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| 1 | Feature Selection with Deep Reinforcement Learning for Intrusion Detection System显示文摘An intrusion detection system(IDS)becomes an important tool for ensuring security in the network.In recent times,machine learning(ML)and deep learning(DL)models can be applied for the identification of intrusions over the network effectively.To resolve the security issues,this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique,called BBOFS-DRL for intrusion detection.The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network.To attain this,the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm(BOA)to elect feature subsets.Besides,DRL model is employed for the proper identification and classification of intrusions that exist in the network.Furthermore,beetle antenna search(BAS)technique is applied to tune the DRL parameters for enhanced intrusion detection efficiency.For ensuring the superior intrusion detection outcomes of the BBOFS-DRL model,a wide-ranging experimental analysis is performed against benchmark dataset.The simulation results reported the supremacy of the BBOFS-DRL model over its recent state of art approaches. | S.Priya K.Pradeep Mohan Kumar | 2023 | Computer Systems Science & Engineering2023，46，9： | 1 |
| 2 | Real-Time Multi Fractal Trust Evaluation Model for Efficient Intrusion Detection in Cloud显示文摘Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion attacks.To address such threats towards cloud services,numerous techniques exist that mitigate the service threats according to different metrics.The rule-based approaches are unsuitable for new threats,whereas trust-based systems estimate trust value based on behavior,flow,and other features.However,the methods suffer from mitigating intrusion attacks at a higher rate.This article presents a novel Multi Fractal Trust Evaluation Model(MFTEM)to overcome these deficiencies.The method involves analyzing service growth,network growth,and quality of service growth.The process estimates the user’s trust in various ways and the support of the user in achieving higher service performance by calculating Trusted Service Support(TSS).Also,the user’s trust in supporting network stream by computing Trusted Network Support(TNS).Similarly,the user’s trust in achieving higher throughput is analyzed by computing Trusted QoS Support(TQS).Using all these measures,the method adds the Trust User Score(TUS)value to decide on the clearance of user requests.The proposed MFTEM model improves intrusion detection accuracy with higher performance. | S.Priya R.S.Ponmagal | 2023 | Intelligent Automation & Soft Computing2023，37，8： | 0 |
| 3 | Pre Screening of Cervical Cancer Through Gradient Boosting Ensemble Learning Method显示文摘In recent years,cervical cancer is one of the most common diseases which occur in any woman regardless of any age.This is the deadliest disease since there were no symptoms shown till it is diagnosed to be the last stage.For women at a certain age,it is better to have a proper screening for cervical can-cer.In most underdeveloped nations,it is very difficult to have frequent scanning for cervical cancer.Data Mining and machine learning methodologies help widely infinding the important causes for cervical cancer.The proposed work describes a multi-class classification approach is implemented for the dataset using Support Vector Machine(SVM)and the perception learning method.It is known that most classification algorithms are designed for solving binary classification problems.From a heuristic approach,the problem is addressed as a multiclass classification problem.A Gradient Boosting Machine(GBM)is also used in implementation in order to increase the classifier accuracy.The proposed model is evaluated in terms of accuracy,sensitivity and found that this model works well in identifying the risk factors of cervical cancer. | S.Priya N.K.Karthikeyan D.Palanikkumar | 2023 | Intelligent Automation & Soft Computing2023，，3： | 0 |
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