A detailed analysis of Deep Learning for Internet of Things (IoT) Security
IoT security helps keep the IoT systems safe. IoT systems are usually prone to network attacks such as denial of service (DOS) or spoofing. Spoofing means disguising a communication from an unknown source and package it as a known and trusted source. HyperText Transfer Protocol (HTTP) and Application Programming Interface (API) is a few critical channels of IoT devices that hackers can intercept. So, in order to full-proof the IoT devices from attackers, security is implemented.
IoT security refers to a technology segment used to safeguard the connected devices and networks in the IoT. The existing security mechanism also needs to be appropriately adjusted for better adaptation in the IoT networks. For instance applying existing defense mechanisms like encryption, authentication, access control, and network security will become challenging. Thus, to address the scaling requirements of IoT networks several changes would be necessary.
Some of the best IoT security practices are as follows:-
- Include secure design, threat modeling, and frequent security tests.
- Prepare a plan to scan the security incidents and respond to them quickly.
- Secure the internet gateway.
- Arrange a firewall.
- Keep the authentication key safe.
- Installation of the latest antivirus software.
As the information security standards and regulations across the global markets are becoming stricter, businesses need to design, create and control connected products in a safe manner.
This whitepaper includes:-
- The potential threats of an IoT system.
- The deep learning methods used in IoT security
- The security requirements in an IoT system.
- How are security attacks detected using deep learning methods?
- The various deep learning methods, used in IoT security.
This document contains details regarding product information, standards, and technical specifications. To know more download this whitepaper.
