Analysing Influential Factors in Student Academic Achievement: Prediction Modelling and Insight
Fahmida Faiza Ananna,
Ruchira Nowreen,
Sakhar Saad Rashid Al Jahwari
et al.
Abstract:The fascination with understanding student academic performance has drawn widespread attention from various stakeholders, including parents, policymakers, and businesses. The 'Students Performance in Exams' dataset, available on platforms like Kaggle, stands as a treasure trove. It extends beyond test scores, encompassing diverse student attributes like ethnicity, gender, parental education, test preparation, and even lunch type. In our tech-driven age, predicting academic success has become a compelling pursu… Show more
This chapter explores the topic of a novel network-based intrusion detection system (NIDPS) that utilises the concept of graph theory to detect and prevent incoming threats. With technology progressing at a rapid rate, the number of cyber threats will also increase accordingly. Thus, the demand for better network security through NIDPS is needed to protect data contained in networks. The primary objective of this chapter is to explore the concept of a novel graph based NIDPS through four different aspects: data collection, analysis engine, preventive action, and reporting. Besides analysing existing NIDS technologies in the market, various research papers and journals were explored. The authors' solution covers the basic structure of an intrusion detection system, from collecting and processing data to generating alerts and reports. Data collection explores various methods like packet-based, flow-based, and log-based collections in terms of scale and viability.
This chapter explores the topic of a novel network-based intrusion detection system (NIDPS) that utilises the concept of graph theory to detect and prevent incoming threats. With technology progressing at a rapid rate, the number of cyber threats will also increase accordingly. Thus, the demand for better network security through NIDPS is needed to protect data contained in networks. The primary objective of this chapter is to explore the concept of a novel graph based NIDPS through four different aspects: data collection, analysis engine, preventive action, and reporting. Besides analysing existing NIDS technologies in the market, various research papers and journals were explored. The authors' solution covers the basic structure of an intrusion detection system, from collecting and processing data to generating alerts and reports. Data collection explores various methods like packet-based, flow-based, and log-based collections in terms of scale and viability.
The rapid proliferation of drones, coupled with their increasing integration into various aspects of our lives, has brought to the forefront a myriad of ethical considerations in the realm of cybersecurity. This chapter delves deep into the intricate web of ethical challenges surrounding drone cybersecurity, aiming to provide a comprehensive understanding of this critical issue. The introduction sets the stage by highlighting the essential role of ethics in drone cybersecurity, emphasizing the need for responsible decision-making in an age where drones are omnipresent. It lays out the scope, objectives, and key concepts of the research, underscoring the contributions it makes to the field. The core of the chapter explores the ethical principles underpinning cybersecurity and elucidates how these principles can be applied to the domain of drone technology. The authors delve into the delicate balance between security and privacy, discussing the ethical implications of data collection, retention, and surveillance in the context of drones.
The rapid proliferation of drone technology has ushered in a new era of innovation and convenience across various industries. However, this technological advancement has also given rise to a host of cybersecurity challenges that demand proactive attention. This chapter delves into the dynamic landscape of drone security, exploring the evolving trends and emerging threats that shape this field. Beginning with an overview of drone technology advancements, including the integration of artificial intelligence and machine learning, this chapter examines their implications for cybersecurity. It then scrutinizes the growing influence of drone swarms and autonomous operations, pinpointing the associated cybersecurity challenges and strategies for securing these systems. The chapter explores the expansion of drone operations beyond visual line of sight (BVLOS) and the cybersecurity risks involved, emphasizing the importance of secure communications and legal considerations.
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