Until now, an effective defense method against Distributed Denial of Service (DDoS) attacks is yet to be offered by security systems. Incidents of serious damage due to DDoS attacks have been increasing, thereby leading to an urgent need for new attack identification, mitigation, and prevention mechanisms. To prevent DDoS attacks, the basic features of the attacks need to be dynamically analyzed because their patterns, ports, and protocols or operation mechanisms are rapidly changed and manipulated. Most of the proposed DDoS defense methods have different types of drawbacks and limitations. Some of these methods have signature-based defense mechanisms that fail to identify new attacks and others have anomaly-based defense mechanisms that are limited to specific types of DDoS attacks and yet to be applied in open environments. Subsequently, extensive research on applying artificial intelligence and statistical techniques in the defense methods has been conducted in order to identify, mitigate, and prevent these attacks. However, the most appropriate and effective defense features, mechanisms, techniques, and methods for handling such attacks remain to be an open question. This review paper focuses on the most common defense methods against DDoS attacks that adopt artificial intelligence and statistical approaches. Additionally, the review classifies and illustrates the attack types, the testing properties, the evaluation methods and the testing datasets that are utilized in the methodology of the proposed defense methods. Finally, this review provides a guideline and possible points of encampments for developing improved solution models of defense methods against DDoS attacks. INDEX TERMS DDoS attack, DDoS defense, artificial intelligence technique, statistical technique.

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A Mobile Ad-Hoc Network (MANET) is made up of wireless mobile nodes that do not require a central infrastructure or administration to establish a network. It is possible for the MANET nodes to function as a router or host. MANET works with an independent multi-hop mobile network which can be used in several real-time applications. Thus, an important issue associated with MANET is the identification of paths with high-level Quality of Service (QoS), like topology. The purpose of having a QoS-aware protocol in MANETs is to enable the discovery of paths that are more efficient between the source and destination nodes of the network and hence, the need for QoS. In this paper, a novel algorithm which can be used in the African Buffalo Optimization (ABO) to improve the QoS of routing protocol MANETs. With ABO, path selection is optimized in the Ad-hoc On-demand Distance Vector (AODV) routing protocol. Results of the test revealed that when ABO is used in AODV, delay and energy-aware routing protocol is manifested.

Numerous non-profit driven establishments depend on volunteers to help achieve their administrative targets. Despite the fact that volunteers work side-by-side or now and again substitute representatives in delivering services, inputting volunteer work into non-profit ventures of delivering services presents remarkable difficulties. Understanding these difficulties provides a significant fundamental building step in comprehending the influence these challenges have on service developmental plans and operations when utilizing volunteers. In this study, the paper brings forward a Charity Fundraising Information System (CFIS) framework and presents the modelling and evaluation of a plan and operational variables applicable to volunteer fulfilment in non-profit driven organizations. Discoveries indicate that fulfilled volunteers are bound to stay longer with the same establishment, give monetarily to the non-profit driven organization, and prescribe the volunteer involvement to other people. Every one of these results guarantees the continuous sustenance of the non-profit driven establishment.

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The filtered orthogonal frequency division multiplexing (F-OFDM) system has been recommended as a waveform candidate for fifth-generation (5G) communications.The suppression of out-of-band emission (OOBE) and asynchronous transmission are the distinctive features of the filtering-based waveform frameworks. Meanwhile, the high peak-to-average power ratio (PAPR) is still a challenge for the new waveform candidates. Partial transmit sequence (PTS) is an effective technique for mitigating the trend of high PAPR in multicarrier systems. In this study, the PTS technique is employed to reduce the high PAPR value of an F-OFDM system. Then, this system is compared with the OFDM system. In addition, the other related parameters such as frequency localization, bit error rate (BER), and computational complexity are evaluated and analyzed for both systems with and without PTS. The simulation results indicate that the F-OFDM based on PTS achieves higher levels of PAPR, BER, and OOBE performances compared with OFDM. Moreover, the BER performance of F-OFDM is uninfluenced by the use of the PTS technique.

Floods are one of the most common natural disasters in the world that affect all aspects of life, including human beings, agriculture, industry, and education. Research for developing models of flood predictions has been ongoing for the past few years. These models are proposed and built-in proportion for risk reduction, policy proposition, loss of human lives, and property damages associated with floods. However, flood status prediction is a complex process and demands extensive analyses on the factors leading to the occurrence of flooding. Consequently, this research proposes an Internet of Things-based flood status prediction (IoT-FSP) model that is used to facilitate the prediction of the rivers flood situation. The IoT-FSP model applies the Internet of Things architecture to facilitate the flood data acquisition process and three machine learning (ML) algorithms, which are Decision Tree (DT), Decision Jungle, and Random Forest, for the flood prediction process. The IoT-FSP model is implemented in MATLAB and Simulink as development platforms. The results show that the IoT-FSP model successfully performs the data acquisition and prediction tasks and achieves an average accuracy of 85.72% for the three-fold cross-validation results. The research finding shows that the DT scores the highest accuracy of 93.22%, precision of 92.85, and recall of 92.81 among the three ML algorithms. The ability of the ML algorithm to handle multivariate outputs of 13 different flood textual statuses provides the means of manifesting explainable artificial intelligence and enables the IoT-FSP model to act as an early warning and flood monitoring system.

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