Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 1503-1508· 0 citations· 15 references
Abstract
Mobile Ad hoc Networks (MANET) are broadly used in crucial application in globally; nevertheless, their dynamic topology, open communication environment and limited resource make highly vulnerable to cyber-attacks, necessitates intelligent and strong security solution. Therefore, in this work, implemented an Optimized Spiking Neural Network (SNN) based Intrusion Detection System (IDS) for improving security of MANET environments. UNSW-NB15 dataset is evaluated, with data preprocessing involves data cleaning and handling missing values for enhancing data quality and feature engineering which includes Exploratory Data Analysis (EDA) and normalization for identifying attack pattern and stabilizing learning feature scale. SNN is designed for modelling the temporal characteristics of network traffic, while the White Shark Optimization (WSO) is employed for automatically tuning the network parameters for enhancing detection accuracy. Using Python software, the proposed work attains an accuracy of 94.71%, precision and specificity of 100%, recall of 94.15%, and F1-score of 96.99%, significantly outperforming conventional detection approaches.
As interconnected devices increasingly transmit personal and sensitive data, security attacks are becoming more sophisticated and prevalent, highlighting the critical need for effective security solutions in Internet of Things (IoT) environments. An automated Network Intrusion Detection (NID) system plays a vital role in notifying system administrators of security breaches, acting as an efficient tool for protecting IoT networks from various threats. This study utilizes the UNSW-NB 15 dataset to enhance intrusion detection accuracy by addressing performance challenges and class imbalances within the data. We employ a combination of feature selection techniques, including Filter Method, Wrapper Method, and an Embedded approach using Lasso and Random Forest with Recursive Feature Elimination (RFE), alongside Pearson Correlation Coefficient (PCC). To tackle class imbalance, we apply the Synthetic Minority Over-sampling Technique (SOMTE). Various algorithms are implemented, including Random Forest, Decision Tree, AdaBoost, Bernoulli Naive Bayes, K-Nearest Neighbors, and Logistic Regression. Notably, the Stacking Classifier, which combines Boosted Decision Trees, Bagging with Random Forest, and LightGBM, demonstrates high performance in accurately detecting intrusions, significantly improving detection rates and reducing false alarms.
Rangu Shashidhar, M. Raju· International Journal of Eng...· 1 citation
Wireless sensor networks (WSNs) play a vital role in modern applications such as environmental monitoring, industrial automation, and smart infrastructure, where reliable data transmission, robustness, and energy efficiency are essential. However, their distributed architecture and constrained computational resources make them highly vulnerable to a wide range of security threats, including Blackhole, Grayhole, Flooding, and Scheduling attacks. These attacks can severely disrupt network functionality, degrade data integrity, and compromise the overall reliability of mission-critical operations. To address these challenges, we present an intrusion detection system (IDS) framework that leverages a diverse set of machine learning (ML) models, incorporating both boosting and non-boosting techniques, as well as deep learning (DL) architectures, including sequential and non-sequential designs. This diversity enables the framework to capture varied learning behaviors and decision boundaries. To further enhance detection accuracy and adaptability, Ant colony optimization (ACO) is employed as a metaheuristic tuning layer, refining hyperparameters to improve performance under the strict resource limitations typical of WSN environments. Each model is evaluated in both its baseline and ACO-optimized form, enabling a detailed comparative analysis that highlights the influence of optimization on intrusion detection effectiveness. Experimental results demonstrate that ACO significantly strengthens model resilience against diverse threats, offering an adaptive and efficient approach to securing modern WSN infrastructures.
Mortada Falah Badri, Mina Malekzadeh· Journal of Electronic &...· 0 citations
An OFLN by addition of PSO to perform automated hyperparameter optimization and MAML to enable high-speed adaptation to new and previously seen attacks is proposed, which is more effective than baseline ensemble models in detection accuracy, resistance to class imbalance and training efficiency.
Designing intrusion detection systems for cloud environments requires a framework that not only achieves high accuracy but also effectively identifies a wide spectrum of attacks, including DoS/DDoS, Probe, R2L, and U2R, under dynamic and noisy network conditions. This paper presents the integrated AQSE-QDST framework, which combines the Adaptive Quantum Swarm Evolution (AQSE) algorithm for feature space optimization with QDST-Net (Quantum-Inspired Dual Spatial-Temporal Network) as the classification engine. In addition, the Entropy-Guided Adaptive Flow Normalization (EAFN) mechanism is incorporated to accelerate convergence and reduce fitness fluctuations during the early stages of training. This three-layer design enables robust feature extraction, dimensionality reduction, and stable feature selection for diverse and imbalanced datasets. Experiments conducted on three benchmark cybersecurity datasets demonstrate that the proposed framework performs effectively in detecting both frequent and rare attacks. The model achieves accuracy rates of 99.69% on NSL-KDD, 98.86% on CIC-IDS2017, and 98.65% on UNSW-NB15, highlighting its capability to detect both high-volume attacks such as DoS/DDoS and Probe and low-frequency attacks such as R2L and U2R. Furthermore, convergence analysis indicates that AQSE-QDST outperforms baseline methods by maintaining more stable fitness values and more consistent feature selection behavior.
Rasoul Farahi, Nahideh Derakhshanfard, Roya Abdollahzadeh Sarnaghi et al.· Discover Internet of Things· 0 citations
Cyber threats are constantly changing and developing, so organizations need to be able to keep up with those changes with security measures that are not limited to static firewalls. This paper presents an adaptive firewall framework that uses machine learning (ML) and deep learning (DL) methods for the creation of dynamic rules and the ability to detect intrusions in real time. Network traffic features are first normalized using Z-score normalization to provide a stable model. Feature selection is accomplished through Recursive Feature Elimination (RFE) and dimensionality reduction is performed using Principal Component Analysis (PCA) to maintain low computation cost while minimizing the loss of information. For classification, the hybrid model consists of Random Forest and Long Short-Term Memory (LSTM) Algorithms; the Random Forest classifier provides an initial identification of a complex, non-linear relationship, while the LSTM algorithm exploits the temporal characteristics of network traffic to identify sequences of attacks that are changing over time. As a result of the use of this hybrid model, both known and zero-day attacks can be detected without difficulty and the adaptive firewall framework will continue to generate new/enhanced rules while adapting to new threats based on ongoing feedback. Experimental results indicate that the adaptive firewall framework produces a high rate of detection, low rates of false positives, and significantly greater adaptability than traditional firewall systems. These results confirm that using statistical pre-processing, feature optimization and hybrid learning models will provide the basis for the development of next generation adaptive firewall systems.
M.Sampath, R. S, Srinitthilan M et al.· 2026 4th International Confe...· 0 citations
As MNI becomes increasingly vulnerable to new kinds of attacks from the cyber world, accurate and timely detection of intrusions becomes a primary key to the power of cybersecurity. More complex attack patterns, complex traffic interactions within large scales are not very collaborable with the typical signature-based detection methods. A Transformer Based Network Anomaly Detection System for intelligent cyber security monitoring based on network flow analysis (NFAs) is proposed in the paper. This framework is derived from the CICIDS2017 data-set and proposes 78 of the statistical flow characteristics, where each flow characteristic impacts the behaviour of a packet, protocol, volume of traffic and temporal communication pattern. The model uses a Transformer Encoder network architecture along with multiple heads of self-attention, which provides greater understanding to deal with complex relationships between features from network traffic. LabelEncoder and StandardScaler have been applied to the columns with values that need to be encoded for categorical variables and scaled to fit the values for models training. A trained model is then applied to progress multiple different categories of cyberattacks including DDoS attacks, PortScan, Brute Force, Botnet, Web Based, etc. and different kinds of traffic, all traffic is considered benign traffic. For providing real-time predictions, confidence interval, prediction of class severity and alerts using trained model an API developed on flask to connect the trained model to a dashboard was built. The experimental results show that the Transformer-based learning could be very effective in achieving successful capturing of the network behavior and conducting realistic detection. The overall proposed system offers an intelligent, scalable and deployment-centric approach to improve the monitoring and proactive detection of threats in contemporary networks in the field of cybersecurity.
S. Nagendrudu, Shaik Mohammed Anays, F. Mahammad et al.· 2026 International Conferenc...· 0 citations
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