Intelligent Intrusion Detection System Using Hybrid Swin Transformer-RNN for Efficient Cyber Threat Mitigation

Transformer-based Network Anomaly Detection System for Intelligent Cyber Security Monitoring

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.

Artificial Intelligence based Intrusion Detection System

Improved Hybrid Model-Based Machine and Deep Learning Approach for Intrusion Detection System

The Intrusion Detection Systems (IDSs) are very important tools for defending a network against emerging cyber threats. This study proposes the hybrid intrusion detection system model of extreme gradient boosting (XGBoost) and KMeans++ clustering algorithm to balance the trade-off between accuracy, efficiency, and robustness in detecting malicious traffic. XGBoost algorithms are good for structured problems where classification problems occur, whereas KMeans++ helps you to get more clustering accuracy by helping centroid initialization. For enhancing the performance of the model some feature extraction steps and data preprocessing steps like normalization, encoding, Synthetic Minority Over-sampling Technique (SMOTE) based imbalance data balancing were considered. The system was trained and validated on Canadian Institute for Cybersecurity Intrusion Detection System (CICIDS) 2017 and put to test in various metrics: accuracy, precision, recall, F1-score, ROC-AUC and false positive rate. Comparative analysis was performed using traditional machine learning models such as SVM, Decision Tree, Random Forest, Naive Bayes and deep learning architectures which include CNN, LSTM and Auto-Encoder. It was found to be high detection accuracy of 99.87% with very low FPR by far i.e. 0.1%. The model provided high recall and precision in different types of attack and successful overfitting resistance could be confirmed using 10-fold cross-validation, XGBoost regularization and structure clustering. This work shall play an important role in improving hybrid models to minimize alert fatigue with trustworthy threat classification in the real operational traffic.

From Signature to Attention: Transformer-Powered Intrusion Detection Systems for Cybersecurity

Experimental results demonstrate that the proposed model achieves high detection accuracy, strong discriminative capability, and low false alarm rates across both datasets, confirming its effectiveness and scalability for next-generation cybersecurity applications.

Optimized Fast-Learning Network Model With SMOTE for University Network Intrusion Detection Systems

Traditional Intrusion Detection Systems (IDSs) tend to have a high false positive detection rate, are time-consuming to train, and lack the flexibility to react quickly to changing cyber threats, such as Distributed Denial of Service (DDoS) attacks and phishing. Such restrictions decrease the responsiveness and reliability of the IDS services implemented in the university network, both wired and wireless, at the Uganda Christian University (UCU), where network and traffic analysis have become a necessity. This study analyzes the current threat environment of networking at UCU in terms of the type, origin, and target of various attacks to identify gaps in existing detection tools. To address data imbalance and high dimensionality, a range of machine learning algorithms was implemented and paired: Random Forest (RF), XGBoost, AdaBoost, Decision Trees, Convolutional Neural Networks (CNNs), and SMOTE. The highest performance was obtained by the combination of RF and SMOTE, with an accuracy of 81.88%, a precision of 82.17%, a recall of 81.88% and an F1-score of 80.19%. These ensemble learning methods can be effectively combined with oversampling to better detect minority attacks and minimize false alarms. This paper proposes an OFLN by addition of PSO to perform automated hyperparameter optimization and MAML to enable high-speed adaptation to new and previously seen attacks. The OFLN incorporates swarm-based optimization with meta-learning, attaining faster convergence, better generalization and efficiency. Empirical results show that OFLN is more effective than baseline ensemble models in detection accuracy, resistance to class imbalance and training efficiency, making it an innovative, scalable and practical IDS.

Enhancing Network Security with a Hybrid Intrusion Detection System Using SVM

A thorough analysis of a modest version of a suggested system that use Support Vector Machines (SVM) to address networking anomaly and misuse detection in the face of insurmountable obstacles, foreseeing an all-encompassing solution to modern network security issues.