Artificial Intelligence to Enhance Smart Grid Cybersecurity

Artificial Intelligence can be leveraged to strengthen the cybersecurity of the smart grid

Artificial Intelligence to Enhance Smart Grid Cybersecurity: Even though security solutions are becoming modern and robust, cyber threats are ever-evolving and always at their peak. The main reason is that the conventional methods to detect malware are falling apart. Cybercriminals regularly devise smarter ways to bypass security programs and infect the network and systems with different kinds of malware.

The thing is. Currently, most antimalware or antivirus programs use the signature-based detection technique to catch the threats, which is ineffective in detecting new threats. This is where Artificial Intelligence can come to the rescue. Researchers and security program manufacturers are trying to use the potential of Artificial Intelligence to develop solutions that can detect and fix sophisticated cyber threats and control data breaches. Artificial Intelligence has changed every field it has been introduced to. It can hugely impact cybersecurity in many ways.

The smart grid relies heavily on information and communication technologies (ICT) to manage and control electricity generation, transmission, and distribution. This increased reliance on ICT has made the smart grid more vulnerable to cyberattacks, leading to disruptions in power supply, financial losses, and even physical damage to critical infrastructure. To address these challenges, AI can be leveraged to strengthen the cybersecurity of the smart grid by providing advanced threat detection, response, and mitigation capabilities.

Applications of Artificial Intelligence in Cybersecurity

Here are some of the crucial applications of Artificial Intelligence in Cybersecurity:

Vulnerability Management

Currently, security solutions wait for the vulnerabilities in the IT infrastructures and then take action on them, depending on their nature. The approach becomes different from AI and ML-enabled tools. AI-based systems are proactive in detecting vulnerabilities. They can analyze the pattern and discover the loose ends that can be the potential vulnerability. By recognizing the attackers' pattern, infiltrating methods can be found, and it becomes easy to distinguish when and how any exposure would make its way to the network or system. 

Improving the Authentication

Most organizations and individuals still depend on the traditional authentication method of entering the login id and password. Let us face it; very few people are serious about creating a unique and strong password. Over that, most people use the same passwords for all or most of their accounts. Such practices can lead organizations or individuals to serious security risks. However, with modern biometric authentication methods like face and iris recognition, login authentication has become highly secure and comfortable. Using AI in biometrics has ensured that cybercriminals cannot hack them. 

Behavioral Analysis

Another great advantage of Artificial Intelligence in cybersecurity is its behavioral analysis ability. AI can develop a pattern by accessing users' working methods. If any malware is introduced in the system, the working way would be changed, and that's where the AI would detect the abnormality and report it to the authorities. The exceptions could be the unusual use of the internet, change in the typing speed, increment in the background activities, and more.  

Controlling Phishing is one of the most common cyber-attacks hackers use to capture login credentials or introduce malware into the system. Artificial Intelligence can be of great help in detecting and preventing phishing attacks. AI can see the most common phishing sources and report them to the system to prepare for the defense against them. AI can easily recognize the difference between a fake and a legitimate website in no time. It can also analyze the phishing pattern according to the specific geographical location.  

Threat Hunting As mentioned, traditional security programs use signature indicators to detect threats. This technique is only effective with the already encountered attacks and becomes useless when reporting threats that have never appeared. Using AI, new threats can be recognized quickly. However, with it, the false-positive cases would also increase. Both the traditional detecting method and the AI behavioral analysis detection must be combined to eliminate the number of false positives.