Time Efficiency on Computational Performance of PCA, FA and TSVD on Ransomware Detection

Ransomware is able to attack and take over access of the targeted user'scomputer. Then the hackers demand a ransom to restore the user's accessrights. Ransomware detection process especially in big data has problems interm of computational processing time or detection speed. Thus, it requires adimensionality reduction method for computational process efficiency. Thisresearch work investigates the efficiency of three dimensionality reductionmethods, i.e.: Principal Component Analysis (PCA), Factor Analysis (FA) andTruncated Singular Value Decomposition (TSVD). Experimental results onCICAndMal2017 dataset show that PCA is the fastest and most significantmethod in the computational process with average detection time of 34.33s.Furthermore, result of accuracy, precision and recall also show that the PCAis superior compared to FA and TSVD

Improving the Anomaly Detection by Combining PSO Search Methods and J48 Algorithm

The feature selection techniques are used to find the most important and relevant features in a dataset. Therefore, in this study feature selection technique was used to improve the performance of Anomaly Detection. Many feature selection techniques have been developed and implemented on the NSL-KDD dataset. However, with the rapid growth of traffic on a network where more applications, devices, and protocols participate, the traffic data is complex and heterogeneous contribute to security issues. This makes the NSL-KDD dataset no longer reliable for it. The detection model must also be able to recognize the type of novel attack on complex network datasets. So, a robust analysis technique for a more complex and larger dataset is required, to overcome the increase of security issues in a big data network. This study proposes particle swarm optimization (PSO) Search methods as a feature selection method. As contribute to feature analysis knowledge, In the experiment a combination of particle swarm optimization (PSO) Search methods with other search methods are examined. To overcome the limitation NSL-KDD dataset, in the experiments the CICIDS2017 dataset used. To validate the selected features from the proposed technique J48 classification algorithm used in this study. The detection performance of the combination PSO Search method with J48 examined and compare with other feature selection and previous study. The proposed technique successfully finds the important features of the dataset, which improve detection performance with 99.89% accuracy. Compared with the previous study the proposed technique has better accuracy, TPR, and FPR

Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)

There are several ways to increase detection accuracy result on the intrusion detection systems (IDS), one way is feature extraction. The existing original features are filtered and then converted into features with lower dimension. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent

Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction

Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%

Time efficiency on computational performance of PCA, FA and TSVD on ransomware detection

Ransomware is able to attack and take over access of the targeted user's computer. Then the hackers demand a ransom to restore the user's access rights. Ransomware detection process especially in big data has problems in term of computational processing time or detection speed. Thus, it requires a dimensionality reduction method for computational process efficiency. This research work investigates the efficiency of three dimensionality reduction methods, i.e.: Principal Component Analysis (PCA), Factor Analysis (FA) and Truncated Singular Value Decomposition (TSVD). Experimental results on CICAndMal2017 dataset show that PCA is the fastest and most significant method in the computational process with average detection time of 34.33s. Furthermore, result of accuracy, precision and recall also show that the PCA is superior compared to FA and TSVD

Enhanced deep learning intrusion detection in IoT heterogeneous network with feature extraction

Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%