AI’s Fight Against Cyber Threats

Artificial Intelligence can drastically change the job market in the coming years, especially where cybersecurity is concerned. Even though AI relieves employees of tedious work, they need to spend time tuning up their tools. Employees need to train AI to spot an attack that is becoming harder and harder to avoid.

The reality is that there is not a simple answer to whether AI will replace human workers in the workforce. As more opportunities arise for the implementation of AI within businesses, it will become a asset for some sectors and a threat for others. There are many possible bleak effects of AI on society, but also many positive ones as well.

Data scientists and developers are constantly coming up with new applications for AI. But the purpose always remains the same: to improve our lives. We are already experiencing some of AI’s benefits through its widespread use in voice assistants and autonomous cars, but there’s much more to come. AI will affect the economy significantly due to its applications in cybersecurity. Malware and ransomware attacks have grown to epidemic proportions, causing significant financial losses. AI can combat these attacks through proper training. So it will be a long and hard battle between good and evil.

AI can help fight cybercrime by detecting activity patterns that signal something isn’t quite right. It’s comparable to how artificial intelligence is used in financial services to detect fraud. AI accomplishes this in systems that must cope with millions of events every second. During such times of turmoil and mayhem, fraudsters frequently try to strike. It’s common in DDOS (Distributed Denial of Service) attacks.

AI’s predictive skills make it incredibly useful in this situation, which is why, as we approach 2022, more businesses will be investing in this cutting-edge technology. Unfortunately, fraudsters are aware of AI’s advantages. New attacks are emerging that use technology like machine learning. These attacks can pass through cybersecurity defenses.

How machine learning works in fighting off malware?

Machine learning is commonly employed in the antivirus industry to increase detection capabilities. Machine learning techniques use sample data to create a mathematical model that predicts whether a file is safe or contaminated.

Machine learning relies on newer patterns rather than coding standards to detect dangerous patterns.

An algorithm gets applied to the observable data points of two manually constructed data sets. One contains only malicious files and the other only non-malicious files. Without being told what types of patterns or data points to look for, the algorithm generates rules that allow it to distinguish between safe and dangerous files. A data point is any piece of information about a file, such as a file’s internal structure, the compiler used, text resources compiled into the file, and so on.

Antivirus software that uses machine learning can detect new threats without relying on signatures. In the past, antivirus software primarily relied on fingerprinting, which compares files to a massive database of known malware.

Signature checkers can only detect known malware which can cause significant issues. It made anti-malware software extremely vulnerable since hundreds of thousands of new malware varieties get developed constantly.

On the other hand, training machine learning to recognize the indications of malicious and non-malicious files is possible. It allows it to discern dangerous patterns and detect malware — whether observed before.

Scientists have automated malware detection by feeding pre processed malware data to the computer. This technique provides the machine with an abstract view of malware. The Neural Network, Decision Tree, and Support Vector Machine use it to decide the correct response. This technique is advantageous because it doesn’t rely on manually designed features based on expert knowledge of the domain.

A neural network could also get trained using characteristics from sequences of malicious binaries that get deconstructed. The feed-forward network, on the other hand, takes as input a list of imported functions and accompanying DLL files, and metadata from the Portable Executable Header.

The feed forward and convolutional neural network designs, as well as their related characteristics, are combined into a single network in the final neural network-based classifier. After combining the information learned by both subnet works, this network delivers the final classification output. The network will be able to classify future cases using this merged technique.

Machine Learning is far from perfect

While machine learning can be a highly beneficial tool, though not without drawbacks. One of machine learning’s most significant flaws is that it doesn’t grasp the ramifications of the models it builds — it just performs them. It simply processes data and makes judgments using the most efficient, mathematically proven way.

As previously stated, the algorithm gets provided with millions of data points but no one tells it which data points are malware signs. That’s something the machine learning model will have to figure out itself. As a result, no human can ever truly know which data points could suggest a threat, according to the machine learning model. A single data point or a precise combination of 20 data points might get used.

A determined attacker might discover how the model utilizes these parameters to detect a threat and exploit the vulnerability. It is why nefarious actors can abuse AI. As they become aware of how it functions, they can find loopholes to avoid detection. A malicious actor could interject clean code found in the whitelisted files and interject it in files that would be otherwise malicious and dupe the AI.

Machine learning systems can only learn as much as the data fed to them. An effective model requires massive data inputs, each of which must be labeled accurately. These labels assist the model in comprehending specific aspects of the data (e.g. whether a file is clean, malicious, or potentially unwanted).

The model’s capacity to train successfully is contingent on the precise labeling of the dataset fed to it. It may be difficult and time-consuming to accomplish, however. A single mislabeled data point amid millions of precisely classified data points can lead to devastation even if it seems like a minuscule error. If the model utilizes the mislabeled input to make a decision, it might cause mistakes. These mistakes might carry on in future training projects. It causes a snowball effect that can have far-reaching consequences in the future.

An amalgamation of old and new is required.

AI cannot survive on its own. A hybrid approach of AI blended in with tried and tested methods like remote monitoring software can be beneficial. These programs have been around for close to 15 years. So there is plenty of data to point out towards their success.

Remote monitoring apps have a human element to them so they can help back AI when it gets dumbfounded by certain exceptions. For example, a cell phone monitoring software could be utilized to track employees with cell phones and monitor fishy emails via their email monitoring feature. Fake news or clickbait titles leading to malicious websites can be blocked via the social media tracking capabilities of these apps.

XNSPY, a remote cellphone monitoring app featured on many a list of employee tracking apps available in the market offers robust email monitoring capabilities. Employers can use these apps and install them on workers’ Android and iOS to secure their workflow and intellectual data. By browsing through this page, you can find insight into the list of the most well-known employee monitoring apps.

Since social media contains billions of users and fake stories get published every second, it will take ages to train AI effectively. But since employers can warn employees from opening fraudulent links, the whole network can be secured via remote monitoring apps.

This software is used for tracking employees with cell phones and can also monitor end-to-end encrypted messaging tools like Telegram or WhatsApp, and since we are seeing a trend of fake stories spreading in group chats like wildfire, these apps can prevent reputational and financial losses. AI can prove too slow to secure these channels due to their volatile and unpredictable nature.Employees selling intellectual property to the highest bidder or a whistleblower may connect with outsiders using WhatsApp or iMessage. End-to-end encryption is used in several messaging apps, providing malicious actors a false feeling of security. XNSPY’s instant messaging monitoring tool can intercept end-to-end encryption in such scenarios too. It, therefore, protects years of research and hard work from falling into the wrong hands.