AI in Spam Checkers to BOT Detections

Global Spam has become a major problem for most companies that generate an immense amount of data every day. Social media platforms are facing issues due to spam or fake users. The data which we use is being exploited by spam users.

In fact, one in every 200 social media messages and one in every 21 tweets is estimated to be spam.

The trolls problem has been extended to online shopping platforms such as Amazon where the product reviews are slowly being manipulated by the merchants. And malicious BOTs are leveraging the data for fraudulent purposes. Hackers and fraudsters are using various advanced tools to make the attacks more effective and faster. This has become the biggest challenge for security teams to overcome smart attacks.

And AI is like:

Dude,

AI-powered Spam Checkers 

Machine Learning algorithms are designed in such a way that they allow the computers to automatically process the data and learn themselves. But for a Machine to recognize the spam emails and spam accounts, they need to be trained to identify the spammers and the patterns. Then the ML algorithms automatically would create a spam filter by creating a new rule. Thus, the human brain nevertheless does a smarter job than the machines. 

So, no matter what, computers and robots do, they need a human brain to function by themselves.

Without digressing any further, the AI-powered spam cops are designed using exceptional algorithms that detect and block spam content. The AI agent knows everything that a human requires. It works based on human preferences. Once you report some data or an email as spam, it ensures in reflecting the spam filter to each of your individual preferences. 

Bot Detections

We know that several automated bots are being used to book a hotel, answer customer service questions, etc. Through the advanced technologies in machine learning, natural language understanding, big data processing, reinforcement learning, and computer vision algorithms, there are several AI-powered bots coming up which are better at understanding human interactions as well as their behaviors. These bots can scale-up the attacks and improve the power of detecting the threats. Machine Learning automates and simplifies the process of identifying billions of threats and recognizes both human and non-human behavior. This is how machine learning makes a difference. Machine Learning has reduced the job of security teams making it highly-scalable and cost-effective.

Ultimately, Machine Learning has increased accuracy by keenly analyzing huge amounts of data using AI agents and supporting enterprises in getting rid of malicious attacks.