Predictive Data Security using AI

Knowledge is power, and in this digital age, knowledge is represented by data, making it one of the most valuable assets. With rapidly evolving technology, there are challenges that directly or indirectly threaten the integrity of data, such as cybercrime, privacy concerns, theft, malware, and viruses. The development of Blockchain Technology has helped in the mitigation of some of these problems by safeguarding online data resources. In this chapter, we introduce the concept of blockchain, discuss its structure and features, and understand its operation. The main focus of this chapter is to observe the vulnerabilities of this technology and scrutinize several attacks exploiting them to understand their outcomes. We go over a few security improvements in an attempt to protect from attacks and alleviate the existing threats. In addition, we explore its application and implementation in various fields. We conclude by discussing the major challenges this technology is facing at present and may encounter in the future.

Social media security is a rising concern among today’s generation, as when the pandemic started, a lot more people than before have begun using social media due to lack of entertainment or other reasons. There is a rise of 62% in Ransomware since 2019 (pre-pandemic), as mentioned by the Cyber Threat Report by SonicWall. As cybersecurity attacks are becoming more severe, this number of attacks is still set to rise. So in order to investigate the possible security issues, this paper digs deep into the concepts of social media security, potential threats and feasible solutions. The importance of having users’ data secure and protected from various threats such as malware attacks, identity theft, cyberbullying and so on, is addressed so that neither the user nor the developers suffer from any loss. Organizations may do more effective patch management to prioritize security-related patching and update their software in accordance with the solutions discussed in this paper.

With the emergence of Fog and Edge architecture, optimization has become a significant aspect of Cloud computing. Not only do these changing architecture necessitate re-evaluating cloud-native optimizations and uncovering Fog and Edge-based outcomes, but the goals also necessitate a significant shift from focusing just on latency to focusing on energy, security, dependability, and cost. As a result, it appears that optimization targets have become broader, with the Internet of Things (IoT)-specific objectives emerging recently. Furthermore, in certain applications that need low latency, the delay generated by transferring data to the cloud and subsequently back to the application can have a significant impact on their performance. Existing IoT designs are becoming increasingly centralized, relying heavily on cloud solutions for data processing, analytics, and decision-making. This survey highlights the main security and privacy challenges that fog and edge computing confront, as well as in what way security concerns may influence the development and usage of edge and fog computing.

Getting delicate information is the objective of the overwhelming majority. Cyber-attack programs target data driven information because majority of strategic and touchy information are available there. Thus, associations should focus on data set security, and the initial step is information knowledge—knowing what touchy information one has, how their data set framework is designed, and who approaches it. It involves a common sense that the web isn't secure. Many occasions have shown that there are individuals in this enormous interconnection of organizations that need to, with different aims, take others’ data, disturb the administration of an overall specialist co-op, and assault frameworks to get entrance or to cut them down. Network security has been a principal component of each association to guarantee secure web availability and insurance against information breaks. While numerous associations have turned towards data centre specialists to save their time and effort on obtaining, establishing and securing of equipments, servers, and gadgets, data centres themselves are not secure from hooligans on the web. It is time for the Data Centre to demonstrate its reliability to clients by getting their information and disconnection from different clients that share a similar framework and offering continuous assistance with a base measure of personal time. To get Data Centres organizations and forestall information breaks, various sellers and Data Centre experts have proposed different arrangements, of which some have been examined in this paper. Besides, as Data Centre innovation has been created to adjust to mechanization through programming reflection, virtualization has become an indivisible piece.

Election in any country is a very inflated and time-consuming process. Many countries have tried to implement numerous ways of conducting an election to make the process easy and time and cost-efficient. One such method is a blockchain-based secure voting system that has been in research for a long. However, it is still a challenge to implement it because of various reasons like security, availability of the internet to everyone, etc. Therefore, this paper presents a secure election system based on blockchain, tackling a few of these problems and making it more secure. The proposed system uses Aadhaar-based voter authentication and One-time password (OTP)-based verification. The proposed scheme uses a private blockchain that solves many security problems and even attacks like Sybil, Distributed denial of service (DDoS), etc. The proposed system can reduce the cost of elections to a large extent while increasing election security.

DevOps is a development and operations practice where engineers collaborate throughout the full-service lifecycle, from design to development to production support. DevOps is a newly emerging field in the field of Software application. Most of the giant companies have now shifted towards DevOps practices as it channelizes the development and operation process for any software development. The present chapter aims to provide the DevOps information for the basic to reach out to maximum people working in or planning to shift towards DevOps. Starting from the definition, the phases, tools, and security are discussed in the present paper. Each phase of the DevOps life cycle is discussed with the tools used in that phase. The DevOps practices are powered with several tools to provide end-to-end automation in software development. The present paper presents the basic knowledge of prevalent tools available for DevOps practices. Along with the DevOps automation, the chapter also gives an overview of DevOps security, its need, and its tool. The chapter covers the software phases and the tools used to automate it. It also provides information regarding the tool platform, availability, and usage. To emphasize more on DevOps, the chapter has also summarized the industrial and academic opportunities in DevOps.

With the rapid progress of cloud computing, there has been a marked improvement in the development of smart healthcare applications such as Internet of Medical Things (IoMT), Telemedicine, etc. Cloud-based healthcare systems can efficiently store and communicate patient electronic healthcare records (EHR) while allowing for quick growth and flexibility. Despite the potential benefits, identity violation, copyright infringement, illegal re-distribution, and unauthorized access have all been significant. To address all these breaches, in this paper, a reversible medical image watermarking scheme using interpolation is proposed. The medical image is partitioned into Border Region (BR), Region of Interest (ROI), and Region of Non-interest (RONI) regions. BR is used for embedding integrity checksum code generated from ROI for tamper detection. RONI is used for embedding watermark. To ensure complete recovery of ROI and high embedding capacity, ROI is compressed before embedding. To ensure high-security compressed ROI, hospital emblem and EHR merged and then encrypted using a random key generated from Polybius magic square to get higher security. The proposed scheme is proved to take less computational time as there are no complex functions used in the embedding. The experiments performed on the proposed scheme is proved to have high imperceptibility, robustness, embedding capacity, security, and less computational time. All these confirm that the proposed approach is a potential candidate for suitable in smart healthcare environment.

Among all the approaches, Digital watermarking is the most widely implemented approach for copyright protection and authentication of data. In this technique, a unique piece of information is known as a watermark. Then the watermark gets into an image, later, to achieve its objective the watermark will be extracted. For the transmission of medical images, digital watermarking schemes are mostly used to ensure that the image has not gone through any unauthorized or illegal modifications during the transmission. Since conventional watermarking schemes alter the pixels in the original image, it is not suited for watermarking medical images. In medical images, permanent modifications may adversely affect the diagnosis process at the receiver side, caused by watermarking, especially when we are using some computer-aided diagnosis tools. This motivated computer scientists to work on reversible watermarking schemes. The reversible watermarking technology makes it possible to recover the required medical image from the watermarked image, while extracting the hidden watermark. So, the reversible watermarking technique does not affect the diagnosis in any way since the recovered image will be equivalent to the original image. This recovered image will be used by the user. The use of reversible watermarking techniques to send patient reports along with medical images is also explored, with the patient reports being embedded in the medical picture itself rather than the watermark. These techniques are commonly known as reversible data hiding techniques. This book chapter gives a brief overview of reversible data hiding techniques, reversible watermarking methods, and the major applications in medical image transmissions. In addition, the chapter addresses contemporary reversible data hiding and reversible watermarking algorithms intended specifically for medical picture transmission. The chapter also discusses some of the obstacles that must be overcome when developing a reversible watermarking system for healthcare applications.

Deep learning has created substantial improvements for industries and set the tempo for a destiny constructed on artificial intelligence (AI) technology. Nowadays, deep learning is turning into an increasing number of vital in our everyday lifestyles. The appearance of deep learning in many applications in life relates to prediction and classification such as self-driving, product recommendation, classified ads and healthcare. Therefore, if a deep learning model causes false predictions and misclassification, it may do notable harm. This is largely a critical difficulty inside the deep learning model. In addition, deep learning models use big quantities of facts inside the training/learning phases, which in corporate touchy facts can motivate misprediction on the way to compromise its integrity and efficiency. Therefore, while deep learning models are utilized in real-world programs, it's mile required to guard the privateness facts used inside the model. The countless opportunities and technological abilities that system learning has added to the arena have concurrently created new safety dangers that threaten development and organizational development. Understanding system learning safety dangers is one of every of our contemporary technological time's maximum vital undertakings due to the fact the results are extraordinarily high, mainly for industries along with healthcare in which lives are at the line. We talk about the forms of system mastering safety dangers that you may stumble upon so you may be higher organized to stand them head-on.

Image forgery (IF) is a technique in which images are manipulated through several tampering software such as Photoshop, Adobe, Corel, etc., and it becomes difficult to discriminate between authentic and forged images. Conventional techniques suffered from the weakness that they can only extract specific kinds of features and can identify only one type of tampering. This chapter introduces deep learning methods especially convolutional neural network (CNN) models, ResNet-50, and MobileNetv2 for tampering detection. Two datasets are used—CASIA v1.0 and CASIA v2.0 for experiments. These datasets have been divided into 80% training set and 20% testing set and achieved an overall highest accuracy of 95%.

In recent years cyberattacks have become destructive and targeted. With technological advancements, diverse threats are launching in a sophisticated way that targets people to defraud them. Many web applications have been struggling to improve the reliability and security of their platforms to protect users from fraud, revenue, or malware. These attacks use malicious uniform resource locators (URLs) to attack web users. These URLs host unwanted content in the form of junk emails, phishing, or unauthorized drive-by downloads. Unsuspecting people click these phishing URLs and become victims of unethical anonymous activities like identity theft (personal or financial details) and installation of viruses. Therefore, it is necessary to detect malicious URLs accurately for resolving security issues. Traditional protection method, such as blacklisting, remains a classical technique for the detection of malicious URLs due to its simplicity but cannot detect unknown malicious URLs; hence, machine learning approaches are being used for achieving better results. This chapter aims to provide a structural understanding of popular feature extraction techniques and machine learning algorithms.