Artificial intelligence research
Artificial intelligence research paper
The use of ontology and artificial intelligence in the management and control of cybercrime is in a major increase since it is crucial in handling voluminous data which are very crucial for organizational performance. Currently, ontology is a process which allows the machines to understand domains and concepts which define the relationship between artificial intelligence and its application in the management of cyber crimes and other internal problems which affect the operations of an organization (In Yager, et.al, 2014). The combination of the ontology as well as the artificial intelligence can give a leeway in the determination of causes of cyber security, their associations and understand the level in which the model can handle cyber security problems (In Yager, et.al, 2014).
Therefore, in this proposal, the researcher aims at examining how Ontology Artificial Intelligence, can be integrated into the management of the cyber security and to determine how ontology aims at defining the relationships between cyber security threats and the use artificial intelligence to control the cyber threats from causing major problems to organization. The literature review is given to recognize the work others have done on this and a methodology to give the procedure of data collections and analysis so as to ensure that the research questions are answered, and the objectives are reached.
1.2. Research Questions
• How can ontology Artificial Intelligence be integrated into the management and control of the cyber security?
• How does ontology help to define the association between artificial intelligence and cyber security?
• What are some of the benefits and challenges of adopting Ontology Artificial Intelligence?
1.3. Research Objectives
• To determine how Ontology Artificial Intelligence can be integrated into the management and control of cyber security
• To examine how ontology helps to define the association between Artificial Intelligence and cyber security
• To clarify some of the benefits and challenges of adopting Ontology Artificial Intelligence
1.4. Purpose of the study
The primary aim of this study is to determine the role played by ontological principles and artificial intelligence in influencing the management and control of cyber security. These will helps the IT experts and other relevant authorities to manage and curb the effect of cybercrime on the management of the organization’s set of data.
Recent research in artificial intelligence
The use of Ontology Artificial Intelligence has taken center stage in the management and control of cyber security threats in various organizations. It has come to the realization to many organizations that the undertaking to keep the enterprise network safe is becoming challenging tasks to the IT experts and analysts (Harris, 2011). The cyber-attacks continue to grow over time as the attackers identify new methods in which they can use to capture, infiltrate and compromise various networks (In Yager, et.al, 2014). Therefore, it is through the use of Ontology Artificial Intelligence which can enable them to limit the damages since the manual process is proved to be time-consuming and expensive to implement.
2.2. Trends which drives the Ontology Artificial Intelligence Approach in an Organization
Due to the changes in the trends and mechanisms in which data are handled and managed by various organizations, the acquisition of Ontology Artificial intelligence has proved to be prudent since it ensures that the analysts handle large set of data as fast as possible thus enabling efficient data protection. According to Callan (2003), during enhancement of cyber security, there are various trends which make the organization to adopt the ontology Artificial Intelligence. These includes a rise in the number and severity of the cyber-attacks across the world, arising from zero-day attacks across organizations, due to the incline in the demand of the qualified Cyber Security professionals which tend to outsprint the supply hence leading to shortages which posit a lot of risks to myriad organizations (Callan, 2003). Besides, due to the high volume of data which is required to be collected, organized and analyzed, the managers and experts in various fields and departments have shown it wise to adopt the process (Callan, 2003).
2.3. Benefits and Challenges of Ontological Artificial Intelligence
According to Margulies (2004), there are various benefits which the Ontology Artificial Intelligence provides to an organization if it is properly integrated and implemented. These can include the replication of the human decisions, actions and activities without the human errors and does not face the human problems such as emotions, fatigue and limited time (Margulies, 2004). Besides, it has led to the growth of effectiveness and thinking about the potential risks associated with the advancement in the cyber security attacks in various parts of the world. Even though it leads to a lot of benefits, there are various challenges which it can still be experienced during its adoption. They include the work of the security professionals is made more grueling by the abundance of malware, distributed denial of service and botnets which are sold in the black markets (German Conference on Artificial Intelligence, In Timm , & In Thimm,2013). Besides, there are various difficulties which arise as a result of identifying the insider threats which posits a lot of pressure in an organization. Besides, there is a continuous attack on computers by various virus and malware which manipulate and interfere with the data.
Research paper of artificial intelligence
The chapter aims at providing the methods of collecting data, research design, the sampling size and the data analysis procedure. These will be done to provide the users of the information with a chronology of the work and how the research questions are answered so as to meet the objectives of the study.
3.2. Research Design
In this research, the researcher will use quantitative data which will aid in the examination of the secondary collected data that can only be collected in a numerical form. Besides, the qualitative analysis will be important to gather information which involves the people’s opinions and the interpretation of different phenomena (Jha, 2008). Besides, the research design clasps positivism proposition thus, the researcher gets incessant data from the secondary and primary data.
3.3. Sources of Data
In this thesis, a random sampling will be used to help in developing discussion for the users hence effectiveness in understanding various variables used. The main source of data for this study will be a secondary data from various journals, books, magazines and articles.
3.4. Data Analysis Procedure
The qualitative and quantitative analysis will be used after an efficient collection of data. The statistics will also be abridged to plaid its uniformity, accuracy, consistency and completeness and then arranged to enable coding process (Kothari, 2004). These thus enhance accuracy and eliminate errors as well as biases which might accrue during data analysis. The analysis of the secondary data will ensure that there is a valid evidence of how the Ontology Artificial Intelligence Approach influences the cyber security management and control.
3.5. Ethical Considerations
During the research process, the researcher will ensure that the guidelines as per the data collection, analysis and interpretations are adhered to without a compromise.
- Callan, R. (2003). Artificial intelligence. Basingstoke: Palgrave Macmillan.
- German Conference on Artificial Intelligence, In Timm, I. J., & In Thimm, M. (2013). KI 2013: advances in artificial intelligence: 36th Annual German Conference on AI, Koblenz, Germany, September 16-20, 2013 : proceedings.
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- Pacific Rim International Conference on Artificial Intelligence, Anthony, P., Ishizuka, M., & Lukose, D. (2012). PRICAI 2012: Trends in artificial intelligence ; 12th Pacific Rim International Conference on Artificial Intelligence, Kuching, Malaysia, September 3-7, 2012, Proceedings. Berlin: Springer.
- Yu, P. S., & Tsai, J. J. (2009). Machine Learning in Cyber Trust: Security, Privacy, and Reliability. Boston, MA: Springer-Verlag US.
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