Proper security solutions in the cyber world are crucial for enforcing network security by providing real-time network protection against network vulnerabilities and data exploitation. An effective intrusion detection strategy is capable of taking a holistic approach for protecting critical systems against unauthorized access or attack. In this paper, we describe a machine learning (ML) based comprehensive security solution for network intrusion detection using ensemble supervised ML framework and ensemble feature selection methods. In addition, we provide a comparative analysis of several ML models and feature selection methods. The goal of this research is to design a generic detection mechanism and achieve higher accuracy with minimal false positive rates (FPR). NSL-KDD, UNSW-NB15, and CICIDS2017 datasets are used in the experiment, and results show that our detection model can identify 99.3% of intrusions successfully with the lowest 0.5% of false alarms, which depicts better performance metrics compared to existing solutions.

Rapid and widespread adoption of emerging Information Technology (IT) infrastructures and services in commercial and private endeavors opens new horizons for novel cyberattacks. Network Intrusion Detection Systems (NIDS) gained attention as an effective means of combating various cyber threats. Recent research demonstrates the potency of machine learning (ML) and deep learning (DL) approaches in the development of NIDS. In this paper, we propose a DL-based framework called the Ensemble Framework for Network Intrusion Detection System (ENIDS) to detect various types of cyberattacks, which includes dynamic data pre-processing, optimal feature selection, the handling of imbalanced data samples, and a DL-based ensemble model. Our DL-based ensemble model is comprised of two layers: the base learner and the meta-learner. The base learner is composed of three robust DL models: convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU), and the meta-learner is a deep neural network (DNN) model. The proposed framework experimented with two publicly available and popular network traffic datasets, namely UNSW-15 and CICIDS-2017. In the UNSW-15 and CICIDS-2017 datasets, our proposed framework detects cyberattacks with an accuracy of 90.6% and 99.6% and an F1-score of 90.5 and 99.6%, respectively. According to experimental findings, the proposed ensemble framework outperforms existing state-ofthe-art approaches and demonstrates better performance than benchmark DL methods in terms of accuracy, F1-score, and execution time for training and testing.

The task of predicting internet traffic is challenging, particularly in multi-step forecasting due to the volatile and random nature of data. In addition, real-world traffic may contain outlier data points, so developing a prediction model that integrates anomaly detection and mitigation is necessary. This paper compares several deep sequence models, such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM Encoder-Decoder (LSTM En De), LSTM Encoder-Decoder with attention layer (LSTM En De Atn), and Gated Recurrent Unit (GRU), with our proposed methodology for single-step prediction. Our proposed LSTM En De model, integrated with outlier detection, outperforms traditional deep sequence models in single-step prediction, reducing the deviation between actual and predicted traffic by over 11%. We also apply our methodology to multi-step forecast analysis, using multiple output strategies for forecast horizons of 3, 6, 9, and 12 steps ahead. Experimental results demonstrate the effectiveness of our proposed methodology in improving the accuracy of singlestep prediction and multi-step forecasting tasks, especially when dealing with outlier data points that adversely affect model accuracy. In summary, this paper investigates the challenges of real-world internet traffic prediction, proposes a novel prediction model integrated with anomaly detection and mitigation, and compares different deep sequence models for single-step and multi-step forecasting tasks.

With recent advancements in artificial intelligence (AI) and next-generation communication technologies, the demand for Internet-based applications and intelligent digital services is increasing, leading to a significant rise in cyber-attacks such as Distributed Denial-of-Service (DDoS). AI-based DoS detection systems promise adequate identification accuracy with lower false alarms, significantly associated with the data quality used to train the model. Several works have been proposed earlier to select optimum feature subsets for better model generalization and faster learning. However, there is a lack of investigation in the existing literature to identify a common optimum feature set for three main AI methods: machine learning, deep learning, and unsupervised learning. The current works are compromised either with the variation of the feature selection (FS) method or limited to one type of AI model for performance evaluation. Therefore, in this study, we extensively investigated and evaluated the performance of 15 individual FS methods from three major categories: filter-based, wrapper-based, and embedded, and one ensemble feature selection (EnFS) technique. Furthermore, the individual feature subset’s quality is evaluated using supervised and unsupervised learning methods for extracting a common best-performing feature subset. According to our experiment, the EnFS method outperforms individual FS and provides a universal best feature set for all kinds of AI models.

The ISP (Internet Service Provider) industry relies heavily on internet traffic forecasting (ITF) for long-term business strategy planning and proactive network management. Effective ITF frameworks are necessary to manage these networks and prevent network congestion and over-provisioning. This study introduces an ITF model designed for proactive network management. It innovatively combines outlier detection and mitigation techniques with advanced gradient descent and boosting algorithms, including Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), CatBoost Regressor (CBR), and Stochastic Gradient Descent (SGD). In contrast to traditional methods that rely on synthetic datasets, our model addresses the problems caused by real aberrant ISP traffic data. We evaluated our model across varying forecast horizons—six, nine, and twelve steps—demonstrating its adaptability and superior predictive accuracy compared to traditional forecasting models. The integration of the outlier detection and mitigation module significantly enhances the model’s performance, ensuring robust and accurate predictions even in the presence of data volatility and anomalies. To guarantee that our suggested model works in real-world situations, our research is based on an extensive experimental setup that uses real internet traffic monitoring from high-speed ISP networks.