Akansha Yadav explains how sequence models like RNNs, LSTMs, and Transformers detect and predict cyberattacks by analyzing network traffic and system logs. The workflow involves collecting data from login logs and file access, labeling normal versus attack activity, then training a model to spot temporal patterns of hacker behavior in real time.
So hello friends, in this particular video we are going to discuss about the intrusion prediction and detection with the deep sequence modeling. Yes, this is very important because these days in 2025 we have so much, uh, of data online and we have to protect it at the same time. So intrusion prediction and detection is something where we try to utilize the deep learning sequence models like we have RNN, we have LSTMs, GRUs, and even transformer models to predict those bad activity that can harm our data and even can harm the security of our bank balance or something. So it is all about related to cyber security. So let's understand this in depth. So let's first understand the first term, which is intrusion prediction and intrusion detection. So let's talk about what is intrusion detection. Now when I say this word intrusion detection, it means this is finding some bad habits. Bad activities like hacking. If someone is trying to attempt hacking or something, this comes under the intrusion detection. The next term that comes up is the intrusion prediction. Now, what is intrusion prediction? Is just predicting if a bad activity that might happen. So it is all about future prediction, just like in stock markets we do the predictions. Same way, it is intrusion prediction. It means we are predicting what might happen in future before it happens. Now the third term that it consists is the deep sequencing model. Deep sequence model or sequencing model is basically, especially AI models I'm talking about, that helps, that can understand the data that happens in order. For example, if there is something that happens step by step, that is happening step by step over time, so these type of models are generally trained to perform intrusion detection and intrusion prediction. Now, why do we need sequence model only? You might be wondering, why do we need these models only? Why not other models? So basically, computer and network activity happens in sequence. This isn't— that doesn't happen at once. There is a sequence, there is a step-by-step method which is followed by hackers. Or even in actual also, if someone is trying to steal money from you or get money from your account, bank account or something, so they follow step-by-step method. So this is something which is sequential thing. That is why we try to utilize our sequential model for this purpose. So for example, if one failed login is there, let's say if someone has tried to log in on your account and it is saying fail log, it means maybe just a mistake. But if someone is trying to do this 10 times, it means 10 failed logins are there. It means there is something wrong, there is something which needs to be detected. So probably a hacking attempt has been, you know, taken. So we try to utilize our sequential model because sequential model remembers what happened. Sequential model remembers what happened earlier and will try to get on to the patterns that, that hacker might be following. So you have to understand the intrusion detection and prediction in 2025 because the process is very, very important. So let's see how it works. So number one step that was followed is we collect the data. And once the data is collected, like from where we can collect the data? We can collect the data from network traffic, like we have internet packets and something, so we can use that internet packets to collect the data. The second is system login, like I need the details of login details and even the file access, that who all are the person who have the access to the file. So first is your task is to collect the data, it can be any type of a data, you can even use the public data as well to do certain training onto the sequence model. The next step, number 2, is to clean the data as we generally do in the machine learning model. So we remove the unless useless data, we pick up the useful detail. Just like, uh, for hacking and all, we need IP addresses. IP addresses plays a major role. We need timing, we need file type, we need file name, we need protocol type. So everything needs— we need everything related to this. So we are just going to pick up the useful things and we are going to ignore the useless data. We are going to arrange the data in the perfect model so that it gives us the better result. We are going to mark it as normal, and we are basically going to categorize them onto attack. So these two categories we can choose. Then we are going to choose the model which will be best for our situation or the scenario that we are handling. So for example, you can go with RNN, you can go with, uh, GRU, you can even go with LSTM, you can even go with the transformer-based model. So these are basic type of model that are generally used for the intrusion detection and prediction. Then you are going to train it, then you are going to test your model, and so on. So this is something which is called intrusion prediction and intrusion detection. Everything is related to cybersecurity needs a lot of good and labeled data. Sometimes it is hard to even explain why AI thinks it is attack, but there are multiple challenges. But though we have this concept which we have to consider because As we are growing in our AI industry, we need to take care of multiple things, multiple actions which are being taken. So attacks are rare, but we have to balance it. So sequence models are the best one. It will— it like, it basically must work very fast for the real-time defense as well. So that is why the sequence models are the best. So we'll meet in the next. Till then, happy learning and stay tuned.
