Machine learning (ML) is taking cybersecurity by storm nowadays as well as other tech fields. In the past year, there has been ample information on the use of machine learning in both defense and attacks. While the defense was covered in most articles (I recommend reading "The Truth about Machine Learning in Cybersecurity"), Machine Learning for Cybercriminals seems to be overshadowed and not unanimous.
Ben Gurion, the main international airport in Israel, is one of the most protected airports in the world. It is known for its multilayered security. On the way from the office to the airport, you get caught in the lens of airport cameras. The road curves several kilometers to the terminal, and when you are driving, the security system has enough time to analyze your identity. In case of any signs of danger, you will be intercepted. The system of behavior anomalies analysis in computer systems works the same way. The implementation of these systems is effective in defense. While a perpetrator is running certain commands, an AI-based system can stave off any damage, having identified an intrusion.
From big players to small and midsize businesses, every organization has faced the impact of cyber threats at some point. But, the new generation of automated cyber attacks will affect multiple businesses to an unimaginable extent. With the onset of the digital age, going online became a necessity for every business. Most business processes, data storage, and data exchange are now handled digitally. Data has become such a significant asset that companies have started monetizing their data.
Defined as the "ability for (computers) to learn without being explicitly programmed," machine learning is huge news for the information security industry. It's a technology that potentially can help security analysts with everything from malware and log analysis to possibly identifying and closing vulnerabilities earlier. Perhaps too, it could improve endpoint security, automate repetitive tasks, and even reduce the likelihood of attacks resulting in data exfiltration.