In the current trend, the network-based system has substantial jobs, and they have become the targets of attackers. When an intrusion occurs, the security of a computer system is compromised. As a result, we must seek out the best methods for ensuring frameworks. A crucial component of the security management architecture is the intrusion detection system (IDS). To maintain effective network security, the design and implementation of IDS remain an important assessment topic. For intrusion detection, the previous system created an enhanced relevance vector machine (ERVM) classifier. However, intrusion detection is not robust for large-scale intrusion datasets, resulting in a high attack rate. The suggested work developed an improved deep bagging based convolutional neural network (DBCNN) for intrusion detection to address this issue. Preprocessing, feature selection, and classification are three processes included in the proposed framework. The KDD dataset is preprocessed in this stage using the kalman filter method. The feature selection is then carried out using the inertia weight based dragonfly method (IWDA). Finally, the DBCNN classifier successfully identifies interruption assaults. The KDD dataset is used to test the new model. The test results show that the proposed work accomplishes better execution contrasted and the current framework as far as accuracy, precision, recall and f-measure.
With the rapid advancement of networking technologies, security system has become increasingly important to academics from several sectors. Intrusion detection (ID) provides a valuable protection by reducing the human resources required to keep an eye on intruders, improving the efficiency of detecting the various attacks in networks. Machine learning and deep learning are two key areas that have recently received a lot of attention, with a focus on improving the precision of detection classifiers. Using defense anvance research project agency (DARPA”98) datasets, a number of academics and research have developed intrusion detection systems. This paper discusses various approaches developed by different researchers, including scale-hybrid-IDS-AlertNet (SHIA), forward feature selection algorithm (FFSA), modified- mutual information feature selection (MMIFS), deep neural network (DNN), and the holes that remain to be filled, highlighting areas where these procedures can be improved, also are addressed and the proposed approach improved deep convolutional neural network (IDCNN) is compared with existing approach.
At present data transmission widely uses wireless network framework for transmitting large volume of data. It generates numerous security problems and privacy issues which laid a way for developing IDS. IDS act as preventive technique in securing computer networks. Previously there are numerous metaheuristic and deep learning algorithms used in IDS for detecting threats. Some are affected by dynamic growth of feature spaces and others are degraded in performance during detection of threats. One fine-grained model for intrusion detection can be developed by selecting accurate features and testing them with the intelligent algorithms. Based on these explorations, in this research IDS is implemented with intelligence from preprocessing to feature classification. At first stage, data preprocessing is done using binning concept to reduce noise. Secondly feature selection is done dynamically using dynamic tree growth algorithm with fire fly optimization techniques. Finally, these features are processed using DTB-FFNN for detecting anomalies perfectly. This DTB-FFNN is evaluated with popular KDD dataset. Our proposed model cable news network (CNN)-classification is compared with existing intelligent techniques: feed forward deep neural network, support vectors machines, decision tree, and CNN-clustering is compared with k-means, density-based spatial clustering of applications with noise (DBSCAN). The experimental outcome proves that dynamic tree based FFNN and CNN-clustering produce higher accuracy than the existing models.
Brain computer interface is an action of translating the brain signal into a command for activating artificial object such as limb. BCI is the collaboration of biomedical, electrical, computer, and mechanical engineering. An action potential is created in the form of electrical signal in the brain for every action of a human being, either physical or mental. The patient himself suffering from epileptic seizure poses danger severely during the absence of continuous monitoring. Taking care of epileptic patients from remote locations has become essential since the patient loses his whole control during epileptic seizure. This paper presented an epileptic tele alert system (ETAS) for a patient being monitored from out of the hospital premises. The brain signals tapped using a noninvasive electro encephalographic (EEG) electrode was given to independent component analysis (ICA) to preprocess the tapped signal. The auto regressive method (AR) was employed to extract the feature from training the brain signal for the normal and abnormal condition of the patient. The support vector machine technique named Gaussian basis function non-linear support vector machine (GBF-NLSVM) was used to classify the signal that is a vulnerable point in the cause of the epileptic seizure with respect to brain action potential for various statuses of activities. The frequency beyond the beta level was identified and the signal was transformed as a command for activating handheld devices using microcontroller via global system for mobile communication (GSM). The MATLAB, Simulink software having built in functions for studying the brain signal was used to analyze the brain signal. The proposed model discussed the signal tapping, feature extraction, classification, and activation of mobile phone using microcontroller. The proposed system incorporating ICA, AR, and GBF- NLSVM was compared with other techniques for identifying epileptic seizure and ensured that the system provided about 97 % of accuracy over the other standalone technique.
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