Security and Privacy in Wireless Sensor Networks Using Intrusion Detection Models to Detect DDOS and Drdos Attacks: A Survey

Wireless sensor networks (WSN) playa significant role in the collection and transmission of data. The principal data collectors and broadcasters are small wireless sensor nodes. As a result of their disorganized layout, the nodes in this network are vulnerable to intrusion. Every aspect of human life includes some form of technological interaction. While the Covid-19 pandemic has been ongoing, the whole corporate and academic world has gone digital. As a direct result of digitization, there has been a rise in the frequency with which Internet-based systems are attacked and breached. The Distributed Denial of Service (DDoS) and Distributed Reflective Denial of Service (DRDoS) assaults are new and dangerous type of cyberattacks that can quickly bring down any service or application that relies on the Internet's infrastructure. Cybercriminals are always refining their methods of attack and evading detection by using techniques that are out of date. Traditional detection systems are not suited to identify novel DDoS attacks since the volume of data created and stored has expanded exponentially in recent years. This research provides a comprehensive overview of the relevant literature, focusing on deep learning for DDoS and DRDoS detection. Due to the expanding number of loT gadgets, distributed DDoS and DRDoS attacks are becoming more likely and more damaging. Due to their lack of generalizability, current attack detection methods cannot be used for early detection of DDoS and DRDoS, resulting in significant load or service degradation when implemented at the endpoint. In this research, a brief review is performed on the models that are used for identification of DDoS and DRDoS attacks. The working of the existing models and the limitations of the models are briefly analyzed in this research. © 2023 IEEE.

BLOCOVID: A blockchain-based COVID-19 digital vaccination certificate verification system

The global impact of the COVID-19 pandemic has been felt in diverse ways. Although the death rate in Africa has not been as devastating as predicted by the World Health Organization (WHO), its economic and social impact has been fully felt by the African continent. As the world goes through the vaccination process to achieve herd immunity, Africa has not only faced problems like the inability to produce and procure vaccines, but some countries in the west are doubting the authenticity of the vaccination process and even vaccine certificates coming from various countries on the continent. The approach of using centralized systems to validate COVID-19 vaccine certificates makes these systems susceptible to Denial of Service (DoS), modification, and Man-in-The-Middle (MiTM) attacks. To curb this problem, we proposed a blockchain-based digital COVID-19 vaccination certificate verification system called BLOCOVID. The proposed system uses the decentralized approach of distributed ledgers to ensure that vaccine certificates are secured, immutable, and verifiable. Our proposed system stores vaccine serial numbers and their corresponding certificates as hash values. These hash values are stored on the blockchain network as transaction values. The authenticity of a vaccine certificate is determined by the availability of the hash values of the certificate and its corresponding vaccine serial number on the blockchain network. The proposed system was simulated using the BlockSim simulator. To begin with, the simulation results show that the proposed system can ensure system availability, thereby minimizing DoS attacks. Secondly, the proposed system can ensure the integrity of vaccine certificates by allowing third parties to verify the authenticity of these certificates. The simulation results show that even with 10240 nodes, the average transaction time was 137.2ms, with a total transaction rate of 9911.034 transactions per second. © 2022 IEEE.

Effective DDoS Attack Detection using Deep Generative Radial Neural Network in the Cloud Environment

Recently, internet services have increased rapidly due to the Covid-19 epidemic. As a result, cloud computing applications, which serve end-users as subscriptions, are rising. Cloud computing provides various possibilities like cost savings, time and access to online resources via the internet for end-users. But as the number of cloud users increases, so does the potential for attacks. The availability and efficiency of cloud computing resources may be affected by a Distributed Denial of Service (DDoS) attack that could disrupt services' availability and processing power. DDoS attacks pose a serious threat to the integrity and confidentiality of computer networks and systems that remain important assets in the world today. Since there is no effective way to detect DDoS attacks, it is a reliable weapon for cyber attackers. However, the existing methods have limitations, such as relatively low accuracy detection and high false rate performance. To tackle these issues, this paper proposes a Deep Generative Radial Neural Network (DGRNN) with a sigmoid activation function and Mutual Information Gain based Feature Selection (MIGFS) techniques for detecting DDoS attacks for the cloud environment. Specifically, the proposed first pre-processing step uses data preparation using the (Network Security Lab) NSL-KDD dataset. The MIGFS algorithm detects the most efficient relevant features for DDoS attacks from the pre-processed dataset. The features are calculated by trust evaluation for detecting the attack based on relative features. After that, the proposed DGRNN algorithm is utilized for classification to detect DDoS attacks. The sigmoid activation function is to find accurate results for prediction in the cloud environment. So thus, the proposed experiment provides effective classification accuracy, performance, and time complexity. © 2022 IEEE.

Machine Learning for Classification of DOS Attack in Smart Healthcare Networks

Healthcare industry has taken 360-degree change when it comes to managing, analyzing and leveraging healthcare data. With 5G technology increased data rates, more reliability and greater capacity, the healthcare system can provide remote services for patients. For remote monitoring of a patient’s health, real time data delivery is a must. The crucial requirement of the current time in the healthcare industry is the security of the patient’s sensitive and critical data against potential threats. Therefore, it is important that we have security mechanisms ensuring not only authorized parties have access to a patient’s sensitive data and medical information but also preserve its privacy and security. By 2022, cybercrime is predicted to cost \$6 trillion each year. Healthcare industry is continually changing and adopting new aspects in technological transformations. In recent years there has been a broad adoption of Machine learning approaches because of their high level performance in healthcare services starting from the prediction of heart arrest, to medical imaging for detection of tumors and even infections like COVID. AI could help the Healthcare Industry protect their patients’ data as well as secure their 5G network of computers across their organization. We have discussed Machine Learning techniques for Data security and privacy and a case study for detecting the intensity of DDOS attack using Decision Tree algorithm on Healthcare application network data. We have also proposed a model which includes 4 interconnecting lifecycle stages- collecting the data, storing the data, processing the data along with the analysis stage and creating knowledge from those data. © 2022, Springer Nature Switzerland AG.

Examining the trends and operations of modern Dark-Web marketplaces

Currently, the Dark Web is one key platform for the online trading of illegal products and services. Analysing the.onion sites hosting marketplaces is of interest for law enforcement and security researchers. This paper presents a study on 123k listings obtained from 6 different Dark Web markets. While most of current works leverage existing datasets, these are outdated and might not contain new products, e.g., those related to the 2020 COVID pandemic. Thus, we build a custom focused crawler to collect the data. Being able to conduct analyses on current data is of considerable importance as these marketplaces continue to change and grow, both in terms of products offered and users. Also, there are several anti-crawling mechanisms being improved, making this task more difficult and, consequently, reducing the amount of data obtained in recent years on these marketplaces. We conduct a data analysis evaluating multiple characteristics regarding the products, sellers, and markets. These characteristics include, among others, the number of sales, existing categories in the markets, the origin of the products and the sellers. Our study sheds light on the products and services being offered in these markets nowadays. Moreover, we have conducted a case study on one particular productive and dynamic drug market, i.e., Cannazon. Our initial goal was to understand its evolution over time, analyzing the variation of products in stock and their price longitudinally. We realized, though, that during the period of study the market suffered a DDoS attack which damaged its reputation and affected users' trust on it, which was a potential reason which lead to the subsequent closure of the market by its operators. Consequently, our study provides insights regarding the last days of operation of such a productive market, and showcases the effectiveness of a potential intervention approach by means of disrupting the service and fostering mistrust. © 2022 IEEE.

A Review on Issues and challenges for Protection against DDoS Attacks using Software-Defined Networks approach

DDoS attacks are noticed from last many years but due to growing figure of such attacks in present time increases the awareness of them. Many researchers proposed useful detection and mitigation methods for such DDoS attacks. DDoS attack is somewhat simple to perform, hard to safeguard against, and the aggressor is once in a while followed back. The assailant dispatches a DDoS assault utilizing a botnet to produce immense measure of traffic against a casualty's web worker. The casualty might be a business association, government, or basic framework. The wellspring of the attack can be any gadget associated with the web. During the last one and half year of covid-19 pandemic, the exponential growth of about 542% for such attacks is noticed. As all the organizations started working online, the security solutions that provide a safe and secure online working environment are required more. Software-Defined Networks solution is the better option for such requirements. It is a stage towards the foundation of a dynamic and unified nature of the organization. In this paper, we have reviewed that challenges and solutions for SDN networks. The study reveals important detection and mit-igation methods and strategies against DDoS attacks. © 2021 IEEE.

DDoS Attack using SYN Flooding: A Case Study

Undoubtedly, technology has not only transformed our world of work and lifestyle, but it also carries with it a lot of security challenges. The Distributed Denial-of-Service (DDoS) attack is one of the most prominent attacks witnessed by cyberspace of the current era. This paper outlines several DDoS attacks, their mitigation stages, propagation of attacks, malicious codes, and finally provides redemptions of exhibiting normal and DDoS attacked scenarios. A case study of a SYN flooding attack has been exploited by using Metasploit. The utilization of CPU frame length and rate have been observed in normal and attacked phases. Preliminary results clearly show that in a normal scenario, CPU usage is about 20%. However, in attacked phases with the same CPU load, CPU execution overhead is nearly 90% or 100%. Thus, through this research, the major difference was found in CPU usage, frame length, and degree of data flow. Wireshark tool has been used for network traffic analyzer. © 2022 Bharati Vidyapeeth, New Delhi.

Machine Learning and Deep Learning Methods for Intrusion Detection Systems in IoMT: A survey

The integration of healthcare-related sensors and devices into IoT has resulted in the evolution of the IoMT (Internet of Medical Things). IoMT that can be viewed as an improvement and investment in order to meet patients' needs more efficiently and effectively. It is progressively replacing traditional healthcare systems, particularly after the worldwide impact of COVID. IoMT devices have enabled real time monitoring in the healthcare field, allowing physicians to provide superior care while also keeping patients safe. As IoMT applications have evolved, the variety and volume of security threats and attacks including routing attacks and DoS (Denial of Service), for these systems have increased, necessitating specific efforts to study intrusion detection systems (IDSs) for IoMT systems. However, IDSs are generally too resource intensive to be managed by small IoMT devices, due to their limited processing resources and energy. In this regard, machine learning and deep learning approaches are the most suitable detection and control techniques for IoMT device-generated attacks. The purpose of this research is to present various methods for detecting attacks in the IoMT system. Furthermore, we review, compare, and analyze different machine learning (ML) and deep learning (DL) based mechanisms proposed to prevent and detect IoMT network attacks, emphasizing the proposed methods, performances, and limitations. Based on a comprehensive analysis of current defensive security measures, this work identifies potential open research related challenges and orientations for the actual design of those systems for IoMT networks, that may guide further research in this field. © 2022 IEEE.

Top Reported Data Security Risks in the Age of COVID-19

Data has been collected and stored for thousands of years. Securing data during the digital age has remained difficult. Research shows that in 2018 there was over 33 zettabytes of data, which is approximately an equivalent to 129 billion 256GB mobile devices of data. Risk management in recent years has made attempts at balancing data security risks with organizational business and budgetary requirements. This research examines high probability data security threats and mitigations. It then reports on the threats in connection with the top United States healthcare data breaches reported during the COVID outbreak to the Health and Human Services (HHS) between June 11, 2020 and June 11, 2021. The data analysis shows that there were nine breaches of over a million affected individuals reported to HHS affecting 15,936,679 individuals in total. Five-million individuals is approximately larger than the populations of Los Angeles, New York, and Chicago combined. We connect the common security risks with the reports of these incidents to gain insights into common network security concerns and inform future network architectures and risk mitigations. © 2021 IEEE.

Investigating Cyber Security Awareness Among Preservice Teachers During the COVID-19 Pandemic

South African institutions of higher education suffered serious disruptions during the COVID-19 pandemic which, resulted in migrating most teaching and learning activities to various online platforms, of which many depended on the open web. This has the potential to expose lecturers and students to cyber security threats and risks. As such cyber security awareness (CSA) becomes important. This study investigated the CSA among preservice teachers pursuing a Bachelor of Education studies in Further Education and Training (FET) at a university in Cape Town, South Africa. The purpose of the study was to gain an insight into CSA among preservice teachers who had been using digital technologies to support learning during the COVID-19 pandemic. An electronic questionnaire was administered to a random sample of 300 preservice teachers. The findings show that preservice teachers were limited in their awareness of cyber security threats and risks likely to affect their use of various digital technologies for remote learning. Furthermore, preservice teachers implemented basic strategies to mitigate basic cyber security threats and attacks. These basic strategies were found not to be sufficient for advanced attacks. The study concluded that lack of proper CSA and knowledge among preservice teachers presented them with challenges in solving threat attacks associated with denial-of-service (DoS), data theft and phishing when using personal digital devices. © 2022, Springer Nature Switzerland AG.

Detecting Distributed Denial of Service in Network Traffic with Deep Learning

COVID-19 has altered the way businesses throughout the world perceive cyber security. It resulted in a series of unique cyber-crime-related conditions that impacted society and business. Distributed Denial of Service (DDoS) has dramatically increased in recent year. Automated detection of this type of attack is essential to protect business assets. In this research, we demonstrate the use of different deep learning algorithms to accurately detect DDoS attacks. We show the effectiveness of Long Short-Term Memory (LSTM) algorithms to detect DDoS attacks in computer networks with high accuracy. The LSTM algorithms have been trained and tested on the widely used NSL-KDD dataset. We empirically demonstrate our proposed model achieving high accuracy (~97.37%). We also show the effectiveness of our model in detecting 22 different types of attacks. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

A Big Data and Deep Learning based Approach for DDoS Detection in Cloud Computing Environment

Recently, as a result of the COVID-19 pandemic, the internet service has seen an upsurge in use. As a result, the usage of cloud computing apps, which offer services to end users on a subscription basis, rises in this situation. However, the availability and efficiency of cloud computing resources are impacted by DDoS attacks, which are designed to disrupt the availability and processing power of cloud computing services. Because there is no effective way for detecting or filtering DDoS attacks, they are a dependable weapon for cyber-attackers. Recently, researchers have been experimenting with machine learning (ML) methods in order to create efficient machine learning-based strategies for detecting DDoS assaults. In this context, we propose a technique for detecting DDoS attacks in a cloud computing environment using big data and deep learning algorithms. The proposed technique utilises big data spark technology to analyse a large number of incoming packets and a deep learning machine learning algorithm to filter malicious packets. The KDDCUP99 dataset was used for training and testing, and an accuracy of 99.73% was achieved. © 2021 IEEE.

An iot-based intrusion detection system approach for tcp syn attacks

The success of Internet of Things (IoT) deployment has emerged important smart applications. These applications are running independently on different platforms, almost everywhere in the world. Internet of Medical Things (IoMT), also referred as the healthcare Internet of Things, is the most widely deployed application against COVID-19 and offering extensive healthcare services that are connected to the healthcare information technologies systems. Indeed, with the impact of the COVID-19 pandemic, a large number of interconnected devices designed to create smart networks. These networks monitor patients from remote locations as well as tracking medication orders. However, IoT may be jeopardized by attacks such as TCP SYN flooding and sinkhole attacks. In this paper, we address the issue of detecting Denial of Service attacks performed by TCP SYN flooding attacker nodes. For this purpose, we develop a new algorithm for Intrusion Detection System (IDS) to detect malicious activities in the Internet ofMedical Things. The proposed scheme minimizes as possible the number of attacks to ensure data security, and preserve confidentiality of gathered data. In order to check the viability of our approach, we evaluate analytically and via simulations the performance of our proposed solution under different probability of attacks. © 2022 Tech Science Press. All rights reserved.