Evaluation of Classification Algorithms for Intrusion Detection System: A Review

Intrusion detection is one of the most critical network security problems in the technology world. Machine learning techniques are being implemented to improve the Intrusion Detection System (IDS). In order to enhance the performance of IDS, different classification algorithms are applied to detect various types of attacks. Choosing a suitable classification algorithm for building IDS is not an easy task. The best method is to test the performance of the different classification algorithms. This paper aims to present the result of evaluating different classification algorithms to build an IDS model in terms of confusion matrix, accuracy, recall, precision, f-score, specificity and sensitivity. Nevertheless, most researchers have focused on the confusion matrix and accuracy metric as measurements of classification performance. It also provides a detailed comparison with the dataset, data preprocessing, number of features selected, feature selection technique, classification algorithms, and evaluation performance of algorithms described in the intrusion detection system

State of Art Survey for IoT Effects on Smart City Technology: Challenges, Opportunities, and Solutions

Automation frees workers from excessive human involvement to promote ease of use while still reducing their input of labor. There are about 2 billion people on Earth who live in cities, which means about half of the human population lives in an urban environment. This number is rising which places great problems for a greater number of people, increased traffic, increased noise, increased energy consumption, increased water use, and land pollution, and waste. Thus, the issue of security, coupled with sustainability, is expected to be addressed in cities that use their brain. One of the most often used methodologies for creating a smart city is the Internet of Things (IoT). IoT connectivity is understood to be the very heart of the city of what makes a smart city. such as sensor networks, wearables, mobile apps, and smart grids that have been developed to harness the city's most innovative connectivity technology to provide services and better control its citizens The focus of this research is to clarify and showcase ways in which IoT technology can be used in infrastructure projects for enhancing both productivity and responsiveness

A State of Art Survey for Intelligent Energy Monitoring Systems

In this study, the significance and necessities of surveillance systems have been investigated in several areas - both in the use of neural networks, street lighting systems, factories, and laboratories - for the monitoring systems, especially concerning the design of artificial intelligence programs. The importance of these initiatives and how they can affect any sector and industry reach an essential point from here. Here we reach an important point. An algorithm and an extraordinary approach have been used in every field to develop an intelligent programmer. Something has been mentioned here: the ability to access these intelligent programs in all areas of life. We concentrate on a variety of fields of use and design of monitoring systems in this review article

A State of Art Survey of Nano Technology: Implementation, Challenges, and Future Trends

Nanotechnology is a field of study that aims to make our lives easier, safer, and more environmentally friendly. With current upgrades and alterations to available networking and communication paradigms, incorporating Wireless Nano Sensor Networks (WNSN) with various products, sensors, and devices would introduce new network paradigms. The Internet of Nano Things is a term for this concept (IoNT), To achieve seamless interconnection between Nano networks and existing communication networks and the Internet, many topologies and communication paradigms must be developed while technological hurdles are addressed. The amount of data accessible limits how much research and decision-making can be done. This research visualizes a wide range of nanotechnology applications. The goal of this project was to show in more than way this research method may be used to information recommendation services. The routing protocol is critical in WNSN and IoNT because of the many nanoscale constraints. While ensuring the flow of data and information, this routing protocol must take into account the specific features of nanoscale communication.This research aims to provide insight into the WNSN (Wireless Nano Sensor Networks) and IoNT (The Internet of Nano Things) paradigms, as well as a detailed assessment of a large number of current routing protocols that are tailored to the characteristics and features of nano communication. Big data applications with their features and characteristics in general also use cloud computing. This paper explains different hands based on neural networks and implemented on FPGAs (which is Field-Programmable Gate Array) and other genetic algorithms and neural networks. Many more approaches and models compare

Deep Learning Approaches for Intrusion Detection

Recently, computer networks faced a big challenge, which is that various malicious attacks are growing daily. Intrusion detection is one of the leading research problems in network and computer security. This paper investigates and presents Deep Learning (DL) techniques for improving the Intrusion Detection System (IDS). Moreover, it provides a detailed comparison with evaluating performance, deep learning algorithms for detecting attacks, feature learning, and datasets used to identify the advantages of employing in enhancing network intrusion detection

Web Server Performance Improvement Using Dynamic Load Balancing Techniques: A Review

Today, web services rapidly increased and are accessed by many users, leading to massive traffic on the Internet. Hence, the web server suffers from this problem, and it becomes challenging to manage the total traffic with growing users. It will be overloaded and show response time and bottleneck, so this massive traffic must be shared among several servers. Therefore, the load balancing technologies and server clusters are potent methods for dealing with server bottlenecks. Load balancing techniques distribute the load among servers in the cluster so that it balances all web servers. The motivation of this paper is to give an overview of the several load balancing techniques used to enhance the efficiency of web servers in terms of response time, throughput, and resource utilization. Different algorithms are addressed by researchers and get good results like the pending job, and IP hash algorithms achieve better performance

Comprehensive Survey for Cloud Computing Based Nature-Inspired Algorithms Optimization Scheduling

Many applications in the real world include optimizing specific targets, such as cost minimization, energy conservation, climate, and maximizing production, efficiency, and sustainability. The optimization problem is strongly non-linear with multifunctional landscapes under several dynamic, non-linear constraints in some instances. It is challenging to address those issues. Also, with the increasing strength of modern computers, simplistic brute force methods are still inefficient and unwanted. Practical algorithms are also vital for these implementations whenever possible. Cloud computing has become an essential and popular emerging computing environment that supports on-demand services and provides internet-based services. Cloud computing allows a range of services and tools to be easily accessed from anywhere in the world. Since cloud computing has global access to its services, there will always be threats and challenges facing its servers and services, such as; task scheduling, security, energy efficiency, network load, and other challenges. In the research area, many algorithms have been addressed to solve these problems. This paper investigates relevant analysis and surveys on the above topics, threats, and outlooks. This paper offers an overview of nature-inspired algorithms, their applications, and valuation, emphasizing cloud computing problems. Many problems in science and engineering can be viewed as optimization problems with complex non-linear constraints. Highly nonlinear solutions typically need advanced optimization algorithms, and conventional algorithms can have difficulty addressing these issues. Because of its simplicity and usefulness, nature-inspired algorithms are currently being used. There are nevertheless some significant concerns with computing and swarming intelligence influenced by evolution

A Comprehensive Study of Caching Effects on Fog Computing Performance

The rapid advancement in the Internet of things applications generates a considerable amount of data and requires additional computing power. These are serious challenges that directly impact the performance, latency, and network breakdown of cloud computing. Fog Computing can be depended on as an excellent solution to overcome some related problems in cloud computing. Fog computing supports cloud computing to become nearer to the Internet of Things. The fog's main task is to access the data generated by the IoT devices near the edge. The data storage and data processing are performed locally at the fog nodes instead of achieving that at cloud servers. Fog computing presents high-quality services and fast response time. Therefore, Fog computing can be the best solution for the Internet of things to present a practical and secure service for various clients. Fog computing enables sufficient management for the services and resources by keeping the devices closer to the network edge. In this paper, we review various computing paradigms, features of fog computing, an in-depth reference architecture of fog with its various levels, a detailed analysis of fog with different applications, various fog system algorithms, and also systematically examines the challenges in Fog Computing which act as a middle layer between IoT sensors or devices and data centers of the cloud

A Comprehensive Study of Malware Detection in Android Operating Systems

Android is now the world's (or one of the world’s) most popular operating system. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices are incorporated in many aspects of our everyday lives. This  paper gives a detailed comparison that summarizes and analyses various detection techniques. This work examines the current status of Android malware detection methods, with an emphasis on Machine Learning-based classifiers for detecting malicious software on Android devices. Android has a huge number of apps that may be downloaded and used for free. Consequently, Android phones are more susceptible to malware. As a result, additional research has been done in order to develop effective malware detection methods. To begin, several of the currently available Android malware detection approaches are carefully examined and classified based on their detection methodologies. This study examines a wide range of machine-learning-based methods to detecting Android malware covering both types dynamic and static

Reliable Communications for Vehicular Networks

Vehicular communications, referring to information exchange among vehicles, and infrastructures. It has attracted a lot of attentions recently due to its great potential to support intelligent transportation, various safety applications, and on-road infotainment. The aim of technologies such as Vehicle-to-Vehicl (V2V) and Vehicle to-Every-thibg (V2X) Vehicle-to very-thing is to include models of connectivity that can be used in various application contexts by vehicles. However, the routing reliability of these ever-changing networks needs to be paid special attention. The link reliability is defined as the probability that a direct communication link between two vehicles will stay continuously available over a specified period. Furthermore, the link reliability value is accurately calculated using the location, direction and velocity information of vehicles along the road