In recent years, the technology that uses HPC (High Performance Computing) has evolved enormously, including previously unexpected fields, with the development of artificial intelligence to support cybersecurity.
The enormous rapid calculation capacity has made many technological developments possible, giving an economic and innovative boost worldwide.
It is thanks to the HPC that artificial intelligence has developed the predictive capacity based on the rapid learning of anomalous behaviors or hacking attacks. Various forms of attacks like phishing, spear-phishing, drive-by attack, password attack, denial of service, etc. they are responsible for serious security problems.
This challenge can be overcome thanks to artificial intelligence techniques and the rapid development of research. Artificial intelligence techniques can easily identify malware in the application and can take action to counter its activity. To process the large amount of information that users generate daily, these rapid calculation systems are therefore used, which lead to the identification of different types of attacks.
The turning point: machine learning and deep learning
But the turning point is machine learning (ML) which, with the use of software for detecting potential illegal activities, encodes what characterizes a threat by learning to recognize it. Deep learning is therefore an effective method for detecting cybersecurity problems. Deep learning techniques, in fact, can efficiently process a large amount of information present in cybersecurity datasets, resisting attacks.
Read also the interview with the team of Deep Instinct, the solution that uses deep learning as a tool to prevent, anticipate and counter potential new malicious threats.
The learning over a period of time allows deep learning systems to achieve ever more accurate performance, thanks to the creation of a neural network that simulates the behavior of the human brain in analytical learning. ML techniques can be used to analyze variants and attribute them to the right malware family, and spam and phishing detection includes a wide range of techniques designed to reduce the waste of time and the potential risk caused by unwanted emails.
The detection of spam and phishing is, in fact, increasingly difficult due to the advanced strategies used by attackers to bypass traditional filters, so ML approaches can improve the spam detection process and thwart its attack. Machine and deep learning techniques are increasingly used for multiple applications, to support security in the operator's activities and for the automation of specific tasks, improving network security and countering new attacks.