7 Sneaky Ways Hackers Are Using Machine Learning to Steal Your Data

Machine learning is famous for its ability to analyze large data sets and identify patterns. It is basically a subset of . Machine learning uses algorithms that leverages previous data-sets and statistical analysis to make assumptions and pass on judgments about behavior. The best part, software or computers powered by machine learning algorithms can perform functions that they have not been programmed to perform. artificial intelligence Despite the , this makes it an ideal choice for identifying cybersecurity threats and mitigating the risk. Microsoft did just that with its Windows Defender in 2018. Equipped with multiple layers of machine learning, their software successfully identified and blocked crypto miners before they even started digging. Cyber attackers were trying to install cryptocurrency miners on thousands of computers through Trojan malware, but they failed to achieve their goal thanks to machine learning. machine learning challenges Due to this, machine learning has been extensively used by cyber-security experts. In fact, it is by adding accuracy and contextual intelligence. Sadly, cyber-security professionals are not the only one benefiting from machine learning capabilities. Cyber attackers are also using this technology to develop sophisticated malware and cybersecurity attacks that can bypass and fool security systems. transforming endpoint security In this article, you will learn about seven ways in which hackers use machine learning to fulfill their malicious designs. 1. Social Engineering Attacks Humans are the weakest link in your cyber security chain and cyber-criminals are fully aware of that. The increasing trend in social engineering attacks is a testament of that. The main objective of these social engineering attacks was to deceive people into giving out their sensitive personal and financial information or persuade them to take a desired action. With machine learning, hackers can take it up a notch and collect sensitive data of businesses, employees and their partners. What’s even worse, they don’t need much time to do it as machine learning can replicate attacks based on social engineering. 2. Phishing and Spear Phishing Cyber attackers are training machine learning algorithms to create real world situations. For instance, hackers are using machine learning algorithms to decipher the pattern of automated emails sent by service providers. This enables them to create fake messages that look identical to real one which makes it almost impossible for the receiver to identify the difference and they end up sharing their user ID and password. The best way to combat this is to increase cybersecurity awareness amongst your employees. Invest in cybersecurity training programs and test their knowledge by launching mock attacks. This will give you a clear picture about how good your employees are against these phishing and spear phishing attacks. Well trained, cybersecurity aware employees can become an asset as they can not only save themselves from such attacks but can also identify and report these attacks before it is too late. 3. Spoofing Spoofing creates fake personas of companies, big brands or famous personalities as well as employees in top positions. By harnessing the power of machine learning algorithms, cyber attackers first analyze the target from different perspectives and try to act like a CEO of a company. Next, they start sending malicious emails. This does not end there. In fact, cybercriminals also use machine learning algorithms to understand how the owner of the company writes, publishes social media posts, and sends emails. Once done, they can generate fake texts, videos and voices from it to trick employees into taking their required action. We have already seen what consequences this could have in some voice fraud incidents already. 4. Ransomware and other Malware Most cybersecurity attacks use malware even though the malware type might vary. It could be ransomware, spyware or trojan horse. By using machine algorithms, cybercrooks are trying to increase the complexity of these malware and making it more sophisticated so it can not be easily detected and eliminated. We are already seeing malware that can change their behavior so they can not be identified by protection systems. The key is to keep your anti malware protection up to date and take backup of your data. 5. Discovering Loopholes Hackers are always one step ahead of cybersecurity experts in this cybersecurity race. Did you know why? They are constantly looking for vulnerabilities that they can exploit. Once they find a loophole, they capitalize on it and launch an attack. On the flipside, cybersecurity experts take longer to patch those vulnerabilities. Machine learning can widen this gap and dramatically accelerate the process, as it can help hackers identify these loopholes far quickly. This means that they will not only be able to uncover more gaps in less time, but they can target them too. To give you an idea, an error or bug which can serve as a vulnerability was previously identified by hackers in days will now be uncovered in minutes, thanks to machine learning. 6. Password and Captcha Violations Most people still use passwords and businesses are still using them to authorize and authenticate users. Even if you are following password best practices and a secure process, passwords are not the safest option. Hackers uses brute force attacks to guess your passwords and machine learning can help their cause. It speeds up the process and allows them to discover your passwords far more quickly. Moreover, cybercriminals are also training bots to get over protection barriers such as captcha code. app development 7. DDoS Attacks With machine learning at their disposal, cyber attackers can automate different elements and phases of cybersecurity attacks. Let’s say, a cybercriminal is planning to launch a phishing attack. For this purpose, he creates a phishing email. He wants to send this email to different groups at different times. Machine learning algorithms can come to his rescue. After the advent of machine learning, we are seeing hackers using machine learning algorithms to launch and control dangerous DDoS attacks which uses botnets and zombie machines. What security measures do you take to protect your critical business assets from AI based cyber-security attacks?