When it comes to improving the overall experience of the users among broad aspects of life such as;
- Improving the shopping experience,
- Reducing street traffic
- Providing with voice-activated assistants
Artificial intelligence and Machine Learning continue to deliver their diligent services in all these walks of life. However, a far more important task is carried out through these interpretations, and that is improving cybersecurity around the globe. Mechanical or human input can’t be there to analyze or interpret various cybersecurity threats, and that is why the need for these AI or ML systems arises. ML and AI can both increase the overall productivity of the cybersecurity systems, provide with on-board support 24/7, and, most importantly, offer safety and security against a series of potential cyber threats.
We will be discussing the role of both systematic approaches used in the IT industry separately. But before we get to that, we will provide you with a general difference between the two.
AI VS ML
It is essential to know that AI is the largest commodity that provides diligent services in broad aspects of the IT industry. AI or artificial intelligence shares a particular theme that is to create various computer-based or scripted programs that are intelligent enough and can provide with human-like functions at somewhat effective rates.
While Machine Learning is the subset of the AI and refers to the adaptive methods or related technologies used by computers or infrastructure to learn from the vast amounts of data that are being interpreted by these, human-built algorithms are used to accomplish this task. Data mining is essentially the very same case; it uses AI and applications of machine learning to continue to interpret the large sets of data without any manual insight whatsoever.
Let’s get back to the role of both these separately;
Role of AI in the development of Cyber Security
Artificial intelligence, as described above, is the dedicated assembly of like working or scripted computers, networks, or other systems to execute or carry out various computer-based tasks only like a human brain. This hasn't been a straightforward journey to take AI technology where it is today. The debacle of using AI technology within the cybersecurity oriented systems is far way from being over and is still debated open-heartedly. The adventures of AI to incorporate human learning and insight can be dated back to mid-1900.
Although many functions are being provided by AI or artificial intelligence such as;
- Understanding various types of languages and speech recognition
- Smart home devices that use AI technologies tend to make lives more comfortable and far better
- Travel associations and integration techniques are provided by AI technologies around the globe such as assisting through various routes
Artificial intelligence can’t just recognize the different unwanted or suspicious behaviors among the security layout or network infrastructure of organizations and add a decent security layer or firewall to block the attack or cover for a breach. This is where the applications of the Machine Learning comes into the practice; it can analyze the previous data libraries and use them to build better security inputs to cover for various cyber threats.
The best line of defense that Artificial Intelligence can provide is to recognize the disrupted or unusual behavior from normal user behavior. By using this statistical or scripted method, the authorized and unauthorized entities can be separated from each other, and necessary actions can be taken place accordingly.
After analyzing this raw data or behavior of the user, a standard is built against which the new entities are cross-referenced; after being examined, the dedicated user is either allowed to continue surfing or blocked simultaneously. All such patterns help the machine learning to recognize a particular threat to the system and use dedicated actions in this regard.
Hackers and cybercriminals are using their way best to exploit any present loopholes within a security system and gain access to the unsolicited data or personal information of the user. Security software or tools can even be hacked or manipulated to look the other way when the cybercriminals perform their illegal actions. This is why there is a need for advanced artificial intelligence systems to be upgraded continuously so the new threats can be managed and stopped in their execution.
Role of Machine Learning in Cybersecurity
Machine learning is described as a subset of artificial intelligence. The machine learning can learn from the large scraps of data using the human-built algorithms, and this new information, when stored, can be used to perform various tasks. The brain, of course, is the scripted or human-built algorithms that help to adapt towards new data entities and interpret any change in the current stream of data to change their decision accordingly.
Machine learning is crucial to the success of the cybersecurity systems because if the cybercriminals continue to use the same type of attack over and over again, then they will be red-flagged by the AI systems abruptly. What if they used the different sets of attacks each time but for the same motive? Would they walk free and operate out of their free will of exploiting data security systems? Not precisely, the machine learning takes over here, it interprets these distributed modes of attack and recognize the endgame or intent of the cybercriminals and block their access at once.
Multiple types of learning behaviors that are used for programming or configuration of the machine learning systems include;
- Supervised learning—the systems or computer infrastructure is provided with scripted parameters to compare various data inputs against it. Half of the work is already done here.
- Unsupervised learning—computer or AI infrastructure is only fed with raw data and make assumptions and formulate particular standards accordingly
If you want to pursue a career in the field of AI or ML, then you will require specialized cybersecurity training to gear yourself up with elements needed to work, interpret and configure various AI and ML-based systems.