technology Goa Kottamoll Plateau Goa Cuncolim403703 India Information Technology Department Goa College of Engineering Farmagudi Goa Ponda403401 India

The area of brain-computer interface research is widely spreading as it has a diverse array of potential applications. Motor imagery classification is a boon to several people with motor impairment. Low accuracy and datasets with few trial recordings present challenges for this classification. This research offers a novel approach to these problems in electroencephalography signal classification for efficient control of neuroprosthetic devices. By using wavelet packet decomposition the proposed method divides the EEG signal into eight frequency bands. Each of these bands were then evaluated by a one-dimensional convolution neural network to find the most discriminative band. Only EEG signals filtered with these bands were used to generate time-frequency images called scalograms using complex Morlet transform. These scalograms were then classified using a two-dimensional convolution neural network optimized for spatial-temporal feature extraction and classification. To mitigate data scarcity, generative adversarial networks were used for effective data augmentation. Experimental evaluation on a benchmark dataset demonstrated substantial improvement: the two-dimensional convolution neural network achieved 93.63% accuracy, surpassing the 85% achieved by the one-dimensional convolution neural network baseline. Precision, recall, and F1-score metrics validated robust performance across motor imagery tasks. Comparative analysis with the latest methods underscored the proposed model’s superiority in accuracy and reliability. Using wavelet transformations, convolution neural network architectures, and generative adversarial network-driven data augmentation presented a promising framework for advancing brain-computer interface applications, particularly in enhancing neuroprosthetic device control. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wavelet decomposition

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and Electronics Engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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IoT and Blockchain for Efficiency in Smart Home Automation with BiLSTM-RNN Model 3

IoT and Blockchain for Efficiency in Smart Home Automation w...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Mahendran, A. Vasa, Yeshwanth Devi, S. Rukmani Dubey, Vivek Ravishankar Rao, Gali Nageswara Nishant, Neerav Sona College of Arts and Science Department of Computer Science Salem India Department of Information Technology Woodside Ct Franklin United States Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Science Chennai India Jai Narain College of Technology Department of Computer Science and Engineering Bhopal India Aditya Institute of Technology and Management Department of Information Technology Srikakulam India Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798331508456

One of the numerous things made easier by the rapid advancement of technology is home automation, which has been growing in popularity over the past few years. Mechanization and automation have permeated nearly every facet of human existence. Implementing a network that can link different home automations through sensors, actuators, and other data sources is the main objective of this approach. Sensors, preprocessing, feature engineering, and training the model are the three stages that make up the suggested method. Sensors are useful because they can gather information about the environment and then process it to produce results that are correct relative to that environment. The raw data on energy usage is normalized and outliers are removed as part of the preparation steps in the suggested methodology. Label encoding methods were used in feature engineering to convert the input into a feature vector. For training the model, the RNN-BiLSTM-CNN was employed. Compared to alternatives such as RNN and LSTM, the suggested model obtains an average accuracy rate of 95.06%. © 2025 IEEE.

关键词： Long short-term memory

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A Package Level Regression Test Selection Approach for Java Software Systems

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IEEE Access 2025年 13卷 87848-87861页

作者： Al-Refai, Mohammed Hammad, Mahmoud M. Faculty of Computer and Information Technology Computer Science Department Jordan university of science and technology P.O. Box 3030 Irbid 22110 Jordan Ajman University Department of Information Technology Faculty of Engineering and Information Technology Ajman United Arab Emirates Jordan University of Science and Technology Faculty of Computer and Information Technology Software Engineering Department P.O. Box 3030 Irbid 22110 Jordan

Regression testing of software systems is an important and critical activity but can be expensive and resource-intensive. An approach to enhance its efficiency is Regression Test Selection (RTS), which selectively re-executes a subset of relevant tests that are impacted by code modifications. Previous studies on static and dynamic RTS for Java software have shown that selecting tests at the class level is more effective than using finer granularities like methods or statements. Nevertheless, RTS at the package level, which is a coarser granularity than class level, has not been thoroughly investigated or evaluated for Java projects. To address this gap, we propose PKRTS, a static package-level RTS approach that utilizes the structural dependencies of the software system under test to construct a package-level dependency graph. PKRTS analyzes dependencies in the graph and identifies relevant tests that can reach modified packages, i.e., packages containing altered classes. In contrast to conventional static RTS techniques, PKRTS implicitly considers dynamic dependencies, such as Java reflection and virtual method calls, among classes belonging to the same package by treating all those classes as a single cohesive node in the dependency graph. We evaluated PKRTS on 885 revisions of 9 open-source Java projects, with its performance compared to Ekstazi, a state-of-the-art dynamic class-level approach, and STARTS, a state-of-the-art static class-level approach. We used Ekstazi as the baseline to measure the safety and precision violations of PKRTS and STARTS. The results indicated that PKRTS outperformed static class-level RTS in terms of safety violation, which measures the extent to which relevant test cases are missed. PKRTS showed an average safety violation of 2.29%, while STARTS recorded 5.94%. Despite this, PKRTS demonstrated lower average precision violation than class-level RTS, as it selected a higher number of irrelevant test cases. The average reduction in te

关键词： Dynamic analysis Java software systems Package-level dependencies Regression test selection Software testing Static analysis

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ColorVein: Colorful Cancelable Vein Biometrics

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IEEE Transactions on information Forensics and Security 2025年 20卷 4943-4955页

作者： Wang, Yifan Gui, Jie Shi, Xinli Gui, Linqing Tang, Yuan Yan Kwok, James Tin-Yau Southeast University School of Cyber Science and Engineering Nanjing210000 China Ministry of Education Purple Mountain Laboratories Engineering Research Center of Blockchain Application Nanjing210000 China Nanjing University of Posts and Telecommunications College of Computer Nanjing210003 China University of Macao Department of Computer and Information Science 999078 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering 999077 Hong Kong

Vein recognition technologies have become one of the primary solutions for high-security identification systems. However, the issue of biometric information leakage can still pose a serious threat to user privacy and anonymity. Currently, there is no cancelable biometric template generation scheme specifically designed for vein biometrics. Therefore, this paper proposes an innovative cancelable vein biometric generation scheme: ColorVein. Unlike previous cancelable template generation schemes, ColorVein does not destroy the original biometric features and introduces additional color information to grayscale vein images. This method significantly enhances the information density of vein images by transforming static grayscale information into dynamically controllable color representations through interactive colorization. ColorVein allows users/administrators to define a controllable pseudo-random color space for grayscale vein images by editing the position, number, and color of hint points, thereby generating protected cancelable templates. Additionally, we propose a new secure center loss to optimize the training process of the protected feature extraction model, effectively increasing the feature distance between enrolled users and any potential impostors. Finally, we evaluate ColorVein’s performance on all types of vein biometrics, including recognition performance, unlinkability, irreversibility, and revocability, and conduct security and privacy analyses. ColorVein achieves competitive performance compared with state-of-the-art methods. © 2005-2012 IEEE.

关键词： Anonymity

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Improved employee bee algorithm for team formation

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Evolving Systems 2025年第2期16卷 1-20页

作者： Shingade, Sandip Niyogi, Rajdeep Shelke, Sushila Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Computer Engineering and Information Technology Veermata Jijabai Technological Institute Matunga Maharashtra Mumbai400019 India Department of Computer Engineering Cummins College of Engineering for Women Karve Nagar Maharashtra Pune411052 India

Finding an appropriate subset of agents (a team) from a larger pool of agents (the source set) so that the team exhibits a desired quality is the essence of the team formation problem. This problem is recognized to have high computational complexity, classified as NP-hard. In order to reduce the state space, this research paper incorporates techniques including vertical and horizontal reductions, which convert data to binary representation. To simplify the data representation for analysis, these methods use vertical and horizontal reductions to convert data to binary format, generating a subgraph. With this, the Honeybee Optimization algorithm’s Employee Bee phase (EBP) swap operation characteristic is used to efficiently optimize team communication costs by making it easier to determine a team with a minimum communication costs (MCC). The proposed approach is assessed using two well-known real-world data sets, the ACM data set and the DBLP data set, demonstrating an improvement of 79% in the ACM dataset and 68% in the DBLP dataset compared to existing literature approaches like Improved Jaya Modified Swap Operation, Improved Grey Wolf Optimization, and State Space Reduction Techniques for Team Formation. This highlights the significant positive impact of state space reduction techniques combined with the IEB on team performance, particularly enhancing communication efficiency and overall team effectiveness, as measured by MCC metrics. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： NP-hard

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