Multiclass Classification Scenario Research Articles

The Impact of Input Types on Smart Contract Vulnerability Detection Performance Based on Deep Learning: A Preliminary Study

Stemming vulnerabilities out of a smart contract prior to its deployment is essential to ensure the security of decentralized applications. As such, numerous tools and machine-learning-based methods have been proposed to help detect vulnerabilities in smart contracts. Furthermore, various ways of encoding the smart contracts for analysis have also been proposed. However, the impact of these input methods has not been systematically studied, which is the primary goal of this paper. In this preliminary study, we experimented with four common types of input, including Word2Vec, FastText, Bag-of-Words (BoW), and Term Frequency–Inverse Document Frequency (TF-IDF). To focus on the comparison of these input types, we used the same deep-learning model, i.e., convolutional neural networks, in all experiments. Using a public dataset, we compared the vulnerability detection performance of the four input types both in the binary classification scenarios and the multiclass classification scenario. Our findings show that TF-IDF is the best overall input type among the four. TF-IDF has excellent detection performance in all scenarios: (1) it has the best F1 score and accuracy in binary classifications for all vulnerability types except for the delegate vulnerability where TF-IDF comes in a close second, and (2) it comes in a very close second behind BoW (within 0.8%) in the multiclass classification.

Intrusion Detection using Deep Learning

The focus of cloud computing nowadays has been reshaping the digital epoch, in which clients now face serious concerns about the security and privacy of their data hosted in the cloud, as well as increasingly sophisticated and frequent cyber attacks. Therefore, it has become imperative for both individuals and organizations to implement a robust intrusion detection system (IDS) capable of monitoring packets in the network, distinguishing between benign and malicious behaviour, and detecting the type of attacks. IDS based on ML are efficient and precise in spotting network threats. Yet, for large dimensional data sizes, the performance of these systems decreases. Thus, it is critical to build a suitable feature selection approach that selects necessary features without having an impact on the classification process or causing information loss. Furthermore, training ML models on unbalanced datasets show a rising false positive rate (FPR) and a lowering detection rate (DR). In this paper, we present an improved cloud IDS designed by incorporating the synthetic minority over-sampling technique (SMOTE) to address the imbalanced data issue, and for feature selection, we propose to use a hybrid approach that includes three techniques: information gain (IG), chi-square (CS), and particle swarm optimization (PSO). Finally, the random forest (RF) model is utilized for detecting and classifying various types of attacks. The suggested system has been verified by the UNSW-NB15 and Kyoto datasets, achieving accuracies of over 98% and 99% in the multi-class classification scenario, respectively. It was noticed that an intrusion detection system with fewer informative features would operate more effectively. The simulation results significantly outperform other methodologies proposed in the related work in terms of different evaluation metrics.

Toward Improved Machine Learning-Based Intrusion Detection for Internet of Things Traffic

The rapid development of Internet of Things (IoT) networks has revealed multiple security issues. On the other hand, machine learning (ML) has proven its efficiency in building intrusion detection systems (IDSs) intended to reinforce the security of IoT networks. In fact, the successful design and implementation of such techniques require the use of effective methods in terms of data and model quality. This paper encloses an empirical impact analysis for the latter in the context of a multi-class classification scenario. A series of experiments were conducted using six ML models, along with four benchmarking datasets, including UNSW-NB15, BOT-IoT, ToN-IoT, and Edge-IIoT. The proposed framework investigates the marginal benefit of employing data pre-processing and model configurations considering IoT limitations. In fact, the empirical findings indicate that the accuracy of ML-based IDS detection rapidly increases when methods that use quality data and models are deployed. Specifically, data cleaning, transformation, normalization, and dimensionality reduction, along with model parameter tuning, exhibit significant potential to minimize computational complexity and yield better performance. In addition, MLP- and clustering-based algorithms outperformed the remaining models, and the obtained accuracy reached up to 99.97%. One should note that the performance of the challenger models was assessed using similar test sets, and this was compared to the results achieved using the relevant pieces of research.

Cochleogram-based adventitious sounds classification using convolutional neural networks

Background:The World Health Organization (WHO) establishes as a top priority the early detection of respiratory diseases. This detection could be performed by means of recognizing the presence of acoustic bio-markers (adventitious sounds) from auscultation because it is still the main technique applied in any health center to assess the status of the respiratory system due to its non-invasive, low-cost, easy to apply, fast to diagnose and safe nature. Method:Despite the novel deep learning approaches applied in this biomedical field, there is a notable lack of research that rigorously focuses on different time–frequency representations to determine the most suitable transformation to feed data into Convolutional Neural Network (CNN) architectures. In this paper, we propose the use of the cochleogram, based on modeling the frequency selectivity of the human cochlea, as an improved time–frequency representation to optimize the learning process of a CNN model in the classification of respiratory adventitious sounds. Our proposal is evaluated using the largest and most challenging public database of respiratory sounds. Results:The cochleogram obtains the best binary classification results among the compared methods with an average accuracy of 85.1% in wheezes and 73.8% in crackles, and a competitive performance evaluating a multiclass classification scenario in comparison with other well-known state-of-the-art deep learning models. Conclusion:The cochleogram provides a suitable time–frequency representation since it is able to model respiratory adventitious content more accurately by means of non-uniform spectral resolution and due to its increased robustness to noise and acoustic changes. This fact implies a significant improvement in the learning process of CNN models applied in the classification of respiratory adventitious sounds.

Pandemic Analysis and Prediction of COVID-19 Using Gaussian Doubling Times

COVID-19 has become a pandemic, with cases all over the world, with widespread disruption in some countries, such as Italy, US, India, South Korea, and Japan. Early and reliable detection of COVID-19 is mandatory to control the spread of infection. Moreover, prediction of COVID-19 spread in near future is also crucial to better plan for the disease control. For this purpose, we proposed a robust framework for the analysis, prediction, and detection of COVID-19. We make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the world. The estimates, analysis and predictions are based on the data gathered from Johns Hopkins Center during the time span of April 21 to June 27, 2020. We use the normal distribution for simple and quick predictions of the coronavirus pandemic model and estimate the parameters of Gaussian curves using the least square parameter curve fitting for several countries in different continents. The predictions rely on the possible outcomes of Gaussian time evolution with the central limit theorem of statistics the predictions to be well justified. The parameters of Gaussian distribution, i.e., maximum time and width, are determined through a statistical χ2-fit for the purpose of doubling times after April 21, 2020. For COVID-19 detection, we proposed a novel method based on the Histogram of Oriented Gradients (HOG) and CNN in multi-class classification scenario i.e., Normal, COVID-19, viral pneumonia etc. Experimental results show the effectiveness of our framework for reliable prediction and detection of COVID-19.

Sequential Classification With Empirically Observed Statistics

Motivated by real-world machine learning applications, we consider a statistical classification task in a sequential setting where test samples arrive sequentially. In addition, the generating distributions are unknown and only a set of empirically sampled sequences are available to a decision maker. The decision maker is tasked to classify a test sequence which is known to be generated according to either one of the distributions. In particular, for the binary case, the decision maker wishes to perform the classification task with minimum number of the test samples, so, at each step, she declares that either hypothesis 1 is true, hypothesis 2 is true, or she requests for an additional test sample. We propose a classifier and analyze the type-I and type-II error probabilities. We demonstrate the significant advantage of our sequential scheme compared to an existing non-sequential classifier proposed by Gutman. Finally, we extend our setup and results to the multi-class classification scenario and again demonstrate that the variable-length nature of the problem affords significant advantages as one can achieve the same set of exponents as Gutman’s fixed-length setting but without having the rejection option.

Bayesian Variable Selection for Multiclass Classification using Bootstrap Prior Technique

In this paper, the one-way ANOVA model and its application in Bayesian multi-class variable selection is considered. A full Bayesian bootstrap prior ANOVA test function is developed within the framework of parametric empirical Bayes. The test function developed was later used for variable screening in multiclass classification scenario. Performance comparison between the proposed method and existing classical ANOVA method was achieved using simulated and real life gene expression datasets. Analysis results revealed lower false positive rate and higher sensitivity for the proposed method.

Decoding natural reach-and-grasp actions from human EEG

Objective. Despite the high number of degrees of freedom of the human hand, most actions of daily life can be executed incorporating only palmar, pincer and lateral grasp. In this study we attempt to discriminate these three different executed reach-and-grasp actions utilizing their EEG neural correlates. Approach. In a cue-guided experiment, 15 healthy individuals were asked to perform these actions using daily life objects. We recorded 72 trials for each reach-and-grasp condition and from a no-movement condition. Main results. Using low-frequency time domain features from 0.3 to 3 Hz, we achieved binary classification accuracies of 72.4%, STD ± 5.8% between grasp types, for grasps versus no-movement condition peak performances of 93.5%, STD ± 4.6% could be reached. In an offline multiclass classification scenario which incorporated not only all reach-and-grasp actions but also the no-movement condition, the highest performance could be reached using a window of 1000 ms for feature extraction. Classification performance peaked at 65.9%, STD ± 8.1%. Underlying neural correlates of the reach-and-grasp actions, investigated over the primary motor cortex, showed significant differences starting from approximately 800 ms to 1200 ms after the movement onset which is also the same time frame where classification performance reached its maximum. Significance. We could show that it is possible to discriminate three executed reach-and-grasp actions prominent in people’s everyday use from non-invasive EEG. Underlying neural correlates showed significant differences between all tested conditions. These findings will eventually contribute to our attempt of controlling a neuroprosthesis in a natural and intuitive way, which could ultimately benefit motor impaired end users in their daily life actions.

A robust multi-class AdaBoost algorithm for mislabeled noisy data

AdaBoost has been theoretically and empirically proved to be a very successful ensemble learning algorithm, which iteratively generates a set of diverse weak learners and combines their outputs using the weighted majority voting rule as the final decision. However, in some cases, AdaBoost leads to overfitting especially for mislabeled noisy training examples, resulting in both its degraded generalization performance and non-robustness. Recently, a representative approach named noise-detection based AdaBoost (ND_AdaBoost) has been proposed to improve the robustness of AdaBoost in the two-class classification scenario, however, in the multi-class scenario, this approach can hardly achieve satisfactory performance due to the following three reasons. (1) If we decompose a multi-class classification problem using such strategies as one-versus-all or one-versus-one, the obtained two-class problems usually have imbalanced training sets, which negatively influences the performance of ND_AdaBoost. (2) If we directly apply ND_AdaBoost to the multi-class classification scenario, its two-class loss function is no longer applicable and its accuracy requirement for the (weak) base classifiers, i.e., greater than 0.5, is too strong to be almost satisfied. (3) ND_AdaBoost still has the tendency of overfitting as it increases the weights of correctly classified noisy examples, which could make it focus on learning these noisy examples in the subsequent iterations. To solve the dilemma, in this paper, we propose a robust multi-class AdaBoost algorithm (Rob_MulAda) whose key ingredients consist in a noise-detection based multi-class loss function and a new weight updating scheme. Experimental study indicates that our newly-proposed weight updating scheme is indeed more robust to mislabeled noises than that of ND_AdaBoost in both two-class and multi-class scenarios. In addition, through the comparison experiments, we also verify the effectiveness of Rob_MulAda and provide a suggestion in choosing the most appropriate noise-alleviating approach according to the concrete noise level in practical applications.

Knowledge Transfer with Interactive Learning of Semantic Relationships

We propose a novel learning framework for object categorization with interactive semantic feedback. In this framework, a discriminative categorization model improves through human-guided iterative semantic feedbacks. Specifically, the model identifies the most helpful relational semantic queries to discriminatively refine the model. The user feedback on whether the relationship is semantically valid or not is incorporated back into the model, in the form of regularization, and the process iterates. We validate the proposed model in a few-shot multi-class classification scenario, where we measure classification performance on a set of ‘target’ classes, with few training instances, by leveraging and transferring knowledge from ‘anchor’ classes, that contain larger set of labeled instances.

An efficient Gaussian kernel optimization based on centered kernel polarization criterion

The success of kernel-based learning methods is heavily dependent on the choice of a kernel function and proper setting of its parameters. In this paper, we optimize the Gaussian kernel for binary-class problems by using centered kernel polarization criterion. This criterion is an extension of kernel polarization and a simplified style of centered kernel alignment. Compared with formulated kernel polarization criterion, the proposed criterion has a defined geometrical significance, and it can locate the global optimal point with less influence of threshold selection. Furthermore, the approximate criterion function can be proved to have a determined global minimum point by adopting the Euler–Maclaurin formula under weaker conditions. In addition, taking the preservation of within-class local structure into account, we present an evaluation criterion named local multiclass centered kernel polarization in multiclass classification scenario. Comparative experiments are conducted on some benchmark examples with three Gaussian kernel based learning methods and the results well demonstrate the effectiveness and efficiency of the proposed quality measures.

Two-stage multiple kernel learning with multiclass kernel polarization

The success of kernel methods is very much dependent on the choice of kernels. Multiple kernel learning (MKL) aims at learning a combination of different kernels in order to better match the underlying problem instead of using a single fixed kernel. In this paper, we propose a simple but effective multiclass MKL method by a two-stage strategy, in which the first stage finds the kernel weights to combine the kernels, and the second stage trains a standard multiclass support vector machine (SVM). Specifically, we first present an evaluation criterion named multiclass kernel polarization (MKP) to assess the quality of a kernel in the multiclass classification scenario, and then develop a heuristic rule to directly assign a weight to each kernel based on the quality of the individual kernel. MKP is a multiclass extension of the kernel polarization, which is a universal kernel evaluation criterion for kernel design and learning. Comprehensive experiments are conducted on several UCI benchmark examples and the results well demonstrate the effectiveness and efficiency of our approach.

Multiclass Kernel Function Evaluation

Kernel is a key component of the support vector machines (SVMs) and other kernel methods. Based on the data distributions of classes in the feature space, this paper proposed a model selection criterion to evaluate the goodness of a kernel in multiclass classification scenario. This criterion is computationally efficient and is differentiable with respect to the kernel parameters. Compared with the k-fold cross validation technique which is often regarded as a benchmark, this criterion is found to yield about the same performance with much less computational overhead.