The size and scale of cyber-attacks have increased in recent years as a result of a number of factors, the key one being the remote or hybrid work culture imposed by the COVID-19 pandemic. This has made cybersecurity a critical part of all organizations, irrespective of their size and type.
Machine Learning plays a crucial role here as it can collect, analyze, and prepare data without human interaction. In cybersecurity, this innovation makes a big difference as it can analyze past cyber-attacks and create individual defense reactions.
Generally, when we talk about Machine Learning, we talk about the positive side. However, cybercriminals are also leveraging Machine Learning to launch sophisticated cyber-attacks. In this article, we’ve covered the risks as well as the benefits of Machine Learning in cybersecurity.
Machine Learning Enabled Cyber-attacks
In the present scenario, the number of businesses targeted by cyber-attacks is expected to grow as hackers use Machine Learning to carry out mass attacks with a higher rate of success. Threat actors can automate the cyber-attacks using Machine Learning, including:
- Vulnerability Discovery
- Social Engineering
- Bypass Captchas
- Advanced Phishing
- Password Brute Force
- Spoofing and Impersonation
The future is man AND machine
The future of cybersecurity isn’t about ‘man OR machine’, but it’s about ‘man AND machine’. Machine Learning has many applications in cybersecurity including identifying cyber threats, improving available antivirus software, fighting cyber-crime that also uses AI capabilities, and so on. Although the utilization of Machine Learning techniques in cybersecurity has started only a few years ago, it is growing at a rapid pace. Let’s look at the growing role of Machine Learning in cybersecurity.
Machine Learning, a boon to cybersecurity
Machine Learning pre-emptively stamps out cyber threats and bolsters security infrastructure in the following ways,
- Big Data Analysis: With the help of Machine Learning, organizations can analyze big data sets of security events and malicious activities. Machine Learning works so that when similar events are detected, they are automatically dealt with by the trained ML model.
- Pattern Detection: This ability enables Machine Learning to detect malicious activity faster and stop attacks before they get started.
- User Behavior Modelling: A machine learning algorithm can be trained to identify the behavior of each user such as their login and logout patterns. If the user behaves out of his/her normal behavioral method, the machine learning algorithm can detect it and alert the concerned teams before it can compromise your systems and operations.
- Automation: One of the key benefits of Machine Learning is that a wide range of specific tasks could be fully or partially automated, including some forms of vulnerability discovery, deception, and attack disruption.
Wrapping up
Machine Learning is a powerful tool, but it is not a silver bullet. As mentioned at the start of the article, while the Machine Learning space is evolving at a significant rate, there will always be bad actors developing their skills and technology to find and exploit weaknesses.
Also, one needs to keep in mind that machine learning algorithms should minimize their false positives i.e. actions that they identify as malicious or part of a cyberattack but that are not.
Overall, technology is only as good (or as bad) as the minds using it. Thus, it is imperative to combine the best technology and processes with industry experts, to be able to detect and respond to cyber threats accurately and rapidly.
The above article is authored by Neelesh Kripalani, Chief Technology Officer of Clover Infotech.
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