Optimizing intrusion detection systems using parallel metric learning

Network and data security in corporate organizations face immense threats due to increasing online vulnerabilities. Intrusion Detection Systems (IDSs) play a vital role in combating these threats, yet their effectiveness is often compromised by a high incidence of false alarms. To mitigate this issue, we propose a novel intrusion detection method, based on Parallel Long-Short Term Memory (p-LSTM). This approach harnesses the power of parallelism to process network data more efficiently and accurately. Experimental outcomes demonstrate that our p-LSTM method significantly decreases false alarm rates, enhancing the reliability of intrusion detection. Moreover, it outperforms traditional LSTM and other cutting-edge algorithms in terms of specificity, recall, and F-score, thus underscoring its superiority. The successful implementation of our p-LSTM model signals a leap forward in the evolution of IDSs, promising better protection for corporate networks against external threats.