Artificial Intelligence and Robo Hunter replace traditional e-security solutions

The approach between operational and digital technologies is well under way, driven by Internet usage models. The digital empowerment of both legacy and modern devices, sensors and other connected devices offers unprecedented benefits.


Real-time real-time data access across the network is fast and can be addressed to deliver insights and business benefits. This cycle increases productivity, reduces operating costs, achieves higher levels of security, and improves overall decision-making.

While profits are widespread and credit rates are increasing rapidly, there is a negative side to this fast-growing trend. Many sensor manufacturers are not doing enough to secure their products by not including encryption at the product development stage. Are lightweight and low-volume products, it may not be possible to add more security at a later stage.

This inherent lack of large-scale, object-based networks will in the future lead to the creation of phishing techniques to disrupt malicious malicious software by having real and fake identities for users.

Converged networks, including the SCADA system, operational technologies, and Internet infrastructure, will see a wider shift and huge security gains through phishing techniques.

Phishing techniques create thousands of false credentials for users in conjunction with real user identities. Once a threat factor exists within an organization's network, it can not distinguish between authentic and counterfeit identity credentials.

Because there are many fake user ID documents distributed, so the potential for interaction with counterfeit data to authenticate user identity and issue an intrusion alert is much greater, then incident response procedures are initiated, and the large number of false credentials resulting from phishing techniques It facilitates pattern tracking, allowing internal teams to re-create the attack pattern and access point.

To further enhance e-security defenses, digitally migrated organizations will begin to take advantage of the power of artificial intelligence and automated learning to secure their networks. These highly popular techniques are identified by programmers and based on a set of algorithms, limiting the amount of self-learning.

Automated learning applied to e-security is traditionally driven by algorithms that provide instructions on the types of malware and related behavior within internal networks. At the present time, automated learning will be replaced by deep learning techniques and application to e-security.

With deep learning techniques, e-security applications are supported by self-learning techniques, user behavior is monitored over a period of time, a user profile is created, this file is dynamic, deep learning techniques continue to add patterns to that file, The profile is embedded in a particular user, and deep learning applications generate very precise patterns and analysis of end-user activities.

The presence of a threat agent within the network using default credentials will have a perverted user pattern. This mode of access to the network, monitored through behavioral analyzes, will trigger a security alert alert without delay.

Such examples of this proactive and rapid approach to securing converged and transient networks can take the behavioral analysis applied to e-security to a new level. With these obvious gains, e-security providers will continue to incorporate deep learning techniques into their products next year.

Artificial intelligence techniques will create a new generation of proactive and defensive electronic security products called Robo-hunters, and thanks to artificial intelligence, robot hunters hunt down automated threats and scan the organization's environment for potential threats. Because this technique is based on predictive behavioral analysis, On normal network activity behavior.

Robot Robo Hunter examines the organization's environment for any changes that may indicate a potential threat while examining the environment, learning from what they discover and making the required correction, and thus making decisions on behalf of humans, helping to provide long-term expectations for the security department, Threat information and tracking within the network.

The threat landscape is moving very quickly, very complex and with large stakes, depending on contemporary technologies alone. Artificial intelligence coupled with predictive analysis and advanced levels of computing, as well as a reliable safety partner, will provide satisfaction in the not too distant future.