In today's digital age, almost everything we do relies heavily on the internet and interconnected networks. Think about how often you use smartphones, tablets, or computers to talk to friends, watch videos, or even just browse social media. With this constant online activity, keeping these networks safe from hackers or any kind of cyberattack has become super important.
Researchers are always on the lookout for better ways to protect our digital lives. In a new study by researchers from Biju Patnaik University of Technology, Odisha, Centurion University of Technology and Management, Odisha, Alexandria University, Egypt, and Manipal Academy of Higher Education, Manipal have used a type of artificial intelligence called deep learning, which helps computers understand patterns in data better than ever before, to detect intrusions. In particular, this research focuses on a technique called Long Short-Term Memory (LSTM) to make sure our networks stay safe.
Computers are increasingly being used to automatically detect when something strange or potentially malicious is happening on a network. Like a guard that's always active and catches any intruders just by checking the pattern in data, an Intrusion Detection System (IDS) in a computer does this. However, older systems often trigger false alarms, like when your cat jumps in front of a motion detector.
The new research improves IDS using LSTM, a special type of neural network. LSTMs are good at understanding sequences, like words in a sentence or, similarly, steps in a network's data traffic. They excel at remembering relevant information for a while and forget what's not needed. Now, LSTM by itself is smart, but researchers incorporated optimization techniques, like Particle Swarm Optimization (PSO), JAYA, and the Salp Swarm Algorithm (SSA). They help fine-tune the system’s settings so that it can spot anomalies or unusual patterns in network traffic more accurately.
Using LSTM combined with these optimization techniques, the system can recognize potential threats almost in real-time, helping stop cyberattacks before they cause harm. The improved model also means fewer false signals, saving time and resources. Since cyber threats constantly evolve, this system can learn from new data and adjust itself, making it future-proof to an extent.
For instance, this upgraded system was tested on well-known datasets that mimic real network traffic, such as NSL KDD, CICIDS, and BoT-IoT. These contain millions of data points about both normal and malicious activities from which the system can learn. By focusing on the best optimization technique, the Salp Swarm Algorithm (SSA), researchers found it provided the most accurate results across all datasets. This proves it to be a powerful tool in real-time network security applications.
While LSTM is powerful, other advanced deep learning models, like transformers, could offer even better results when adapted for network security. The researchers also want to explore using multiple variants of LSTM, along with evolutionary techniques that allow the system to learn and grow faster.
The development of more advanced and smarter Intrusion Detection Systems is crucial in our hyperconnected world. By employing techniques like LSTM along with high-tech optimizers, researchers are making great strides in protecting networks against ever-evolving cyber threats while minimizing false alarms.
This research news was partly generated using artificial intelligence and edited by an editor at Research Matters