Atharva Joshi Data Scientist | AWS Certified Machine Learning Specialist | Finalist at the 9th CSI National Student Project Awards 2020 | Entrepreneur

Deep Learning & Network Security

2 min read

A possible fruitful combination for efficient network security?

Cyber-security & security in Wireless Sensor Networks (or WSNs) are topics of considerable focus in today’s world, which is switching to digital modes of transactional protocols and methods. However, their wireless nature makes them prone to an immense number of attacks, which, when exploited, can lead to disastrous results. There have been significant advancements in dealing with attacks at a professional level of knowledge in network security. This could contribute in either or both of the two ways: Detection & Elimination (of malicious entities). But, doing so has resulted in the generation of 2 broad and significant issues.

Issues:

1. Accuracy: These methods often are newbies and can provide only a certain level of accuracy, which most of the time isn’t enough to deploy them at practical sites.

2. Speed/Efficiency: Methods that are expected to provide greater accuracy in either or both of the phases, viz. detection and elimination, take a longer time to execute, which is usually beyond the feasible and acceptable time frame to allow execution of subsequent actions by the administrators.

Machine Learning As A Solution:

Machine learning seems to be an opportunistic solution that can be explored to address the above issues. But, machine learning does not only limit to merely applying established algorithms to cyber-entities. According to some newly done research studies, some scholars have presented this problem to the AI and deep learning community with a broader and new perspective to think upon this by providing related datasets.

While identifying such attacks is a very cumbersome and challenging process, deep learning techniques can be a helping hand with respect to the issues mentioned above.

Limited availability of datasets:

Though deep learning or machine learning can prove to be valuable techniques, another problem is the limited availability of datasets. Datasets required for analysis by the deep learning models are not available in appropriate forms. Several challenges need to address to be able to provide the community with an abundance of such datasets, including but limited to the metrics and the labels in consideration. This brings us to the next point.

Lack of skilled domain experts:

The current scenario clearly shows that experts exist only in the exclusive fields, i.e., AI or Network Security. Due to the minimal knowledge of inter-domain skills in these two fields, the ability to create and work with the datasets, machine learning models, and network security concepts simultaneously has been hindered quite a lot. This results in a lack of labeled samples, numerous labeling errors as well as unbalanced datasets.

What can be done?

There lies an immense opportunity in combining the concepts of deep learning with network security and cyber-security to generate effective solutions to problems such as detection and elimination of attacks. The possible obstacles that can hinder this process have been discussed earlier. Resolution of these obstacles can be considered a milestone in the journey. The treasure that lies within this combination can be harnessed and brought into action, which can pave the way for incredible and immensely efficient algorithms which will not only give real-time security solutions and give multi-layered security to the wireless sensor networks but also reduce the errors caused during the frequent manual interference for corrective actions in the network.

Thanks for reading. I’ll be coming up with another article to give an insight into how we have contributed to minimizing the problems of the attacks on wireless sensor networks. Comments and inputs are welcomed.

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