Neural Network-Based IMAP Interpreter for Juniper Switches in Bring Your Own Device (BYOD) Networks

Recently, our London office faced a challenging problem with the increasing use of Bring Your Own Devices (BYOD) on the company network. While this policy has allowed for greater flexibility and productivity amongst employees, it has resulted in an overwhelming amount of mobile devices being connected to the wireless network. We saw an increase in network congestion, resource consumption, and security risks. After several brainstorming sessions, we arrived at a solution that involved using neural networks, IMAP, and Juniper switches.

Understanding the Problem

In BYOD environments, hundreds of new devices join the network daily which increases the load on the network infrastructure exponentially. As a result, traditional solutions such as role-based access control or MAC address filtering provided little to no help in mitigating network bottlenecks. Network administrators were burdened with manually identifying each device and doing manual configurations for each one. The sheer volume of devices made detection and configuration almost unmanageable.

Our engineers proposed using advanced Machine Learning models such as Deep Neural Networks to analyse traffic data from switches and identify mobile devices that were connecting to the network. This would enable us to dynamically configure switches and monitor traffic based on device types and usage patterns.

Our Proposed Solution

The proposed system consists of two intelligent entities: the first being a neural network-based IMAP interpreter, and the second being a Juniper switch that uses link aggregation groups (LAGs) to manage traffic from mobile devices.

Neural Network-Based IMAP Interpreter

We trained a multilayer perceptron (MLP) neural network on a large dataset of IMAP protocol interactions and mobile device traffic patterns from our BYOD environment. This enabled us to build an algorithm that could interpret the IMAP traffic between client devices and email servers, making it possible to identify the software and hardware characteristics of connecting devices in real-time.

To accomplish this, we first extracted the feature vectors from each email transaction by considering all the columns of the IMAP messages exchanged between the client and server. We then applied a sequence of filters, including arithmetic encoding, normalization, feature selection, and dynamic scaling, to construct a reduced feature space manageable by the MLP.

The resulting model was capable of distinguishing between different types of email clients and mail servers, as well as detecting anomalies in email transactions. When this is used in conjunction with the second part of our solution, we can dynamically reconfigure the network switches based on device activity, resource usage, and security compliance.

Juniper Switch Using LAGs

We implemented Juniper EX4550 Series Ethernet Switches for link aggregation features and reduced connection times between switch ports. The switches are manipulated by the neural network-based IMAP interpreter to invoke specific configurations at runtime, using either the NETCONF or RESTCONF protocols depending on availability and scheme compatibility. Network administrators can set up rules for specific mobile devices using JNC Service Automation Frameworks for Junos APIs, which can communicate directly with the switches to configure MAC limits, authorization policies, and bandwidth allocation as required.

Conclusion

Our solution shows how the combination of Machine Learning techniques and Juniper switches can be adapted to solve problems in full-on BYOD environments, driving unprecedented performance and flexibility. By using the ML algorithms models, it becomes possible to manage network resources dynamically and automatically without human intervention, improving both efficiency and security. However, the challenge remains to develop these complex systems to be easy-to-use and accessible by all network administrators. As a tech company, we believe that this is the way forward to run complex IT environments with maximum reliability and security!