The problem of network intrusion detection poses innumerable challenges to the research community, industry, and commercial sectors. Moreover, the persistent attacks occurring on the cyber-threat landscape compel researchers to devise robust approaches in order to address the recurring problem. Given the presence of massive network traffic, conventional machine learning algorithms when applied in the field of network intrusion detection are quite ineffective. Instead, a hybrid multimodel solution when sought improves performance thereby producing reliable predictions. Therefore, this article presents an ensemble model using metaclassification approach enabled by stacked generalization. Two contemporary as well as heterogeneous datasets, namely, UNSW NB-15, a packet-based dataset, and UGR’16, a flow-based dataset, that were captured in emulated as well as real network traffic environment, respectively, were used for experimentation. Empirical results indicate that the proposed stacking ensemble is capable of generating superior predictions with respect to a real-time dataset (97% accuracy) than an emulated one (94% accuracy).
Drug-Target interaction (DTI) plays a crucial role in drug discovery, drug repositioning and understanding the drug side effects which helps to identify new therapeutic profiles for various diseases. However, the exponential growth in the genomic and drugs data makes it difficult to identify the new associations between drugs and targets. Therefore, we use computational methods as it helps in accelerating the DTI identification process. Usually, available data driven sources consisting of known DTI is used to train the classifier to predict the new DTIs. Such datasets often face the problem of class imbalance. Therefore, in this study we address two challenges faced by such datasets, i. e., class imbalance and high dimensionality to develop a predictive model for DTI prediction. The study is carried out on four protein classes namely Enzyme, Ion Channel, G Protein-Coupled Receptor (GPCR) and Nuclear Receptor. We encoded the target protein sequence using the dipeptide composition and drug with a molecular descriptor. A machine learning approach is employed to predict the DTI using wrapper feature selection and synthetic minority oversampling technique (SMOTE). The ensemble approach achieved at the best an accuracy of 95.9 %, 93.4 %, 90.8 % and 90.6 % and 96.3 %, 92.8 %, 90.1 %, and 90.2 % of precision on Enzyme, Ion Channel, GPCR and Nuclear Receptor datasets, respectively, when evaluated excluding SMOTE samples with 10-fold cross validation. Furthermore, our method could predict new drug-target interactions not contained in training dataset. Selected features using wrapper feature selection may be important to understand the DTI for the protein categories under this study. Based on our evaluation, the proposed method can be used for understanding and identifying new drug-target interactions. We provide the readers with a standalone package available at https://github.com/shwetagithub1/ predDTI which will be able to provide the DTI predictions to user for new query DTI pairs.
Network Intrusion Detection is one of the most researched topics in the field of computer security. Hacktivists use sophisticated tools to launch numerous attacks that hamper the confidentiality, integrity and availability of computer resources. There is an incessant need to safeguard these resources to avoid further damage. In the proposed study, we have presented a meta-classification approach using decision jungle to perform both binary and multiclass classification. We have established the robustness of our approach by configuring an optimal set of hyper-parameters coupled with relevant feature subsets using a production-ready environment namely Azure machine learning. We have validated the efficiency of the proposed design using three contemporary datasets namely UNSW NB-15, CICIDS 2017, and CICDDOS 2019. We could achieve an accuracy of 99.8% pertaining to UNSW NB-15 whereas the accuracy in the case of CICIDS 2017 and CICDDOS 2019 datasets has been 98% and 97% respectively. A distinctive ability of the proposed model lies in its finesse to detect thirty-three modern attack types considerably well. Unlike conventional stacking ensembles, the proposed solution relies on a train-test ratio of 40:60 to establish the legitimacy of predictions. We also conducted statistical significance tests to compare the performance of classifiers involved in the study. To extend the functionalities further, we have automated the proposed model that can be a reliable candidate for real-time network intrusion detection.
Network data is expanding and that too at an alarming rate. Besides, the sophisticated attack tools used by hackers lead to capricious cyber threat landscape. Traditional models proposed in the field of network intrusion detection using machine learning algorithms emphasize more on improving attack detection rate and reducing false alarms but time efficiency is often overlooked. Therefore, in order to address this limitation, a modern solution has been presented using Machine Learning-as-a-Service platform. The proposed work analyses the performance of eight two-class and three multiclass algorithms using UNSW NB-15, a modern intrusion detection dataset. 82,332 testing samples were considered to evaluate the performance of algorithms. The proposed two class decision forest model exhibited 99.2% accuracy and took 6 seconds to learn 1,75,341 network instances. Multiclass classification task was also undertaken wherein attack types like generic, exploits, shellcode and worms were classified with a recall percentage of 99%, 94.49%, 91.79% and 90.9% respectively by the multiclass decision forest model that also leapfrogged others in terms of training and execution time.
Objective: Automated Pap smear cervical screening is one of the most effective imaging based cancer detection tools used for categorizing cervical cell images as normal and abnormal. Traditional classification methods depend on hand-engineered features and show limitations in large, diverse datasets. Effective feature extraction requires an efficient image preprocessing and segmentation, which remains prominent challenge in the field of Pathology. In this paper, a deep learning concept is used for cell image classification in large datasets. Methods: This relatively proposed novel method, combines abstract and complicated representations of data acquired in a hierarchical architecture. Convolution Neural Network (CNN) learns meaningful kernels that simulate the extraction of visual features such as edges, size, shape and colors in image classification. A deep prediction model is built using such a CNN network to classify the various grades of cancer: normal, mild, moderate, severe and carcinoma. It is an effective computational model which uses multiple processing layers to learn complex features. A large dataset is prepared for this study by systematically augmenting the images in Herlev dataset. Result: Among the three sets considered for the study, the first set of single cell enhanced original images achieved an accuracy of 94.1% for 5 class, 96.2% for 4 class, 94.8% for 3 class and 95.7% for 2 class problems. The second set includes contour extracted images showed an accuracy of 92.14%, 92.9%, 94.7% and 89.9% for 5, 4, 3 and 2 class problems. The third set of binary images showed 85.07% for 5 class, 84% for 4 class, 92.07% for 3 class and highest accuracy of 99.97% for 2 class problems. Conclusion: The experimental results of the proposed model showed an effective classification of different grades of cancer in cervical cell images, exhibiting the extensive potential of deep learning in Pap smear cell image classification.
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