Efficacy of Nonlinear Manifold Learning in Malware Image Pattern Analysis

The Android operating system has gained popularity and evolved rapidly since the previous decade. Traditional approaches such as static and dynamic malware identification techniques require a lot of human intervention and resources to design the malware classification model. The real challenge lies with the fact that inspecting all files of the application structure leads to high processing time, more storage, and manual effort. To solve these problems, optimization algorithms and deep learning has been recently tested for mitigating malware attacks. This manuscript proposes Summing of neurAl aRchitecture and VisualizatiOn Technology for Android Malware identification (SARVOTAM). The system converts the malware non-intuitive features into fingerprint images to extract the quality information. A fine-tuned Convolutional Neural Network (CNN) is used to automatically extract rich features from visualized malware thus eliminating the feature engineering and domain expert cost. The experiments were done using the DREBIN dataset. A total of fifteen different combinations of the Android malware image sections were used to identify and classify Android malware. The softmax layer of CNN was substituted with machine learning algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) to analyze the grayscale malware images. It is observed that CNN-SVM model outperformed original CNN as well as CNN-KNN, and CNN-RF. The classification results showed that our method is able to achieve an accuracy of 92.59% using Android certificates and manifest malware images. This paper reveals the lightweight solution and much precise option for malware identification.

Identification of malware is vital for safeguarding safety critical systems from cyber threats. Currently learning based approaches aid the malware detection pipeline. Current techniques to detect malware on Windows systems tend to overlook various types of behaviors, primarily concentrating on Application Programming Interface (API) calls without considering heterogeneous information, and also it requires large amounts of annotated data. To solve these problems, in this work, Hashed Nebula is proposed, a self-attention transformer-based neural network is considered along with a hashing method to encode API call names, categories, and arguments separately to improve features selection. Proposed Hashed-Nebula is evaluated on benchmark malware datasets such as Speakeasy and Malicious Code Dataset (MCD). Hashed-Nebula performs better than the existing Nebula model, with 4% increase in detection accuracy and 2% improvement in F1 score on Speakeasy dataset. In addition, it shows an 18% increase in detection accuracy and a 4% improvement in F1 score in the MCD dataset. Hashed-Nebula could serve as a reliable and efficient solution for malware detection.

EfficientNet convolutional neural networks-based Android malware detection

COMPARISON OF EQUIVALENT LINEAR AND NON LINEAR METHODS ON GROUND RESPONSE ANALYSIS: CASE STUDY AT WEST BANGKA SITE. Within the framework of identifying NPP sites, site surveys are performed in West Bangka (WB), Bangka-Belitung Island Province. Ground response analysis of a potential site has been carried out using peak strain profiles and peak ground acceleration. The objective of this research is to compare Equivalent Linear (EQL) and Non Linear (NL) methods of ground response analysis on the selected NPP site (West Bangka) using DeepSoil software. Equivalent linear method is widely used because requires soil data in simple way and short time of computational process. On the other hand, non linear method is capable of representing the actual soil behaviour by considering non linear soil parameter. The results showed that EQL method has similar trends to NL method. At surface layer, the acceleration values for EQL and NL methods are resulted as 0.425g and 0.375g respectively. NL method is more reliable in capturing higher frequencies of spectral acceleration compared to EQL method.

A comparison was made of the linear least-squares method and a nonlinear least-square method of the Langmuir isotherm and pseudo second order kinetic model for the biosorption of methylene blue onto rice husk. The parameters obtained from the linear method and the nonlinear method were different. The error analysis was performed. Results showed that both linear and nonlinear method can be used to fit the experimental data. The nonlinear method may be a better way to obtain the desired parameters.

The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, chaos theory and nonlinear dynamic methods are widely used in extracting the EEG signal features for computer-aided diagnosis (CAD) of depression. Hence, this article presents the recent efforts on CAD of depression using EEG signals with a focus on using nonlinear methods. Such a CAD system is simple to use and may be used by the clinicians as a tool to confirm their diagnosis. It should be of a particular value to enable the early detection of depression.

This paper attempted to introduce a deep learning-based model for the classification of malicious software (Malware). Malware is growing exponentially every year and malware writers try to evade the antivirus software by producing polymorphic and metamorphic malware. Most antiviruses are based on signature detection which is not sufficient against the new generation of malware. For a solution against malicious software, antivirus vendors started to use Machine Learning approaches which had a positive impact on malware detection and classification. Recently, Deep Learning algorithms and specifically Convolutional Neural Networks (CNN) caught more attraction for malware classification and it is the best deep learning algorithm for extracting features from images. By integrating the CNN with Gradient Boosting (XG-Boost) algorithm we can have a powerful model to classify malware images into their classes or families. The input source for the model is the Malimg dataset [1] which is an open collection of already converted malware to a grayscale image. There are many papers used CNN-SVM, CNN-Softmax and other models for malware image classification and they got good accuracies, but this paper proposed to used CNN-XGBoost model and achieve more accuracy than previously used algorithms for malware classification.

As the security challenges are inherent to 5G networks, the threat of malware continues to rise. Due to the high-speed nature of 5G, there is a need for automated malware detection and classification methods. This chapter presents a hybrid model for malware classification that combines machine learning and deep learning techniques. This model leverages the power of feature extraction using the InceptionResNetV2 deep learning model pre-trained on ImageNet. The features extracted from malware images are fed into a random forest classifier for the final classification task. The experimental evaluation conducted on a dataset comprising 31 malware families achieves an accuracy of 94.15%. Furthermore, the predicted labels closely align with the ground truth labels across various malware families, showcasing the model's ability to capture the intricate patterns and characteristics of diverse malware strains.

The attack that occurred recently involved the utilization of malicious software, commonly referred to as malware, along with advanced techniques such as machine learning, specifically deep learning, code transformation, and polymorphism. This makes it harder for cyber experts to detect malware using traditional analysis methods. In view of the low accuracy and high false positive rate of traditional malware detection methods, this research proposes a fine-tuned deep learning model with a novel dataset that is compared with ANN, Support Vector Machine (SVM), random forest (RF), K-Nearest Neighbourhood (KNN) classifiers. Converting a malware code into an image could allow users to effectively identify the presence of malware, even if the original code is modified by the creator. This is due to the fact that the attributes of images remain unchanged, allowing for reliable identification. So, researchers used deep learning technology to detect malware, like detecting malaria from red blood cells. The deep learning model found that a more detailed analysis of malware data sets, focusing on RGB and greyscale images, is needed. These data sets currently rely on publicly available data, but the accuracy of the traditional model could be a lot higher. It also produces many false positive results. The main goal is to create a new data set and model using malware images to identify and categorize malware using deep learning without relying on existing image detection and transfer learning models. The researcher adjusted different hyper parameters, like the number of neurons, filters, stride, hidden units, layers, learning rate, batch size, activation function, optimizer, and epochs. Identifying and correcting issues in models, improving their clarity, stability, and fairness, as well as debugging and monitoring them, is a challenging task. To eliminate this obstacle, assess the model's behaviour and performance by employing various methods such as logging, profiling, testing, visualization, and model clarity. To overcome the challenges posed by the electricity shutdown, we utilized Google's cloud-based GPU and Python 3.7 language to conduct the experiment and train the model. Kali Linux is an operating system that can automatically encrypt file systems and has a lower chance of crashing the system due to malware in a virtual sandbox. The researcher used techniques like early stopping and cross-validation to prevent over fitting and assess generalization in addition to monitoring and evaluating the model's behaviour and performance. The researcher used different methods like L1 and L2 regularization, dropout, and batch normalization to improve the model's performance and avoid over fitting. The binary portable executable (PE) malware dataset is collected from Kaggle, Malimg, Virusshare, Malvis, MS Big2015, and VX-underground and finally converted to greyscale and RGB images to create a novel dataset to fill the lack of image dataset. The raw dataset was then rescaled to a 128x128 greyscale and RGB (red, green, blue) image and flattened to 1024-byte vector images input into convolution neural network (CNN) interpolation to extract features for malware detection. The newly acquired dataset is utilized as an input for the innovative DL algorithms in order to create a tailor-made model capable of accurately predicting malware. The findings in this customized CNN model by fine-tuning hyper-parameters with the three-channel RGB dataset outperformed a greyscale dataset with an accuracy of 98.7% and error rate of 1.3% in current malware compared with other artificial neural network (ANN), ML algorithms such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and also Random Forest (RF) models. The proposed approach is compatible with all operating systems (Windows, Linux, and Mac) and can identify different types of malwares, such as packed, polymorphic, obfuscated, metamorphic, or variations of a malware family. There are some limitations to the shortage of malware image datasets and computational costs for fine-tuning hyper parameters in the whole model. Furthermore, the proposed approach detects malware and groups it into families without using common techniques like disassembly, decompilation, de-obfuscation, or running malicious code in a virtual environment.

Background/Introduction Prior studies revealed exposure to ambient air pollution would increase cardiovascular disease–related mortality and nonfatal events, but the underlying mechanism was not fully elucidated. Disruption of autonomic regulation might be a possible cause of increased cardiovascular morbidity and mortality, but previous work showed inconsistent relationship between ambient air pollution and heart rate variability analyzed by traditional linear methods. Our previous study showed the relationship between ambient air pollution and home blood pressure was non-linear, thus non-linear methods, such as detrended fluctuation analysis (DFA) and multi-scale entropy (MSE), could be more useful tools while assessing the effect of ambient air pollution on biomedical parameters. Purpose To use both traditional linear and novel non-linear methods to evaluate the impact of ambient air pollution on 24-hour heart rate variability. Methods From 2015/01 to 2015/12, patients undergoing 24-hour Holter ECG study in a tertiary medical center were enrolled as the study population. The patients were divided into two groups based on the severity of air pollution in their living areas (severe and mild air pollution groups). The two groups were matched by propensity-score matching. Linear and non-linear methods, including DFA and MSE, were used to calculate 24-hour heart rate variability. Results A total of 568 patients (284 patients in each group) were enrolled in this study. Traditional linear analysis of HRV didn’t show significant difference between the two groups regarding any time- or frequency-domain parameters. In non-linear analysis of HRV, DFA failed to detect significant difference between the two groups, while most MSE parameters (Area1-5, Ln_Area1-5, Area6-20 and Ln_Area6-20) were significantly lower in the severe air pollution group. The MSE analysis revealed significant differences across all scales (1 to 20). Conclusion(s) Ambient air pollution was associated with decreased 24-hour heart rate variability, and the impact was better evaluated by non-linear analysis (specifically multi-scale entropy) than traditional linear method.Table Figure

Linear and non-linear regression analysis for the sorption kinetics of methylene blue onto activated carbon

Background: Individuals with spinal cord injury (SCI) lose their abilities to stand. The effectiveness of compensating strategies such as the use of various orthosis is studied by stability analysis. These kind of analyses are usually studied by linear method. In this study, the standing stability of complete and incomplete SCI individuals is evaluated by linear versus non-linear methods. Methods: Study groups consisted of 10 normal, 5 incomplete and 5 complete SCI individuals. SCI participants stood with crutch and/or orthosis on a Kistler force plate. The excursions of center of pressure (COP), velocity of COP, correlation dimension, and approximate entropy in the anteroposterior and mediolateral planes were calculated in this study. Statistical analysis was done by one-way ANOVA and Post-hoc calculations by Tukey HSD. Results: Linear method revealed that the difference in “the mediolateral COP sway” and “anteroposterior COP velocity” was insignificant among the groups, whilst the difference in “anteroposterior COP sway”, “mediolateral COP velocity” and “total velocity” was significant. In contrast, non-linear method revealed that the difference in “mediolateral embedding diversion”, “anteroposterior embedding diversion”, “mediolateral correlation dimension” and “anteroposterior ApEn” was insignificant among the groups, whilst the difference in “anteroposterior correlation dimension” and “mediolateral ApEn” was significant. Conclusion: Based on linear method, the stability of SCI subjects seems to be like normal subjects. However, non-linear analysis revealed that although SCI patients knew how to put their body in a good posture to have a stable position, they had no abilities to control their posture.

With the rapid development of information technology and the rapid popularization of the Internet, while people enjoy the convenience and efficiency brought about by new technologies, they are also suffering from the harm caused by cyber attacks. In addition to efficiently thwarting network assaults, a high volume of complicated security event data might unintentionally increase the strain of policy makers. At present, NS threats mainly include network viruses, trojans, DOS (Denial-Of-Service), etc. For the increasingly complex Network Security (NS) problems, the traditional rule-based network monitoring technology is difficult to predict the unknown attack behavior. Environment-based, dynamic and integrated data fusion can integrate data from a macro perspective. In recent years, Machine Learning (ML) technology has developed rapidly, which could easily train, test and predict existing third-party models. It uses ML algorithms to find out the association between data rather than manually sets rules. Support vector machine is a common ML method, which can predict the security of the network well after training and testing. In order to monitor the overall security status of the entire network, NS situation awareness refers to the real-time and accurate reproduction of network attacks using the reconstruction approach. Situation awareness technology is a powerful network monitoring and security technology, but there are many problems in the existing NS technology. For example, the state of the network cannot be accurately detected, and its change rule cannot be understood. In order to effectively predict network attacks, this paper adopted a technology based on ML and data analysis, and constructed a NS situational awareness model. The results showed that the detection efficiency of the model based on ML and data analysis was 7.18% higher than that of the traditional NS state awareness model.

The effects of linear, nonlinear, and differential motor learning methods on the emergence of creative action in individual soccer players

Malicious software (ransom ware) cyber attacks in frequency and severity, posing an increasingly serious threat to computer systems everywhere. Malware detection is a hot study area as several multiple computers, organisations, and governments have been affected by an exponential rise in malware attacks. Dynamic and static assessment of malicious characteristics and behaviour patterns is time expensive and useless in real-time malware detection, according to current technologies. It is becoming increasingly common for malicious apps to use polymorphic and adaptive techniques to rapidly modify their behaviour and develop a number of new malicious apps. In order to undertake an effective malware analysis, machine learning techniques (MLAs) are increasingly being used to create new malware varieties. This approach is time-consuming since it requires considerable feature engineering, learning and representation of features. Moreover the feature extraction process could be effectively eliminated by using advanced MLAs like deep learning. These methods have been shown to perform better with a biased training dataset, which restricts their practical application in real-time scenarios. A new improved approach for successful zero-day malware detection must be developed in order to eliminate biases and analyze these approaches autonomously.