There is a varied range of multi-disciplinary networks between artificial intelligence and cybersecurity. Artificial intelligence (AI) technologies like deep learning form a distinct interception by cybersecurity to initiate smart modeling for malware classification, threat sense intelligence, and intrusion detection. Decision making by artificial intelligence models faces cyber threats that interfere with the interface, sampling, and learning. For adequate and succinct preservation of sensitive information and technologies, AI models require a specified cybersecurity defensemechanism for combating adversary machine learning, protect privacy, and secure federate learning systems. In the following analysis, we seek to find the correlation between cybersecurity and AI.

Existing Applications for Artificial Intelligence Techniques

Intelligent Agent Applications

In unexpected outcomes, intelligent agents are independent computer-generated forces that establish communication amongst themselves to share data and aid in planning and executing responses. Examples of smart agent applications are shown below;

Multi-agent system for Worm Detection and Containment in Metropolitan Area Networks

Metropolitan Area Networks has recently experienced a surge in worm attacks, with active worms responsible for high-speed damage. Patches left by these worm ware remains available after wears, which elevates them to a top security concern in Metropolitan Area Networks (MAN).MWDCM is built to provide an automated reaction mechanism that executes automatic containment strategies blocking their propagation.

The following is an illustration of worm containment through a multi-agent system.

Fig a: Multi-Agent System for Security Auditing and Worm Containment

SAaaS Cloud Incident Detection System Security Audit as a Service

Introduced in 2011, SAaaS is based on autonomous intelligence agents mindful of the inbuilt business flows of deployed cloud instances, doing away with rigidity and supportive of the crosscustomer event monitoring for cloud infrastructure.

Artificial Immune System Applications

Known as AISs, these systems are employed to sustain stability in a constantly changing environment. They are immune-based intrusion detection techniques comprising immunocytes with features like self-tolerance, variation, and clone. They detect antigens as well. Forinstance, Hong presented an AIS-based hybrid learning algorithm for anomaly detection in computer systems in 2008.

Artificial Neural Network Applications (ANN)

In ANN, structural and functional aspects of neutral networks existing in a biological nervous system are stimulated in a computational mechanism.


In 2008, Chen designed NeuroNet, bestowed with collecting and processing disseminated information, addressingirregularities, managing alerts, and coordinating core network devices’ activities.

Improving Cyber Security for Artificial Intelligence

At the advent of 2017, a group of artificial intelligence researchers joined efforts in coining the 23 principles for artificial intelligence and a few included;

Safety: Where applicable and feasible, I systems should be secure and safe throughout their operational lifetime.

Race Avoidance): teams developing AI systems should actively cooperate to avoid compromise on safety standards.

Failure Transparency): If an AI system is responsible for a harmful activity, it should be possible to ascertain why.

Science-Policy Link: There should be a constructive and healthy exchange between AI researchers and policy-makers.

Take Away

Artificial intelligence has already provided crucial tools that can be routinely utilized by people worldwide, and its continuous improvement guided by the principles mentioned above can offer long-lasting solutions and empower people.