How Machine Learning Can Improve Cybersecurity

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Abdallah Shami, Western Engineering assosciate dean of research (acting) and professor, department of electrical and computer engineering.

In an increasingly connected and digital world, the internet has become an essential aspect of daily life. Unfortunately, the dependency on the internet to expedite communication, commerce and store information leaves individuals and organizations exposed to hackers with malicious intentions.   

To that end, numerous protection mechanisms such as firewalls, antivirus and malware programs, user authentication, and network intrusion detection systems (NIDSs) have been deployed but there is room for improvement. 

Typically, (NIDSs) analyze incoming network traffic for malicious activity but this work is based on the observation of pre-defined attack patterns. Because of this, they perform well with notorious signatures and patterns but are defenceless to new attacks due to their incapacity to detect new attacks by learning from past observations.  

A recent machine learning (ML)-based scheme from Western Engineering professor Abdallah Shami, in collaboration with fellow IEEE researchers, teases an innovative solution. 

“The proposed novel multi-stage network intrusion detection framework reduces the overall computational complexity of the deployed machine learning model—by reducing the number of features as well as the number of training samples the model needs—while enhancing its detection performance in terms of network attack detection accuracy,” said Shami. 

“Additionally, it accounts for a common problem facing public cybersecurity datasets (i.e., the imbalanced nature of the datasets) by applying an oversampling technique to provide more network attack samples to better learn attack patterns/characteristics.” 

Read the full story in the IEEE Innovation Spotlight. The findings were published in the journal IEEE Transactions on Network and Service. 

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