Optimizing Cloud-Integrated Computer Networks: Strategies for Enhanced Performance and Security

In network security, intrusion detection plays an important role. Feature subsets obtained by different feature selection methods will lead to different accuracy of intrusion detection. Using individual feature selection method can be unstable in different intrusion detection scenarios. In this paper, the idea of ensemble is applied to feature selection to adjust feature subsets. Feature selection is converted into a two-category problem, and odd number of feature selection methods is used for voting method to decide whether a feature is required or discarded. In actual operation, mean decrease impurity, random forest classifier, stability selection, recursive feature elimination and chi-square test are used. Feature subsets obtained from them will be adjusted by our proposed method to get ensemble feature subsets. To test the performance, support vector machine, decision tree, knn and multi-layer perception are used to observe and compare the classification accuracy with ensemble feature subsets. Three intrusion detection data sets, including kddcup99, cidds-001 and unsw_nb15 are used in our experiments. The best result is reflected on cidds-001 with a 99.40% classification accuracy. The investigation shows that our method has a certain improvement on classification accuracy of intrusion detection.

Bandwidth load balancing is a critical technique for improving the quality of service (QoS) in local area networks (LANs). Insufficient load balancing and QoS utilization can lead to network congestion, reduced performance, latency, packet loss, and an overall degradation of network performance. This paper presents an overview of load balancing techniques, load balancing algorithms, QoS parameters, load balancing strategies, and considerations for calculating bandwidth. The challenges of implementing load balancing algorithms and preferences for their implementation are discussed. Accurate network measurements and monitoring, coordination between network devices, security and privacy considerations, and careful evaluation of load balancing algorithms are among the challenges faced. The paper also discusses optimization and monitoring of bandwidth use, including options such as increasing bandwidth, bandwidth throttling, and data transfer throttling. The findings presented in this paper provide insights into the importance of load balancing and QoS in LANs and offer guidance for addressing related challenges.

Background. In modern telecommunication networks, the functioning of network protocols is aimed at achieving a high level of Quality of Service and network security. Therefore, an urgent scientific and applied problem is the adaptation of routing solutions with load balancing to the requirements of network security. The novelty of the proposed model is the modification of the load balancing conditions, in which, in addition to the link bandwidth (Quality of Service indicator), the probability of its compromise (network security indicator) is also taken into account. The routing solutions obtained within the framework of the proposed model are aimed at reducing the overload of network links that have a high probability of compromise by switching traffic to more secure links.Objective. Developing the flow-based routing model with load balancing, which takes into account the parameters of network security.Methods. Analysis of all known publications on load balancing and network security. Synthesis of a load balancing flowbased routing model. Study of the influence of network security parameters of communication links on routing and load balancing processes.Results. A new condition for load balancing is proposed, which is a modification of the existing Traffic Engineering solution based on accounting the network security parameters - the probabilities of compromising communication links.Conclusions. In routing with load balancing, parameters of network security should be considered. Depending on the network state and user requirements, different models of network security parameters for load balancing processes can be used.

Cloud computing involves virtualization, distributed computers, networking, software, and web services. Clouds have clients, datacenters, and servers. It has fault tolerance, high availability, scalability, flexibility, little user overhead, cheap ownership cost, on-demand services, etc. These difficulties require a powerful load balancing algorithm. Memory, CPU, latency, or network-load. Load balancing distributes demand to avoid overloaded distributed system nodes and optimises resource use and job response time. Load balancers ensure that processors and network nodes perform similarly. Methods initiated by sender, recipient, or symmetric. Using the divisible load scheduling theorem, a load balancing method will optimise throughput and latency for clouds of different sizes.

This special issue focuses on the security and performance of computer networks and cloud computing. It was inspired by papers presented at a special symposium held on 22 September 2011 in honor of Professor Satish K. Tripathi on the occasion of his 60th birthday and on the eve of his formal investiture as President of the University at Buffalo, the State University of New York. The symposium brought together some 100 participants, including several of Professor Tripathi's current and former collaborators, colleagues and students.

Intrusion detection has been a vast-surveyed topic for many decades as network attacks are tremendously growing. This has heightened the need for security in networks as web-based communication systems are advanced nowadays. The proposed work introduces an intelligent semi-supervised intrusion detection system based on different algorithms to classify the network attacks accurately. Initially, the pre-processing is accomplished using null value dropping and standard scaler normalization. After pre-processing, an enhanced Deep Reinforcement Learning (EDRL) model is employed to extract high-level representations and learn complex patterns from data by means of interaction with the environment. The enhancement of deep reinforcement learning is made by associating a deep autoencoder (AE) and an improved flamingo search algorithm (IFSA) to approximate the Q-function and optimal policy selection. After feature representations, a support vector machine (SVM) classifier, which discriminates the input into normal and attack instances, is employed for classification. The presented model is simulated in the Python platform and evaluated using the UNSW-NB15, CICIDS2017, and NSL-KDD datasets. The overall classification accuracy is 99.6%, 99.93%, and 99.42% using UNSW-NB15, CICIDS2017, and NSL-KDD datasets, which is higher than the existing detection frameworks.

Computer network security is an international problem, Economic losses caused by breakage of computer network security system up to several billions of dollars on a global scale every year. After entering the new century, the loss will reach over 200 billion U.S. dollars. Inofrmation thieves in the past 5 years have been growing to 250%, 99% of large companies have been big in the invasion according to the statistics. World-famous commercial websites, such as Yahoo, Buy, EBay, Amazon, and CNN have been hacked, resulting in huge economic losses. Even RSA, specializes in network security web sites, has been attacked by hackers at some time in the past. Effective security policy or program development is the primary goal of network information security. Network security technology include authentication and authorization, data encryption, access control, security auditing. This paper focuses on intrusion detection technology which is the core technology in the security audit that is one of the network security protections of the important part.

SummaryWith digitization and modern network applications, information security has gained a tremendous importance. Therefore, accurate and efficient detection systems are crucial for maintaining proactive security in computer networks. Machine learning (ML) has shown great potential as a promising solution since it can teach a machine to distinguish malicious and normal network activities. However, recently proposed methods are suffering from at least one of the following: detection accuracy, false alarm rate, and computational complexity issues. The main reason behind this problem is the complexity of the model in terms of attack types. From the ML perspective, intrusion detection is a classification problem where each attack type is identified by a set of different features, and features are used for classifying network activities. Thus, training an ML algorithm to detect more than one attack type leads to a more complex model; the increasing number of used features contributes positively to the model complexity, and may result in relatively lower detection accuracy or a higher false positive rate. To tackle this problem, this study proposes an attack‐wise customized network intrusion detection system (AWC‐NIDS) based on ML, concurrency, and distributed systems to achieve accurate and efficient network‐wide intrusion detection. Since CICIDS2017 contains many modern attacks, it was used for model development and performance evaluation. The experimental results showed that the proposed methodology achieved high classification performance for all datasets with a small number of features. However, it was observed that the lowest accuracy was achieved for the comprehensive dataset (which contains all attack types); for the single attack‐type datasets, the obtained accuracy was above 99%. This finding proves the concept of attack‐wise customization for intrusion detection and shows the significance of the proposed methodology. In conclusion, this framework is promising for implementing robust and accurate cybersecurity systems for traditional and modern networking.

In today fast-paced society, psychological health issues such as anxiety, depression, and stress have become increasingly prevalent across diverse populations.This paper explores the prediction of anxiety, depression, and stress levels via machine learning algorithms. Five different machine learning algorithms were employed to predict the severity of anxiety, depression, and stress across the sampled population. These algorithms were chosen for their high accuracy, making them particularly well-suited for predicting psychological problems. However, applying these algorithms made it apparent that class imbalances existed in the confusion matrix, necessitating incorporating the F1 score measure. The F1 score proved crucial in identifying the most accurate model among the five applied algorithms, ultimately revealing that the random forest classifier was the most effective in predicting anxiety, depression, and stress levels. Moreover, the specificity parameter was examined, showing that the algorithms exhibited notable sensitivity to negative results. The authors emphasized the trade-off between accuracy and execution time, particularly in the hybrid approach. While the hybrid model combined the strengths of multiple algorithms to achieve higher prediction accuracy for anxiety, depression, and stress levels, it also resulted in longer execution times. This trade-off is a common challenge in machine learning, where increasing model complexity can lead to improved accuracy but at the cost of slower processing speeds. This study contributes to the growing body of research on machine learning in mental health prediction, offering valuable insights into the intricacies of class imbalances and the importance of performance metrics such as the f1 score and specificity. The findings underscore the potential of machine learning algorithms, particularly the random forest classifier, in enhancing our understanding and prediction of psychological health issues in diverse and dynamic populations.

Security of networks is becoming progressively more necessary as the commercial internet keeps on developing. Intrusion detection techniques using network data have emerged as a significant area of investigation among the various network protection solutions. Many techniques for detecting network anomalies were suggested up to this point. A substantial amount of labeled data is needed for modeling in Machine Learning (ML) that constitutes the basis of the majority of more effective performance detection strategies. Nevertheless, manually filtering and labeling large-scale traffic data is not feasible in a real network. For real detection, network managers can only employ unsupervised ML techniques and the results of such strategies are far less accurate than those of supervised ML strategies. To increase network security’s efficiency of processing, adaptability, and specificity of detection, this method is useful for instantaneous, network-based safety applications because it maximizes intrusion detection accuracy across several attack types by utilizing quantum computer technology. This paper suggests an innovative multiclassification fusion strategy for optimizing network intrusion detection performance. Numerous fusion approaches are used in this research to combine the identification outcomes of effective intrusion detection approaches. A variety of publicly available KDD Cup 99 datasets were utilized for comparative investigations. According to the experiments, the suggested fusion approach [Decision Tree, K-Nearest Neighbor (KNN), improved Siam Intrusion Detection System (I-SiamIDS), Long Short-Term Memory (LSTM)] outperforms individual intrusion detection techniques in multiple measurements. It also exhibits enhanced resilience and stability, making it suitable for use in a variety of network-based safety recognition settings. The study uses the four primary classes of attacks such as Remote-to-Local (R2L), User to Root (U2R), Denial of Service (DoS), and Probing (Probe), which were compared with the performance metrics such as Precision, F-score, Accuracy, and Recall.

Anomaly intrusion detection technologies are essential for network and computer security as the threat gets more serious yearly. Ensemble learning techniques are promising machine learning methods in anomaly detection that aim to produce multiple models and combine their output in a specific manner to obtain a perfect attack detection. However, it’s still difficult to choose an appropriate ensemble method for a particular dataset. This research is conducted on entry-disciplinary concept in which the knowledge is transferred between network security and machine learning. Thus, the problematic of anomaly detection in network traffic is considered, and two novel ensemble methods for anomaly detection is presented. In both methods, the decision rule (henceforth, Rule-set) which is extracted from two different families of classifiers Naïve Bayes and decision tree J48 will be used as an ensemble constitute classifiers. In the first method, a set of Rule Evaluation Metrics (henceforth, REMs) extracted from Rule-sets will be used for combining classifiers and solving rules conflict whenever occurred. While in the second method the paper presents a novel stacking approach as follows: a cover property of Rule-sets will be utilized to re-encode training instances and produce metadata set that is used for training a meta-level classifier which produces the ultimate result. The evaluation of the proposed methods will be conducted on CICIDS2017 dataset in a term of detection rate, execution time, false alarm rate, accuracy, and other interesting measures. The experimental results show attests their superiority of accuracy that reaches 99.8630% and 99.8642% for first and second methods respectively and lower execution time for both methods especially for the second proposed method, which is (0.25 s).

Intrusion detection is crucial in computer network security issues; therefore, this work is aimed at maximizing network security protection and its improvement by proposing various preventive techniques. Outlier detection and semisupervised clustering algorithms based on shared nearest neighbors are proposed in this work to address intrusion detection by converting it into a problem of mining outliers using the network behavior dataset. The algorithm uses shared nearest neighbors as similarity, judges whether it is an outlier according to the number of nearest neighbors of a data point, and performs semisupervised clustering on the dataset where outliers are deleted. In the process of semisupervised clustering, vast prior knowledge is added, and the dataset is clustered according to the principle of graph segmentation. The novelty of the proposed algorithm lies in outlier detection while effectively avoiding the dependence on parameters, thus eliminating the influence of outliers on clustering. This article uses real datasets: lypmphography and glass for simulation purposes. The simulation results show that the algorithm proposed in this paper can effectively detect outliers and has a good clustering effect. Furthermore, the experimentation reveals that the outlier detection‐based SCA‐SNN algorithm has the best practical effect on the dataset without outliers, clearly validating the clustering performance of the outlier detection‐based SCA‐SNN algorithm. Furthermore, compared to the other state‐of‐the‐art anomaly detection method, it was revealed that the anomaly detection technology based on outlier mining does not require a training process. Thus, they overcome the current anomaly detection problems caused due to incomplete normal patterns in training samples.

Uses of Artificial Intelligent Techniques to Build Accurate Models for Intrusion Detection System

The coordinated development of big data, Internet of Things, cloud computing and other technologies has led to an exponential growth in Internet business. However, the traditional Internet architecture gradually shows a rigid phenomenon due to the binding of the network structure and the hardware. In a high-traffic environment, it has been insufficient to meet people’s increasing service quality requirements. Network virtualization is considered to be an effective method to solve the rigidity of the Internet. Among them, virtual network embedding is one of the key problems of network virtualization. Since virtual network mapping is an NP-hard problem, a large number of research has focused on the evolutionary algorithm’s masterpiece genetic algorithm. However, the parameter setting in the traditional method is too dependent on experience, and its low flexibility makes it unable to adapt to increasingly complex network environments. In addition, link-mapping strategies that do not consider load balancing can easily cause link blocking in high-traffic environments. In the IoT environment involving medical, disaster relief, life support and other equipment, network performance and stability are particularly important. Therefore, how to provide a more flexible virtual network mapping service in a heterogeneous network environment with large traffic is an urgent problem. Aiming at this problem, a virtual network mapping strategy based on hybrid genetic algorithm is proposed. This strategy uses a dynamically calculated cross-probability and pheromone-based mutation gene selection strategy to improve the flexibility of the algorithm. In addition, a weight update mechanism based on load balancing is introduced to reduce the probability of mapping failure while balancing the load. Simulation results show that the proposed method performs well in a number of performance metrics including mapping average quotation, link load balancing, mapping cost-benefit ratio, acceptance rate and running time.

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network's edge. The system's performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model's performance empirically in real-world IoMT scenarios.