Cyber Security and Network Security

Chapter 3includes that in the last few decades, cyber-attacks have become far more common. According to statistics, 12.4 million attacks were documented in 2009, and this number has since climbed to 812.67 million known occurrences in 2018. To be fair, these are merely the documented cases; there are many more. Small cyber attacks to massive Ransom ware attacks or a mix of several complex cyber attacks that include advanced exploitation techniques and persistence capacity for long-term infiltration campaigns. However, the deployment of malware was a common thread in all of the cyber attacks that have occurred thus far. To counter these attacks, we must first comprehend malware’s basic structure, functionality, and impacts on the target. This paper gives an in-depth look at malware architectures by studying the malware using a technique known as malware analysis, as well as other related methods that vary based on the type of malware and a closer look at several types of malware, as well as certain well-known malware methods.

Chapter 4discusses that fraud is one of the most common sources of substantial financial consequences in today’s society, not just for businesses but also for individual customers. The extraction of user profiles based on previous transaction data and then deciding whether or not an incoming transaction is a fraud based on those profiles is an important approach of detecting fraud. The suggested block-chain technology enables certified users to securely store, review, and exchange digital data, facilitating the development of trust, integrity, and transparency in online commercial connections. Block-chain systematically examines the resilience of block-chain-based reputation systems, with a focus on the secure and reliable extraction and transfer of data to customers. Block-chain uses cryptographic hashes generated from summarized shopping blocks that are signed and sent to enable a safe and secure online buying experience without the need for third-party intervention.

In Chapter 5, it is shown that the demand for blockchain-based identity management systems is especially evident in the internet age; we’ve been dealing with identity management issues since the internet’s inception. Privacy, security, and usability have all been cited as major concerns. User identities are organized using identity management systems (IDMSs), which also manage authentication, authorization, and data interchange over the internet. In addition to a lack of interoperability, single points of vulnerability, and privacy concerns, such as allowing bulk data collection and device tracking, traditional identity management systems suffer from a lack of interoperability, single points of vulnerability, and privacy concerns. Blockchain technology has the potential to alleviate these problems by allowing users to track who owns their own IDs and authentication credentials, as well as enabling novel information ownership and administration frameworks with built-in control and consensus methods. As a result, the number of blockchain-based identity management solutions, which can benefit both enterprises and clients, has been fast expanding. We’ll classify these frameworks using scientific criteria based on differences in blockchain architecture, administration methods, and other important features. Context is provided by scientific classification, which includes the depiction of significant concepts, evolving principles, and use cases, as well as highlighting important security and privacy concerns.

In Chapter 6, the concept of feed forward networks is introduced which serve as the foundation for recurrent neural networks. Simple writing analysis is the best analogy for RNN, because the prediction of the next word is always dependent on prior knowledge of the sentence’s contents. RNN is a form of artificial neural network that is used to recognize a sequence of data and then analyze the results in order to predict the outcome. The LSTM is a type of RNN that consists of a stack of layers with neurons in each layer. This article also goes into the issues that each technology has as well as possible remedies. Optimization algorithms alter the features of neural networks, such as weights and learning rates, to reduce losses. Optimization Algorithms in Neural Networks is one of the sections. A section dedicated to some of the most current in-depth studies on Steganography and neural network combinations. Finally, for the prior five years, we give an analysis of existing research on the current study (2017 to 2021).

In Chapter 7, it has been found that cyber physical systems (CPS) will be used in the majority of real-time scenarios in the future. The use of such technologies is unavoidable in order to make the world smarter. However, as the use of such technologies grows, so does the need for improved privacy. Users will not be easily used to such systems if the privacy component is compromised. Because Cyber Physical Systems use a variety of heterogeneous sensor data sources, incorporating a high level of privacy is becoming increasingly difficult for system designers. The applicability of the precise penalty function and its benefits in increasing the privacy level of cyber physical systems will be presented in this chapter. We’ll compare this to existing privacy-preserving strategies in cyber-physical systems and discuss how our suggested privacy framework could be improved in the future.

In Chapter 8, the increasing demands for the preservation and transit of multi-media data have been a part of everyday life over the last many decades. Images and videos, as well as multimedia data, play an important role in creating an immersive experience. In today’s technologically evolved society, data and information must be sent rapidly and securely; nevertheless, valuable data must be protected from unauthorized people. A deep neural network is used to develop a covert communication and textual data extraction strategy based on steganography and picture compression in such work. The original input textual image and cover image are both pre-processed using spatial steganography, and then the covert text-based pictures are separated and implanted into the least significant bit of the cover image picture element. Following that, stego-images are compressed to provide a higher-quality image while also saving storage space at the sender’s end. After that, the stego-image will be transmitted to the receiver over a communication link. At the receiver’s end, steganography and compression are then reversed. This work contains a plethora of issues, making it an intriguing subject to pursue. The most crucial component of this task is choosing the right steganography and image compression method. The proposed technology, which combines image steganography and compression, achieves higher peak signal-to-noise efficiency.

Chapter 9shows the number of mobile network-connected devices is steadily increasing. The 5G network will theoretically provide a speed of 20 gigabits per second, allowing customers to access data at a rate of 100 megabits per second. Around the world, there are estimated to be 5 billion gadgets. With the advancement of wearable technology, a typical client can now carry up to two network-connected devices or engage in D2D communication. Clients are attracted to the 5G network because it advertises reduced inertness information correspondence, faster access and data transfer rates, and a more secure nature. As the number of supporters grows, concerns about information and computerized assurance will grow in order to keep up with the integrity of data security. Similarly, with any type of data security, there are always concerns about the safety of clients and their sensitive information. This chapter will discuss how to secure the diverse structures that are associated with networks, where these networks are vulnerable to compromise, well-known attack tactics, and how to avoid technical discrepancies.