Depression is a prevalent sickness, spreading worldwide with potentially serious implications. Timely recognition of emotional responses plays a pivotal function at present, with the profound expansion of social media and users of the internet. Mental illnesses are highly hazardous, stirring more than three hundred million people. Moreover, that is why research is focused on this subject. With the advancements of machine learning and the availability of sample data relevant to depression, there is the possibility of developing an early depression diagnostic system, which is key to lessening the number of afflicted individuals. This paper proposes a productive model by implementing the Long-Short Term Memory (LSTM) model, consisting of two hidden layers and large bias with Recurrent Neural Network (RNN) with two dense layers, to predict depression from text, which can be beneficial in protecting individuals from mental disorders and suicidal affairs. We train RNN on textual data to identify depression from text, semantics, and written content. The proposed framework achieves 99.0% accuracy, higher than its counterpart, frequency-based deep learning models, whereas the false positive rate is reduced. We also compare the proposed model with other models regarding its mean accuracy. The proposed approach indicates the feasibility of RNN and LSTM by achieving exceptional results for early recognition of depression in the emotions of numerous social media subscribers.

Mobile ad hoc network (MANET) is a collection of wireless mobile nodes that dynamically form a temporary network without the reliance of any infrastructure or central administration. Energy consumption is considered as one of the major limitations in MANET, as the mobile nodes do not possess permanent power supply and have to rely on batteries, thus reducing network lifetime as batteries get exhausted very quickly as nodes move and change their positions rapidly across MANET. This paper highlights the energy consumption in MANET by applying the fitness function technique to optimize the energy consumption in ad hoc on demand multipath distance vector (AOMDV) routing protocol. The proposed protocol is called AOMDV with the fitness function (FF-AOMDV). The fitness function is used to find the optimal path from source node to destination node to reduce the energy consumption in multipath routing. The performance of the proposed FF-AOMDV protocol has been evaluated by using network simulator version 2, where the performance was compared with AOMDV and ad hoc on demand multipath routing with life maximization (AOMR-LM) protocols, the two most popular protocols proposed in this area. The comparison was evaluated based on energy consumption, throughput, packet delivery ratio, end-to-end delay, network lifetime and routing overhead ratio performance metrics, varying the node speed, packet size, and simulation time. The results clearly demonstrate that the proposed FF-AOMDV outperformed AOMDV and AOMR-LM under majority of the network performance metrics and parameters.

While the cloudification of networks with a micro-services-oriented design is a well-known feature of 5G, the 6G era of networks is closely related to intelligent network orchestration and management. Consequently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) have a big part to play in the 6G paradigm that is being imagined. Future end-to-end automation of networks requires proactive threat detection, the use of clever mitigation strategies, and confirmation that 6G networks will be self-sustaining. To strengthen and consolidate the role of AI in safeguarding 6G networks, this article explores how AI may be employed in 6G security. In order to achieve this, a novel anomaly detection system for 6G networks (AD6GNs) based on ensemble learning (EL) for communication networks was redeveloped in this study. The first stage in the EL-ADCN process is pre-processing. The second stage is the feature selection approach. It applies the reimplemented hybrid approach using a comparison of the ensemble learning and feature selection random forest algorithms (CFS-RF). NB2015, CIC_IDS2017, NSL KDD, and CICDDOS2019 are the three datasets, each given a reduced dimensionality, and the top subset characteristic for each is determined separately. Hybrid EL techniques are used in the third step to find intrusions. The average voting methodology is employed as an aggregation method, and two classifiers—support vector machines (SVM) and random forests (RF)—are modified to be used as EL algorithms for bagging and adaboosting, respectively. Testing the concept of the last step involves employing classification forms that are binary and multi-class. The best experimental results were obtained by applying 30, 35, 40, and 40 features of the reimplemented system to the three datasets: NSL_KDD, UNSW_NB2015, CIC_IDS2017, and CICDDOS2019. For the NSL_KDD dataset, the accuracy was 99.5% with a false alarm rate of 0.0038; the accuracy was 99.9% for the UNSW_NB2015 dataset with a false alarm rate of 0.0076; and the accuracy was 99.8% for the CIC_IDS2017 dataset with a false alarm rate of 0.0009. However, the accuracy was 99.95426% for the CICDDOS2019 dataset, with a false alarm rate of 0.00113.

Recently, artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important area that helps discover hidden behavior within the data that is most vulnerable to attack. It also helps detect network intrusion. Algorithms such as hybrid K-mean array and sequential minimal optimization (SMO) rating can be used to improve the accuracy of the anomaly detection rate. This paper presents an anomaly detection model based on the machine learning (ML) technique. ML improves the detection rate, reduces the false-positive alarm rate, and is capable of enhancing the accuracy of intrusion classification. This study used a dataset known as network security-knowledge and data discovery (NSL-KDD) lab to evaluate a proposed hybrid ML technology. K-mean cluster and SMO were used for classification. In the study, the performance of the proposed anomaly detection was tested, and results showed that the use of K-mean and SMO enhances the rate of positive detection besides reducing the rate of false alarms and achieving a high accuracy at the same time. Moreover, the proposed algorithm outperformed recent and close work related to using similar variables and the environment by 14.48% and decreased false alarm probability (FAP) by (12%) in addition to giving a higher accuracy by 97.4%. These outcomes are attributed to the common algorithm providing an appropriate number of detectors to be generated with an acceptable accurate detection and a trivial false alarm probability (FAP). The proposed hybrid algorithm could be considered for anomaly detection in future data mining systems, where processing in real-time is highly likely to be reduced dramatically. The justification is that the hybrid algorithm can provide appropriate detectors numbers that can be generated with an acceptable detection accuracy and trivial FAP. Given to the low FAP, it is highly expected to reduce the time of the preprocessing and processing compared with the other algorithms.

A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network’s security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network’s integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. In the first part of this survey paper, we offer an introduction to the NIDS theory, as well as recent research that has been conducted on the topic. After that, we conduct a thorough analysis of the most recent ML- and DL-based NIDS approaches to ensure reliable identification of potential security risks. Finally, we focus on the opportunities and difficulties that lie ahead for future research on SDN-based ML and DL for NIDS.