Big data classification: problems and challenges in network intrusion prediction with machine learning

By Shan Suthaharan. PERV, 2014

358 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/469460d71ede47dc1082a3123e39f84dc33ba2cc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Naeem Seliya
2. Aunn Raza
3. Ala Al-Fuqaha
4. Hunain Arif
5. Bin Jiang
6. Amir Hussain
7. Randall Wald
8. Alfred Stein

In Computer Networks and Communications

1. Alexandra L’Heureux
2. Ala Al-Fuqaha
3. Miriam A. M. Capretz
4. Taghi M. Khoshgoftaar
5. Junaid Qadir
6. Petros Spachos
7. Maryam Mousaarab Najafabadi
8. Flavio Villanustre

Citations in the corpus

• Deep learning applications and challenges in big data analytics (2015, background): “In some Big Data domains, the input corpus consists of a mix of both labeled and unlabeled data, e.g., cyber security [59], fraud detection [60], and computer vision [45].”
• Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, background): “Therefore, this is a challenge of IoT security to adopt an intelligent and real-time updated machine learning algorithm to detect unknown attacks [190], [191].”
• Machine Learning With Big Data: Challenges and Approaches (2017, background): “Suthaharan [120] discussed issues related to combining machine learning, including lifelong learning, with Big Data technologies such as Hadoop and Hive.”
• Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, background): “…demands of supervised ML for labeled networking data, which is expensive to curate at scale (since labeled data may be unavailable and manual annotation prohibitively inconvenient) in addition to being suspect to being outdated quickly (due to the highly dynamic nature of computer networks) [13].”
• Visualizing Big Data with augmented and virtual reality: challenges and research agenda (2015, neutral): “solutions for complex data visualization [151].”
• Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges (2015, background): “Suthaharan (2014) even argued that 3Vs cannot support early detection of big data characteristics for its classification and proposed 3Cs: cardinality , continuity , and complexity .”
• Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics (2017, background): “There have been a few review papers on big data in general [2], [18] [26],”