From a security perspective, the research of the jeopardized 6G wireless communications and its expected ultra-densified ubiquitous wireless networks urge the development of a robust intrusion detection system (IDS) with powerful capabilities which could not be sufficiently provided by the existing conventional systems. IDSs are still insufficient against continuous renewable unknown attacks on the wireless communication networks, especially with the new highly vulnerable networks, leading to low accuracy and detection rate with high (false-negative and false-positive) rates. To this end, this paper proposed a novel anomaly detection in communication networks by using an ensemble learning (EL) algorithm-based anomaly detection in communication networks (ADCNs). EL-ADCNs consists of four main stages; the first stage is the preprocessing steps. The feature selection method is the second stage. It adopts the proposed hybrid method using correlation with the random forest algorithm of ensemble learning (CFS–RF). It reduces dimensionality and retrieves the best subset feature of all the three datasets (NSL_KDD, UNSW_NB2015, and CIC_IDS2017) separately. The third stage is using hybrid EL algorithms to detect intrusions. It involves modifying two classifiers (i.e., random forest (RF), and support vector machine (SVM)) to apply them as adaboosting and bagging EL Algorithms; using the voting average technique as an aggregation process. The final stage is testing the proposal using binary and multi-class classification forms. The experimental results of applying 30, 35, and 40 features of the proposed system to the three datasets achieved the best results of a 99.6% accuracy with a 0.004 false-alarm rate for NSL_KDD, a 99.1% accuracy with a 0.008 false-alarm rate for UNSW_NB2015, and a 99.4% accuracy with a 0.0012 false-alarm rate for CIC_IDS2017.

In this paper, cooperative simultaneous wireless information and power transfer terahertz (THz)-nonorthogonal multiple access (NOMA) is considered to overcome the challenging shortages that THz communications have due to THz characteristics. The proposed system presents a noticeable improvement in energy efficiency (EE) and spectral efficiency (SE), in addition to other important metrics. By utilizing NOMA technology and THz frequencies, it aims to improve connectivity, resource management, SE, reliability, scalability, user fairness, and to enhance the overall performance of wireless communications. Accordingly, the outcome shows how the introduced energy harvesting technique manages to improve EE and SE compared with the conventional cooperative networks of the recent related work (e.g., cooperative MIMO-NOMA with THz) by 70%. The author also minimizes the transmission power and maximizes the EE by using a decode-and-forward relay rather than an intelligent reflecting surface, which aims to reduce the dissipation in the transceiver hardware, computational complexity, and improves reliability and transmission rate.

In this paper, cooperative simultaneous wireless information and power transfer (SWIPT) terahertz (THz) multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) are considered. The aim is to improve wireless connectivity, resource management, scalability, and user fairness, as well as to enhance the overall performance of wireless communications and reliability. We optimized the current wireless communication systems by utilizing MIMO-NOMA technology and THz frequencies, exploring the performance and gains obtained. Hence, we developed a path-selection mechanism for the far user to enhance the system performance. The EH SWIPT approach used to improve THz communications performance was investigated. Moreover, we proposed a reliable transmission mechanism with a non-LoS (NLoS) line of THz communications for open areas or any location where the intelligent reflecting surface (IRS) cannot be deployed, in addition to using the cheap decode-forward (DF) relaying instead of IRS. The performance and scalability of the upgradeable system were examined, using adjustable parameters and the simplest modulation scheme. The system presents a noticeable improvement in energy efficiency (EE) and spectral efficiency (SE), in addition to reliability. Accordingly, the outcome showed an improvement in the overall reliability, SE, EE, and outage probability as compared to the conventional cooperative networks of the recent related work (e.g., cooperative MIMO-NOMA with THz) by multiple times with a simpler design, whereas it outperformed our previous work, i.e., cooperative SWIPT SISO-NOMA with THz, by more than 50%, with a doubled individual user gain. This system reduces the transceiver hardware and improves reliability with increasing transmission rates.

The constraints of 5G communication systems compel further improvements to be compatible with 6G candidate technologies, especially to cope with the limited wavelengths of blockage-sensitive terahertz (THz) frequencies. In this paper integrating cooperative simultaneous wireless information and power transfer (SWIPT) and hybrid-non-orthogonal multiple access (H-NOMA) using THz frequency bands are suggested. We investigated and developed an optimal SWIPT-pairing mechanism for the multilateral proposed system that represents a considerable enhancement in energy/spectral efficiencies while improving the significant system specifications. Given the system performance investigation and the gains achieved, in this paper, wireless communication systems were optimized and upgraded, making use of promising technologies including H-NOMA and THz communications. This process aimed to alleviate the THz transmission challenges and improve wireless connectivity, resource availability, processing, robustness, capacity, user-fairness, and overall performance of communication networks. It thoroughly optimized the best H-NOMA pairing scheme for cell users. The conducted results showed how the proposed technique managed to improve energy and spectral efficiencies compared to the related work by more than 75%, in addition to the dynamism of the introduced mechanism. This system reduces the transceivers’ hardware and computational complexity while improving reliability and transmission rates, without the need for complex technologies, e.g., multi-input multi-output or reflecting services.

The rapid leap in wireless communication systems incorporated a plethora of new features and challenges that accompany the era of 6G and beyond being investigated and developed. Recently, machine learning techniques were widely deployed in many fields, especially wireless communications. It was used to improve network traffic performance regarding resource management, frequency spectrum optimization, latency, and security. The studies of modern wireless communications and anticipated features of ultra-densified ubiquitous wireless networks exposed a risky vulnerability and showed a necessity for developing a trustworthy intrusion detection system (IDS) with certain efficiency/standards that have not yet been achieved by current systems. IDSs lack acceptable immunity against repetitive, updatable, and intelligent attacks on wireless communication networks, significantly concerning the modern infrastructure of 6G communications, resulting in low accuracies/detection rates and high false-alarm/false-negative rates. For this objective principle, IDS system complexity was reduced by applying a unique meta-machine learning model for anomaly detection networks was developed in this paper. The five main stages of the proposed meta-model are as follows: the accumulated datasets (NSL KDD, UNSW NB15, CIC IDS17, and SCE CIC IDS18) comprise the initial stage. The second stage is preprocessing and feature selection, where preprocessing involves replacing missing values and eliminating duplicate values, leading to dimensionality minimization. The best-affected subset feature from datasets is selected using feature selection (i.e., Chi-Square). The third step is represented by the meta-model. In the training dataset, many classifiers are utilized (i.e., random forest, AdaBoosting, GradientBoost, XGBoost, CATBoost, and LightGBM). All the classifiers undergo the meta-model classifier (i.e., decision tree as the voting technique classifier) to select the best-predicted result. Finally, the classification and evaluation stage involves the experimental results of testing the meta-model on different datasets using binary-class and multi-class forms for classification. The results proved the proposed work’s high efficiency and outperformance compared to existing IDSs.

When it comes to web search, information retrieval (IR) represents a critical technique as web pages have been increasingly growing. However, web users face major problems; unrelated user query retrieved documents (i.e., low precision), a lack of relevant document retrieval (i.e., low recall), acceptable retrieval time, and minimum storage space. This paper proposed a novel advanced document-indexing method (ADIM) with an integrated evolutionary algorithm. The proposed IRS includes three main stages; the first stage (i.e., the advanced documents indexing method) is preprocessing, which consists of two steps: dataset documents reading and advanced documents indexing method (ADIM), resulting in a set of two tables. The second stage is the query searching algorithm to produce a set of words or keywords and the related documents retrieving. The third stage (i.e., the searching algorithm) consists of two steps. The modified genetic algorithm (MGA) proposed new fitness functions using a cross-point operator with dynamic length chromosomes with the adaptive function of the culture algorithm (CA). The proposed system ranks the most relevant documents to the user query by adding a simple parameter (∝) to the fitness function to guarantee the convergence solution, retrieving the most relevant user’s document by integrating MGA with the CA algorithm to achieve the best accuracy. This system was simulated using a free dataset called WebKb containing Worldwide Webpages of computer science departments at multiple universities. The dataset is composed of 8280 HTML-programed semi-structured documents. Experimental results and evaluation measurements showed 100% average precision with 98.5236% average recall for 50 test queries, while the average response time was 00.46.74.78 milliseconds with 18.8 MB memory space for document indexing. The proposed work outperforms all the literature, comparatively, representing a remarkable leap in the studied field.