Wired LAN and Wireless LAN Attack Detection Using Signature Based and Machine Learning Tools

2017 ◽  
pp. 15-24 ◽  
Author(s):  
Jaspreet Kaur
Keyword(s):  
Machine Learning ◽  
Wireless Lan ◽  
Attack Detection ◽  
Learning Tools

Wired LAN and Wireless LAN Attack Detection using Signature Based and Machine Learning Tools

2017 ◽  
Vol 1 (1) ◽  
pp. 63-74
Author(s):  
Shanmugapriyan Thiagarajan ◽  
◽  
Kokula Krishna Hari K ◽  
Daniel James ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Wireless Lan ◽  
Attack Detection ◽  
Learning Tools

scholarly journals Experimental Cyber Attack Detection Framework

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1682
Author(s):  
Cătălin Mironeanu ◽  
Alexandru Archip ◽  
Cristian-Mihai Amarandei ◽  
Mitică Craus
Keyword(s):  
Machine Learning ◽  
Attack Detection ◽  
Learning Tools ◽  
Cyber Attack ◽  
Network Access ◽  
Public And Private ◽  
Real Time Traffic ◽  
Attack Pattern ◽  
Attack Patterns ◽  
Novel Concept

Digital security plays an ever-increasing, crucial role in today’s information-based society. The variety of threats and attack patterns has dramatically increased with the advent of digital transformation in our lives. Researchers in both public and private sectors have tried to identify new means to counteract these threats, seeking out-of-the-box ideas and novel approaches. Amongst these, data analytics and artificial intelligence/machine learning tools seem to gain new ground in digital defence. However, such instruments are used mainly offline with the purpose of auditing existing IDS/IDPS solutions. We submit a novel concept for integrating machine learning and analytical tools into a live intrusion detection and prevention solution. This approach is named the Experimental Cyber Attack Detection Framework (ECAD). The purpose of this framework is to facilitate research of on-the-fly security applications. By integrating offline results in real-time traffic analysis, we could determine the type of network access as a legitimate or attack pattern, and discard/drop the latter. The results are promising and show the benefits of such a tool in the early prevention stages of both known and unknown cyber-attack patterns.

A Comparative Study of Different Machine Learning Tools

2019 ◽  
Vol 7 (4) ◽  
pp. 184-190
Author(s):  
Himani Maheshwari ◽  
Pooja Goswami ◽  
Isha Rana
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Learning Tools

Improved nutrient management in cereals using Nutrient Expert and machine learning tools: Productivity, profitability and nutrient use efficiency

2021 ◽  
Vol 192 ◽  
pp. 103181
Author(s):  
Jagadish Timsina ◽  
Sudarshan Dutta ◽  
Krishna Prasad Devkota ◽  
Somsubhra Chakraborty ◽  
Ram Krishna Neupane ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nutrient Management ◽  
Nutrient Use Efficiency ◽  
Learning Tools ◽  
Nutrient Use ◽  
Use Efficiency

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-32
Author(s):  
Daniel Buschek ◽  
Charlotte Anlauff ◽  
Florian Lachner
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
User Study ◽  
Concept Development ◽  
Lessons Learned ◽  
Design Tool ◽  
Learning Tools ◽  
Interface Elements ◽  
Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

scholarly journals Organizing workers and machine learning tools for a less oppressive workplace

2021 ◽  
Vol 59 ◽  
pp. 102353
Author(s):  
Amber Grace Young ◽  
Ann Majchrzak ◽  
Gerald C. Kane
Keyword(s):  
Machine Learning ◽  
Learning Tools
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