In the digital age, cyber-attacks and data breaches pose complex challenges for organizations operating in cyberspace. Machine learning offers a promising solution to detect and prevent these threats, addressing issues like malware, ransomware, fraud, and spoofing. This chapter explores machine learning's role in cybersecurity, discussing algorithms and their applications in intrusion and malware detection. It also examines challenges and limitations in employing machine learning for cybersecurity. Providing valuable case studies, the chapter serves as a useful reference for researchers developing intelligent security systems.

The rapid growth of the internet and the technological revolution in the information and communication sectors has created a gateway for users to connect online. With digital information being stored, security and privacy has been a major concern around the world to protect vital data. The modern tools and techniques used by cybercriminals perform malicious activities which aim at disrupting, distorting, and stealing sensitive data for self-financial gains. More complexities were added to the cyber world with the emergence of AI and ML algorithms in the smart phones. Cybercriminals take advantage of this barrier and build their network to commit cybercrimes across the globe. This chapter focuses on an audience that includes researchers, professionals, academicians and UG, PG students who are interested to know more about cybersecurity. This chapter aims to provide a brief overview of cybersecurity, cyber laws, cyber-attacks and security tools, objectives of cybersecurity, applications of cybersecurity, and so on. Finally, the future of cybersecurity along with some case studies are discussed.

: Ransomware, a form of malicious software originating from cryptovirology, poses a serious threat by coercing victims to pay a ransom under the risk of exposing their data or permanently restricting access. While basic ransomware may lock a system without causing harm to files, more sophisticated variants utilize cryptoviral extortion techniques. The danger of ransomware is significant, with ongoing discoveries of new strains and families on the internet and dark web. Recovering from ransomware infections is challenging due to the complex encryption schemes employed. The exploration of machine learning and deep learning methods for ransomware detection is crucial, as these technologies can identify zero-day threats. This survey delves into research contributions on the detection of ransomware using deep learning algorithms. With deep learning gaining prominence in cybersecurity, we aimed to explore techniques for ransomware detection, assess weaknesses in existing deep learning approaches, and propose enhancements using those deep learning algorithms. Machine learning algorithms can be employed to tackle worldwide computer security challenges, encompassing the detection of malware, recognition of ransomware, detection of fraud, and identification of spoofing attempts. Machine learning algorithms play a crucial role in assessing prevalent forms of cyber security risks. They are instrumental in identifying and mitigating attacks, conducting vulnerability scans, and evaluating the risks associated with the public internet. By leveraging machine learning, computer defense mechanisms can effectively identify and respond to various cyber threats. These techniques aid in fortifying systems against potential vulnerabilities and enhance the overall security posture. Research in this field investigates the utilization of cyber training in both defensive and offensive contexts, offering insights into the intersection of cyber threats and machine learning techniques.

In recent days, the smart city project has been emerging concept all over the world. In this process, the proper communication between the sensors and the smart devices, and identification of optimal path between sensors and mutation sensors in large geographical area is very difficult. The main objective has been considered to overcome the drawbacks as mentioned above. The proposed algorithm is efficient to provide integrated communication of IoT-based ubiquitous networking (UBN) devices to improve in large geographically distributed area. The data storage capacity and accuracy of sensors and smart devices are enhanced using the proposed algorithm. The communication latency and data pre-processing of IoT-based UBN nodes deployed in smart cities are reduced. The proposed algorithm also analyses the performance of IoT-based UBN nodes by considering geographical testbeds that represent a smart city scenario. The analysis and comparison are carried out by considering the heuristic parameters. The proposed algorithm will also optimize the communication latency and data pre-processing time by analyzing various sensitivity levels by considering the heuristic parameters in different probability of nodes in smart cities. The proposed IoT-based UBN computing devices improve the objective function due to proper integrated communication between the sensors using a machine learning based regression algorithm. The proposed algorithm also identifies the probability sensitivity of optimal path between smart devices in a smart city thereby enhancing the connectivity of mutated sensor nodes. The proposed algorithm also enhances the probability of smart device connectivity to improve the accuracy, flexibility and large geographical coverage using machine learning based regression algorithm.

Researchers have developed an innovative approach to ensure seamless connectivity in ubiquitous networks with limited or irregular network coverage. The proposed method leverages advanced network technologies and protocols to seamlessly establish and maintain network connections across various environments. It integrates multiple wireless communication technologies and dynamic network selection algorithms, overcoming issues like poor reliability, limited scalability, and security problems. Compared to existing solutions, the method exhibits improved connection A Novel Approach by Integrating Dynamic Network Selection and Security Measures to improve Seamless Connectivity in Ubiquitous Networks 30 Volume 14 (2024), Issue 1 handover efficiency, network throughput, and end-to-end delay. Considering user mobility, network availability, and quality of service needs, it makes informed decisions about the most suitable network connections. The proposed method is expected to significantly impact the development of future ubiquitous networking solutions.