Automatic Malware Detection

The problem of automatic malware detection presents challenges for antivirus vendors. Since the manual investigation is not possible due to the massive number of samples being submitted every day, automatic malware classication is necessary. Our work is focused on an automatic malware detection framework based on machine learning algorithms. We proposed several static malware detection systems for the Windows operating system to achieve the primary goal of distinguishing between malware and benign software. We also considered the more practical goal of detecting as much malware as possible while maintaining a suciently low false positive rate. We proposed several malware detection systems using various machine learning techniques, such as ensemble classier, recurrent neural network, and distance metric learning. We designed architectures of the proposed detection systems, which are automatic in the sense that extraction of features, preprocessing, training, and evaluating the detection model can be automated. However, antivirus program relies on more complex system that consists of many components where several of them depends on malware analysts and researchers. Malware authors adapt their malicious programs frequently in order to bypass antivirus programs that are regularly updated. Our proposed detection systems are not automatic in the sense that they are not able to automatically adapt to detect the newest malware. However, we can partly solve this problem by running our proposed systems again if the training set contains the newest malware. Our work relied on static analysis only. In this thesis, we discuss advantages and drawbacks in comparison to dynamic analysis. Static analysis still plays an important role, and it is used as one component of a complex detection system.The problem of automatic malware detection presents challenges for antivirus vendors. Since the manual investigation is not possible due to the massive number of samples being submitted every day, automatic malware classication is necessary. Our work is focused on an automatic malware detection framework based on machine learning algorithms. We proposed several static malware detection systems for the Windows operating system to achieve the primary goal of distinguishing between malware and benign software. We also considered the more practical goal of detecting as much malware as possible while maintaining a suciently low false positive rate. We proposed several malware detection systems using various machine learning techniques, such as ensemble classier, recurrent neural network, and distance metric learning. We designed architectures of the proposed detection systems, which are automatic in the sense that extraction of features, preprocessing, training, and evaluating the detection model can be automated. However, antivirus program relies on more complex system that consists of many components where several of them depends on malware analysts and researchers. Malware authors adapt their malicious programs frequently in order to bypass antivirus programs that are regularly updated. Our proposed detection systems are not automatic in the sense that they are not able to automatically adapt to detect the newest malware. However, we can partly solve this problem by running our proposed systems again if the training set contains the newest malware. Our work relied on static analysis only. In this thesis, we discuss advantages and drawbacks in comparison to dynamic analysis. Static analysis still plays an important role, and it is used as one component of a complex detection system

Radial Basis Function Neural Networks : A Review

Radial Basis Function neural networks (RBFNNs) represent an attractive alternative to other neural network models. One reason is that they form a unifying link between function approximation, regularization, noisy interpolation, classification and density estimation. It is also the case that training RBF neural networks is faster than training multi-layer perceptron networks. RBFNN learning is usually split into an unsupervised part, where center and widths of the Gaussian basis functions are set, and a linear supervised part for weight computation. This paper reviews various learning methods for determining centers, widths, and synaptic weights of RBFNN. In addition, we will point to some applications of RBFNN in various fields. In the end, we name software that can be used for implementing RBFNNs

Classification Techniques In Blood Donors Sector – A Survey

This paper focuses on the classification and the recent trends associated with it. It presents a survey of the classification system and clarifies how classification and data mining are related both to each other. Classification is arranging the blood donor dataset into the predefined group and helpful to predict group membership for data instances. This enables users to search target donors become easier because the blood stocks always required replacing expired stocks after a certain period and useful in emergency demands such as surgery and blood transfusion. This paper has also sought to identify the research area in classification to fulfill gaps where further work can be carried on

Knowledge management overview of feature selection problem in high-dimensional financial data: Cooperative co-evolution and Map Reduce perspectives

The term big data characterizes the massive amounts of data generation by the advanced technologies in different domains using 4Vs volume, velocity, variety, and veracity-to indicate the amount of data that can only be processed via computationally intensive analysis, the speed of their creation, the different types of data, and their accuracy. High-dimensional financial data, such as time-series and space-Time data, contain a large number of features (variables) while having a small number of samples, which are used to measure various real-Time business situations for financial organizations. Such datasets are normally noisy, and complex correlations may exist between their features, and many domains, including financial, lack the al analytic tools to mine the data for knowledge discovery because of the high-dimensionality. Feature selection is an optimization problem to find a minimal subset of relevant features that maximizes the classification accuracy and reduces the computations. Traditional statistical-based feature selection approaches are not adequate to deal with the curse of dimensionality associated with big data. Cooperative co-evolution, a meta-heuristic algorithm and a divide-And-conquer approach, decomposes high-dimensional problems into smaller sub-problems. Further, MapReduce, a programming model, offers a ready-To-use distributed, scalable, and fault-Tolerant infrastructure for parallelizing the developed algorithm. This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-The-Art cooperative co-evolution and MapReduce-based feature selection techniques, and future research directions

Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems

Designing multiple classifier combinations a survey

Classification accuracy can be improved through multiple classifier approach. It has been proven that multiple classifier combinations can successfully obtain better classification accuracy than using a single classifier. There are two main problems in designing a multiple classifier combination which are determining the classifier ensemble and combiner construction. This paper reviews approaches in constructing the classifier ensemble and combiner. For each approach, methods have been reviewed and their advantages and disadvantages have been highlighted. A random strategy and majority voting are the most commonly used to construct the ensemble and combiner, respectively. The results presented in this review are expected to be a road map in designing multiple classifier combinations