Technology transformation is moving towards the world of automation with its advancements in areas like Artificial Intelligence, Machine Learning, and even the android stack. Before these stacks, the core purpose of IT tech was only to provide better performance and human conveniences, but now they are being used for detecting and mitigating cybersecurity threats.
As the technological revolution continues, it will also bring some cybersecurity risks that are hard to detect, and their effect can severely influence businesses.
Seeing the critically of such a situation, DevOps developers are now deploying cybersecurity modules in their continuous software development lifecycle. But to achieve that, they need to research and reverse engineer the thought process of hackers and implement them into products. AI can be more helpful in creating such scenarios, guessing new possible attacks from datasets, and proposing sophisticated solutions to mitigate them entirely.
With the advancement in AI, malware has also become more powerful, which is nearly unable to detect and defended by any company. Therefore, DevOps developers must take it seriously and try all possible aspects to secure their systems from such attacks.
This article contains the most impacting issues with AI while developing Ransomware defence modules, methods of detecting Ransomware and much other information.
Why Choose AI and ML Models To Combat Ransomware?
The growing sophisticated nature of Ransomware requires an intelligent defence system that can predict each move. That’s where AI comes into the picture, which studies every mutation ofRansomwaree and makes developers aware of their nature to make their variants to do defence experiments on them. Plus, many hackers are also using AI to develop new variants, and by integrating ML algorithms, they can make it more effective.
With such emergencies, many organizations have encountered new cybersecurity challenges generated by AI. That’s why cybersecurity experts need to adopt AI-based solutions to find ransomware alterations that can help to mitigate serious cybersecurity threats for DevOps applications.
Variants of Targeted Ransomware:
A few years back, IBM created the most sophisticated variant of Ransomware called Deeplocker using AI and got lots of publicity. It was more hazardous than its source and targeted selected devices. Their intention was only to show the capability of AI to create possible cyberattacks on targeted machines. Plus, to find out at what level hackers can go to attack specific business entities.
That was about an experiment, but what if hackers try this type of Ransomware to destroy any entity? Yes, hackers do attempt many times. A few years back, a group of hackers launched a digital virus of an identical nature called WannaCry, which can act like a teleconferencing application.
When they released this virus on the digital landscape, it didn’t start to reflect but was impacting slowly on those applications. When it got complete access to such applications, it began to scan users’ faces to find a particular identity. Once it detected the specific person, it started the pre-programmed work blocking that person’s system.
Ideally, it is nearly impossible for any advanced malware to make them proactive and think for themselves. Hackers were somehow able to crack the logic behind it. They succeeded in creating new malware that could code for itself. For such, they need to implement a new set of proactive instructions for malware. AI makes it possible or on the way.
AI also set free hackers to write new instructions from the start as it contains more powerful library codes. So, they only need to release the malware. Now they have to wait for it to start executing and improving its programmed behaviours according to the environment. Organizations may be able to stop Ransomware at a certain level. Although its self-modified nature makes it settle for all possible scenarios and bypass cybersecurity layers. For creators to make such Ransomware is easy using AI, but hard to achieve such defence layers for defenders.
Use of AI To Combat Self-Modifiable Ransomware:
DevOps developers must train their AI/ML algorithms to detect and protect against possible modified versions of malware. For such, they need to implement predictive analytics in their cybersecurity applications. To safeguard systems against self-evolving viruses by making them able to address such with modifiable instructions.
Getting Information from Targeted Ransomware:
Many times, Ransomware was created to get control over targeted devices to access confidential information. Access could be gained in many ways, for example, through the screen, microphone, files in transitions, or any other way. You must be thinking about how microphone access can benefit hackers. AI can convert audio into text form. Through this text form they can get information about the company through conversation going in surroundings. AI-enable Ransomware helps attackers get all this information and develop custom software for their targets.
AI helps DevOps engineers to reverse engineer such self-modifiable machine learning algorithms. For that, they will require specific data from the victim of the ransomware attack to learn its mutation nature. After getting such information, they can utilize it to convertRansomwaree to malware for better understanding. Expert DevOps security engineers who have studied such attacks and their casualties can easily recognize and mitigate them entirely during the confrontation.
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