In today’s digital era, most electrical gadgets have become smart, and the great majority of them can connect to the internet. The Internet of Things (IoT) refers to a network comprised of interconnected items. Cloud-based IoT infrastructures are vulnerable to Distributed Denial of Service (DDoS) attacks. Despite the fact that these devices may be accessed from anywhere, they are vulnerable to assault and compromise. DDoS attacks pose a significant threat to network security and operational integrity. DDoS assault in which infected botnets of networks hit the victim’s PC from several systems across the internet, is one of the most popular. In this paper, three prominent datasets: UNSW-NB 15, UNSW-2018 IoT Botnet and recent Edge IIoT are using in an Anomaly-based Intrusion Detection system(AIDS) to detect and mitigate DDoS attacks. AIDS employ machine learning methods and Deep Learning (DL) for attack mitigation. The suggested work employed different types of machine learning and Deep Learning (DL): Random Forest (RF), Support Vector Machine (SVM), Logistic Regression, and Multi-layer perceptron (MLP), deep Artificial Neural Network (ANN), and Long Term Short Memory (LSTM) methods to identify DDoS attacks. Both of these methods are contrasted by the fact that the database stores the trained signatures. As a results, RF shows a promising performance with 100% accuracy and a minimum false positive on testing both datasets UNSW-NB 15 and UNSW-2018 Botnet. In addition, the results for a realistic Edge IIoT dataset show a good performance in accuracy for RF 98.79% and for deep learning LSTM with 99.36% in minimum time compared with other results for multi-class detection.

Autism Spectrum Disorder (ASD) is a contemporary disease that has recently spread among toddlers and children. Many researchers have been interested to determine the features of the autism. However, this kind of studies is costly in term of the gathering information from several sources. In this paper, we introduced and applied a novel and early prediction techniques based on the using of data mining and machine learning tools. It is difficult to determine the features of any autism ages. In this paper, we used data mining predication techniques which play an integral role to predict the symptoms of autism for any age group. The data of this study is AQ-10 dataset which are involved for toddlers and children. The results present a superior performance for ASD classification. Random forest, Decision Tree, Support Vector Machine, and Naive Bayes the accuracy of 1.0 with the features selected by correlation technique.

The introduction of a new technology has aided the exponential growth of the internet of things (IoT), allowing for the connecting of more devices in the IoT network to be made possible by the availability of quicker connections and reduced latency. As IoT networks have become more prevalent and widely used, security has become one of the fundamental requirements, and a distributed denial of service (DDoS) attack poses a significant security threat due to the limited resources (CPU, memory, open source, persistent connection) that can be used to either intentionally or unintentionally increase DDOS attacks. Fog computing is proposed in this study as a framework for real-time detection and mitigation of DDoS assaults. Fog computing is accurate and quick in detecting attacks due to its proximity to IoT devices. DDOS assaults are detected using an approach that combines randomness measurement of traffic with k-nearest neighbors (KNN) machine learning algorithm. Suggested system obtained 100% detection accuracy for transmission control protocol TCP attacks, 98.79% detection accuracy for UDP attacks, and 100% detection accuracy for internet control message protocol ICMP attacks.

REVIEW CLUSTERING MECHANISMS OF DISTRIBUTED DENIAL OF SERVICE ATTACKS

The internet of things (IoT) is an ecosystem of connected objects that are accessible and available through the internet. This "thing" in the IoT could be a sensor such as a heart monitor, temperature, and oxygen rate in the blood. These sensors produce huge amounts of information that lead to congestion and an effect on bandwidth in the IoT network. In this paper, the proposed system is based on the Zstandard compression algorithm to compress the sensor data to minimize the amount of data transmitted from the IoT level to the fog level and decrease network overloading. The proposed system was evaluated using compression ratio, throughput, and latency time for healthcare applications. The result showed better calculation through decreased response time and increased throughput for transmitted data compared with the case of non-compressed data. It showed the compression data ratio about 70% of orignial data, maximum number of IoT sensor reads as 100, throughput is 85.43 B/ms, and fog processing delay is 6.25 ms.

Fog computing is a novel idea created by Cisco that provides the same capabilities as cloud computing but close to objects to improve performance, such as by minimizing latency and reaction time. Packet failure can happen on a single fog server across a large number of messages from internet of things (IoT) sensors due to several variables, including inadequate bandwidth and server queue capacity. In this paper, a fog-to-server architecture based on the IoT is proposed to solve the problem of packet loss in fog and servers using hybrid load balancing and a distributed environment. The proposed methodology is based on hybrid load balancing with least connection and weighted round robin algorithms combined together in fog nodes to take into consideration the load and time to distribute requests to the active servers. The results show the proposed system improved network evaluation parameters such as total response time of 131.48 ms, total packet loss rate of 15.670%, average total channel idle of 99.55%, total channel utilization of 77.44%, average file transfer protocol (FTP) file transfer speed (256 KB to 15 MB files) of 260.77 KB/sec, and average time (256 KB to 15 MB) of 19.27 sec.