Artificial Intelligence and Data Mining Approaches in Security Frameworks. Группа авторов

Chapter 13, in this current research, firefly algorithm has been used for optimizing maize crop yield by considering the various constraints and risks. This research investigates the development of new firefly algorithm module for predicting the optimal climatic conditions and predicts the crop cultivation output. As the pre-processing, the maize crop cultivation data for 96 months have been collected and provided as response to Minitab software to formulate the relational equation. The collected data have been stored in the cloud using IoT and the cloud has to be updated periodically for obtaining the accurate results from the algorithm.

      In Chapter 14, gestures are of two types as: static and dynamic sequences, this is where vision based techniques plays a vital role. The survey on the research study on the vision-based gesture recognition approaches have been briefed in this paper. Challenges in all perspective of recognition of gestures using images are detailed. A systematic review has been conducted over 100 papers and narrowed down into 60 papers on summarized. The foremost motive of this paper is to provide a strong foundation on vision based recognition and apply this for solutions in medical and engineering fields. Outlines gaps & current trends to motivate researchers to improve their contribution.

      In Chapter 15, we will cover a examine of diverse thoughts, attempts, efficiency and different studies trends in junk mail filtering. The history observe explains the packages of device gaining knowledge of strategies to clear out the antispam emails of main e mail service carriers like gmail, yahoo, outlook and so on. We can talk the e-mail unsolicited mail filtering techniques and sundry efforts made via various researchers in fighting the unsolicited mail emails via using device mastering strategies. Here, we talk and make comparisons within the strengths & weaknesses of already present machine learning algorithms & techniques and different open studies troubles in spam filtering. We might suggest deep gaining knowledge & deep adversarial getting to know as these technologies are the destiny to be able to capable of efficaciously deal with spam emails threats.

       Prof. Neeraj Bhargava Professor & HeadDepartment of Computer ScienceSchool of Engineering and System ScienceMDS University, Ajmer, Rajasthan, India

       Dr. Ritu Bhargava Assistant ProfessorDepartment of Computer ScienceSophia Girl’s College Autonomous Ajmer, Rajasthan, India

       Pramod Singh Rathore Assistant ProfessorAryabhatta College of Engineering and Research Center, Ajmer, Rajasthan, IndiaDepartment of Computer Science & EngineeringVisiting Faculty, MDS University, Ajmer, Rajasthan, India

       Prof. Rashmi Agrawal ProfessorManavrachna International Institute of Research and Studies, Faridabad, India

      1

      Role of AI in Cyber Security

       Navani Siroya1* and Prof Manju Mandot2

       ¹ M.Tech Scholar, Computer Science, MDS University, Ajmer, India

       ² Director, Department of Computer Science and IT JRN Rajasthan Vidyapeeth University, Udaipur, Rajasthan

       Abstract

      Borderless cyberspace as a part of “global commons” does not exist. Information breaches, ID theft, cracking the captcha, and other such stories proliferate, more so in times of the pandemic era, affecting people at a global level.

      Today where AI advancements, for example, deep learning, can be incorporated into cyber security to develop shrewd models for executing malware classification, intrusion detection and threatens intelligence sensing. The flip side of the coin shows how AI models have to confront different digital dangers, which upsets their sampling, learning models, and decision-making. The ever-expanding danger of digital assaults, cybercrimes, and malware attacks has grown exponentially with the evolution of artificial intelligence. Conventional ways of cyber-attacks have now taken a turning point; consequently, the attackers resort to more intelligent ways.

      This chapter discusses the cyber security needs that can be addressed by AI techniques. It talks about the traditional approach and how AI can be used to modify the multilayered security mechanism used in companies today. Here we propose a system that adds an additional layer of security in order to detect any unwanted intrusion. The chapter ends with deliberations on the future extent of artificial intelligence and cyber security.

      Keywords: Artificial intelligence, cyber security, machine learning, Botnet

1.1 Introduction

      Artificial Intelligence (AI) can be characterized as artificial decision making similar to human decision making, based on certain unique algorithms and related mathematical estimations. Cyber Security relates to measures taken to protect against digital assaults in the virtual world.

      Moreover, the job of AI is ever expanding in the modern world, where there is a looming threat to cyber security.

      With the headway in innovation, cybercrimes are also increasing and getting unpredictable. Cyber criminals are launching sophisticated attacks that are putting current security frameworks in danger. Thus, the cyber security business is evolving to satisfy the expanding security needs of organizations. But, these defensive strategies of security professionals may not live up expectations and may fall short of its proposed agenda sooner or later [1].

1.2 Need for Artificial Intelligence

      AI’s vital job is to offload work from human cyber security engineers presently, to deal with the depth and detail that humans cannot tackle effectively. Advancement in machine learning technology implies that AI applications can also automatically adapt to changes in threats and spot issues as they emerge.

      Cyber security needs that AI tools and platforms can help to meet:

      Data Extent

      People get confused immediately when confronted with huge amounts of log information and cautions delivered by the present frameworks. Simulated intelligence programming running on today’s powerful processors can go through more data in minutes than humans could handle in months. Thus, it can also account for issues and inconsistencies while taking care of enormous volumes of security information.

      Threat needles

      Cyber threat hunting is a constant proactive search through networks and data sets to detect threats that elude existing computerized tools.

Digital lawbreakers are now inside numerous frameworks, waiting to complete their attacks. They can frequently escape people. But AI can quickly examine different circumstances to detect the threat needles compared to malicious activity.

      Optimization of Response

      Artificial intelligence can accelerate recognition of certifiable issues, quickly cross-referencing various alerts and sources of security information. The priorities of the incidents to be dealt with will still be the domain of human cyber security experts but they can be further helped by AI systems that will increase speed of recognition and reaction times.

      AI arms race

      Cyber criminals today are already equipped with advanced AI techniques. AI technology in general can be a boon or a bane. Programmers can easily utilize the most recent tools to launch more sophisticated attacks, each one being more dangerous. It has become an arms