Artificial Intelligence is shaping the cybersecurity space.
In 2016, a new malware was detected every 4.6 seconds. By 2017, that alarming figure had grown even further to a new malware detection every 4.2 seconds. According to Symantec, one in every 13 URLs analyzed in 2017 was malicious. In October 2017, thousands of harmless cameras were used to produce a huge distributed denial-of-service (DDoS) attack that worked by sending billions of requests. Some of the affected websites include Amazon, Twitter, and Netflix. In April 2018, Facebook had to notify over 90 million users that their personal data might have been improperly shared.
However, the biggest threat facing most businesses and the cybersecurity industry is the lack of preparation for tackling the growing number and creativity of cyber attacks. By 2021, there will be nearly 3.5 million unfilled cybersecurity positions globally. If the current state of affairs continues, it means cybersecurity professionals will have to compensate for those empty positions either by working harder or for longer hours. A Spiceworks survey revealed that IT professionals already work an average of 52 hours per week and an overworked cybersecurity team will find it tough to respond to threats appropriately and effectively.
How artificial intelligence (AI) can help
Considering the increased manpower required to maintain effective levels of cybersecurity, the implementation of AI technology can be a significant turning point. AI-enabled systems prepare cybersecurity professionals for handling cyber-attacks swiftly and effectively.
Artificial intelligence and machine learning (ML) are connected more extensively across various industries and applications as computing power, storage capacities, and data collection increase. The vast amount of information can't be efficiently processed by humans without a significant expenditure of time and effort. With machine learning and AI, these massive amounts of data can be managed and processed in a fraction of time, helping professionals to identify and recover from cyber attacks.
Artificial intelligence (AI) techniques for cybersecurity
Artificial Intelligence techniques can proficiently identify even the tiniest changes in systems. These systems can act much earlier, based on a massive amount of data at a faster speed than humans.
An AI expert system is a computer system that mimics a human's capacity for decision making. For example, an expert system consists of two sub-systems: the knowledge base and the inference engine. The knowledge base refers to facts and illustrations in the real world, while the inference engine is an automatic reasoning system. The system evaluates the current situation, applies relevant rules and data, and adds new knowledge back into the system.
The AI-enabled security expert system follows a set of rules to combat cyber attacks. It checks with the knowledge base if there are known, safe and effective processes. If there is no such process in the knowledge base, the security expert system uses an inference engine to find the machine state, which consists of three states â?? safe, moderate and severe. Accordingly, the system alerts users about the states and uses inference to interpret the knowledge base.
A neural net, also known as deep learning is inspired by the functions of the human brain. The neurons in our brain are general purpose and domain-independent, which can learn any data.
Similarly, artificial neuron (Perceptron) can learn and tackle issues by combining with another perception. Perceptrons themselves learn to identify the entity on which they are trained by processing high-level raw data. When applied to cybersecurity, this system identifies whether a file is legitimate or malicious without human involvement.
This technique detects malicious threats and is able to undertake processes at an incredibly fast speed. Neural nets make an exact detection of new malware threats, bridge the dangerous gaps and arms organizations to tackle cyber attacks.
Intelligent agent (IA)
AI as an independent entity uses sensors to recognize movement and uses actuators to follow up on an environment. It then performs activities for accomplishing objectives. Intelligent agents may use the knowledge base to accomplish similar objectives and might be extremely simple or extremely complex.
The intelligent agent is formed against Distributed Denial of Service (DDoS) attacks. It requires an infrastructure to support the quality and interaction among intelligent agents. Multi-agent tools are an operative appearance of the cyber police.
Artificial intelligence systems are able to process large amounts of data very quickly, and accurately identify any anomalies that could indicate a malicious attack. Artificial intelligence provides flexible and robust solutions for cybersecurity and in the future, AI could benefit the cybersecurity industry in many other ways with more intelligent techniques. Clearly, the advances in data understanding, processing and handling using AI applications will greatly enhance the cybersecurity capability of systems that would use them, so the future of cybersecurity looks to be increasingly intelligent.