Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the problem of intrusion detection in a computer network, and investigate the use of extreme learning machines (ELMs) to classify and detect the intrusions. With increasing connectivity beten networks, the risk of information systems to external attacks or intrusions has increased tremendously. Machine learning methods like support vector machines (SVMs) and neural networks have been widely used for intrusion detection. These methods generally suffer from long training times, require parameter tuning, or do not perform ll in multi-class classification.  propose a basic ELM method based on random features, and a kernel based ELM method for classification.  compare our methods with commonly used SVM techniques in both binary and multi-class classifications. Simulation results show that the proposed basic ELM approach outperforms SVM in training and testing speed, while the proposed kernel based ELM achieves higher detection accuracy than SVM in multi-class classification case.
['Chi Cheng', 'Wee Peng Tay', 'Guang-Bin Huang']
I am writing a research paper on a idea Extreme learning machines for intrusion detection
[{"from": "human", "value": "I am writing a research paper on a idea Extreme learning machines for intrusion detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the problem of intrusion detection in a computer network, and investigate the use of extreme learning machines (ELMs) to classify and detect the intrusions. With increasing connectivity beten networks, the risk of information systems to external attacks or intrusions has increased tremendously. Machine learning methods like support vector machines (SVMs) and neural networks have been widely used for intrusion detection. These methods generally suffer from long training times, require parameter tuning, or do not perform ll in multi-class classification.  propose a basic ELM method based on random features, and a kernel based ELM method for classification.  compare our methods with commonly used SVM techniques in both binary and multi-class classifications. Simulation results show that the proposed basic ELM approach outperforms SVM in training and testing speed, while the proposed kernel based ELM achieves higher detection accuracy than SVM in multi-class classification case."}]