While 2017 has been the year of the most substantial number of cyber crimes, 2018 will have to face the repercussion of the shortcuts taken in the past. Hence, IT departments are currently being assigned the responsibility to protect sensitive data, support new types of endpoints, and manage the exponentially increasing complex networks.
Machine Learning for Gathering Information:
As hackers continue to develop noise generation attacks in an attempt to weaken the automated defense systems and solutions, the use of AI has become more widespread among the hacker community. The first step toward this objective is to gather information and identify unauthorized access by knowing common security exploits. The chances of success depend on the scale of the collected data. That is why hackers collect vast amounts of data to improve social engineering techniques.
Conducting Personalized Attacks:
Hackers are increasingly making use of ML and AI capabilities to conduct personalized attacks by tailoring malware unique to each victim. It is clear that the machine learning-enabled development is going to turn into an arms race since cybercriminals, just as the defenders, are leveraging ML techniques to lower the defense of organizations.
Developers Are Developing Malware:
As developers continue to develop sophisticated malware that aims to escape attack detection, the best AI tools currently available on the market are failing due to the lack of maturity or precision of ML-based software and solutions. Although most companies invest in expensive malware detection software, they are not able to select the right tool, which is essential to protecting endpoints and keeping the network safe from attackers.
A report from HP also suggests that hackers are primarily the technicians who seek to improvise their craft with ML and incorporate new tools and malware.
Outlook
Supporting the thought that advanced ML and AI techniques might usher in new eras of hacking and cyber attacks, several warfare experts believe that machine vs. machine could be the new normal to crack into software vulnerabilities of an organization. Several companies have invested millions of dollars for preventing ransomware attacks and using ML technologies to detect malware.
Although true, leveraging ML technology is an expensive proposition and not viable for most organizations today. While AI technologies are being used to prevent phishing and malware attacks, hackers are continually upgrading their arsenal with ML for data theft and gaining unauthorized access into system security.
Malware is said to be a more significant problem than DDoS attacks and unfortunately more powerful than the technology that is deployed by companies to prevent these attacks. This is why ML proves inefficient in automating the process of blocking malware. Another shortcoming is that given the lengthy process of tracking these attacks, companies often fail to adopt a proactive approach.
