Analysis and Categorization of Emotet IoT Botnet Malware

To provide the ease control and remote monitoring, Internet of Things (IoT) plays an important role in smart devices. The IoT system ranges from smart city to healthcare sector, and supply chain management. This extent of advancement generated a huge amount of data which may be the reason of malware threats of the IoT system. IoT Malware is a threat which may affects all sectors such as business, network, telecoms, media, military, etc. The recent report claimed the proliferation of global cost of malware estimated that till 2023 it would be around 8 trillion dollars annually which may double due to coronavirus outbreak. The analysis of IoT malware needs serious concern as now warfare and digital retaliation can cause serious damage than the war lead on ground. The major aim of this paper is performing the critical analysis of an IoT malware named Emotet. The IoT malware analysis can be categorized in two types such as static and dynamic malware analysis. Static analysis is the process of analyzing malware or binary without executing it. It is considered a more effective method when it comes to the diversity of processor architecture. While dynamic analysis is based on the detection of malware and its behavior with real-time execution. This paper focused on the testbed and Analysis of Emotet malware statically and dynamically using distinguished malware analysis tools. © 2023 IEEE.

Computational fluid dynamic analysis of corona virus patients breathing in an airplane

Effective ventilation systems are essential to control the transmission of airborne aerosol particles, such as the SARS-CoV-2 virus in aircraft cabins, which is a significant concern for people commuting by airplane. Validated computational fluid dynamic models are frequently and effectively used to investigate air distribution and pollutant transport. In this study, the effectiveness of different ventilation systems with varying outlet vent locations were computationally compared to determine the best ventilation system for minimizing the risk of airborne transmission. The cabin air conditioning system was optimized to determine how design variables (air inlet temperature, outlet valve width and location, and mass flow rate) affect output parameters, including particle residence time, age of air, and thermal comfort conditions. Inlet mass flow rate was observed to be an influential variable impacting all output parameters, especially on age of air, where it was the most influential. In contrast, the least effective variable was width of the outlet valve, which only affected the particle residence time. Also, Predicted Mean Vote and Predicted Percentage Dissatisfied indices were the most affected by air inlet temperature, which had an inverse relation, while the outlet valve location had the greatest effect on particle residence time. © 2023 Author(s).

DeepWare: Imaging Performance Counters With Deep Learning to Detect Ransomware

In the year passed, rarely a month passes without a ransomware incident being published in a newspaper or social media. In addition to the rise in the frequency of ransomware attacks, emerging attacks are very effective as they utilize sophisticated techniques to bypass existing organizational security perimeter. To tackle this issue, this paper presents "DeepWare,” which is a ransomware detection model inspired by deep learning and hardware performance counter (HPC). Different from previous works aiming to check all HPC results returned from a single timing for every running process, DeepWare carries out a simple yet effective concept of " imaging hardware performance counters with deep learning to detect ransomware ,” so as to identify ransomware efficiently and effectively. To be more specific, DeepWare monitors the system-wide change in the distribution of HPC data. By imaging the HPC values and restructuring the conventional CNN model, DeepWare can address HPC's nondeterminism issue by extracting the event-specific and event-wise behavioral features, which allows it to distinguish the ransomware activity from the benign one effectively. The experiment results across ransomware families show that the proposed DeepWare is effective at detecting different classes of ransomware with the 98.6% recall score, which is 84.41%, 60.93%, and 21% improvement over RATAFIA , OC-SVM , and EGB models respectively. DeepWare achieves an average MCC score of 96.8% and nearly zero false-positive rates by using just a 100 ms snapshot of HPC data. This timeliness of DeepWare is critical on the ground that organizations and individuals have the opportunity to take countermeasures in the first stage of the attack. Besides, the experiment conducted on unseen ransomware families such as CoronaVirus, Ryuk, and Dharma demonstrates that DeepWare has excellent potential to be a useful tool for zero-day attack detection.

Real Time Multistage Attack Detection Leveraging Machine Learning and MITRE Framework

Organizations regardless of their size are rapidly transforming, adopting and embracing digitalization amid the COVID pandemic. The pandemic forced organizations to ratio- nalize offline operations and swift towards online operations. Many organizations have digitized their services and have witnessed increasing Multistage cyber-attacks. Further, a lot of organizations have enabled remote access to the enterprise resources and services. As a result, organizations are striving to defend against Multistage cyber-attacks. These multistage attacks often spread across many stages, which is best described by MITRE Adversarial Tactics, Techniques, and Common Knowl- edge (ATT&CK) Framework. There are many research efforts for static detection of malicious binaries but very few or limited research targeting run-time detection of malicious processes in the system. Detection of these malicious processes are key for identifying new variants of multistage attacks or malware in the real world. This paper proposes a system for detecting multistage attacks in real-time or run-time by leveraging Machine learning and MITRE ATT&CK Framework. Machine learning facilitates detecting the malicious process in the system, and the MITRE ATT&CK framework offers insight into adversary techniques. Combination of these two is very effective in detecting multistage attacks and identifying individual stages. The proposed system shows promising results when tested on real-time/latest malware. Test result shows that our system can achieve 95.83% of accuracy. This paper discusses the challenges in detection of runtime malware, dataset generation © 2022 IEEE.

A Study on Hospitality and Tourism Management Pre and Post Covid

This research demonstrates that the geospatial method adds greatly to identifying pre-and post-COVID 19 pandemic situations and aiding in sound decision-making, not only in Rajasthan but globally. Results reveal that hotels with a more management mindset are more likely to use dynamic pricing techniques. When the pandemic's intensity is severe, hotel managers resort to a more streamlined booking portfolio. Hotel owners and the pricing community as a whole may use our theoretical implications and practical management levers to increase profits as much as possible during the epidemic. This research set out to learn how the hotel industry's top brass think the sector would fare in the near future, and whether or not the response to the shutdown and following decline in domestic and international travel should have been handled better. Research like this is important because it draws attention to the fact that more precautions may have been taken to mitigate the disaster's effects. Qualitative surveys sent through email to partners in the hotel sector and their responses provide light on the days and hours leading up to the lockdown. Copyright © 2022, Anka Publishers. All rights reserved.

Memory Forensics-Based Malware Detection Using Computer Vision and Machine Learning

Malware has recently grown exponentially in recent years and poses a serious threat to individual users, corporations, banks, and government agencies. This can be seen from the growth of Advanced Persistent Threats (APTs) that make use of advance and sophisticated malware. With the wide availability of computer-automated tools such as constructors, email flooders, and spoofers. Thus, it is now easy for users who are not technically inclined to create variations in existing malware. Researchers have developed various defense techniques in response to these threats, such as static and dynamic malware analyses. These techniques are ineffective at detecting new malware in the main memory of the computer and otherwise require considerable effort and domain-specific expertise. Moreover, recent techniques of malware detection require a long time for training and occupy a large amount of memory due to their reliance on multiple factors. In this paper, we propose a computer vision-based technique for detecting malware that resides in the main computer memory in which our technique is faster or memory efficient. It works by taking portable executables in a virtual environment to extract memory dump files from the volatile memory and transform them into a particular image format. The computer vision-based contrast-limited adaptive histogram equalization and the wavelet transform are used to improve the contrast of neighboring pixel and to reduce the entropy. We then use the support vector machine, random forest, decision tree, and XGBOOST machine learning classifiers to train the model on the transformed images with dimensions of 112 × 112 and 56 × 56. The proposed technique was able to detect and classify malware with an accuracy rate of 97.01%. Its precision, recall, and F1-score were 97.36%, 95.65%, and 96.36%, respectively. Our finding shows that our technique in preparing dataset with more efficient features to be trained by the Machine Learning classifiers has resulted in significant performance in terms of accuracy, precision, recall, F1-score, speed and memory consumption. The performance has superseded most of the existing techniques in its unique approach.

Simulation of Virus-Laden Droplets Transmitted from Lung to Lung

In this study, we conducted a computational fluid dynamics analysis to estimate the trajectory of the virus-laden droplets. As numerical models, two human body models with airways were prepared. These models are represented by unstructured grids. Having calculated the unsteady airflow in the room, we simulated the trajectory of droplets emitted by the human speaking. In addition, inhaling the droplets into the lung of the conversation partner was simulated. The number of the droplets adhered to the respiratory lining of the partner was counted separately on the nasal cavity, oral cavity, trachea, bronchi, and bronchial inlet surface. The diameters of the droplets were also investigated in the same manner. It was noticeable that more than 80% of the droplets inhaled by the conversation partner adhered to the bronchial inlet surface. Also, the conversation partner did not inhale droplets larger than 35 μm in diameter. It was found that when the distance between two people was 0.75 m, more droplets adhered to the partner’s torso. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Tax compliance, public spending and happiness in Europe

Purpose>This study aims to analyze whether tax compliance is the basis for the short-run dynamics of the development of welfare and happiness. The strengthening of tax compliance of corporates and citizens is not only important to achieve the goals assumed by fiscal policy but also is part of the values that can generate a higher level of welfare and happiness in Europe.Design/methodology/approach>This study uses a dynamic factor model to offer new indexes that allow to monitor tax compliance, public spending and happiness trajectories and to evaluate their short-run relationships. Next, an analysis of the cyclical characteristics in terms of duration, amplitude and intensity is provided using the Harding and Pagan method (2002).Findings>The empirical findings show that the European countries were able to reinforce tax compliance during the expansionary periods of the economy, and this has made it possible to increase public spending, and indirectly, happiness. Otherwise, this paper shows that the contractions of public resources during the global crisis, such as the case in the COVID-19, reduced the possibilities of well-being in Europe and made it more difficult to increase public spending and happiness.Research limitations/implications>This study tries to analyze the transmission channels and relationships of three very complex variables: tax compliance, public spending and happiness. Incorporating these three variables into this research, with a short-run perspective, the authors have opened a new line of research that enriched the previous analysis. Therefore, the authors’ results should be considered the first step, that this study is going to continue to unravel the complexity of these relationships.Practical implications>The design of policies aimed at improving individual, corporate and the well-being of nations needs them to incorporate elements of tax compliance as an objective that has economic and social implications. Individuals and corporates contribute to a fairer and more equitable society through compliance with tax obligations.Originality/value>To the best of the authors’ knowledge, this is the first paper that offers evidence on the short-run dynamics of tax revenue, public spending and happiness for a better understanding of their relationships and behavior during the different periods of the economy.

Computational Modelling of VIG Plates Using FEM: Static and Dynamic Analysis.

Vacuum insulated glass (VIG) panels are becoming more and more popular due to their good thermal performance. Little information about the mechanical or strength parameters, which are crucial for the durability of a window, might be found in the published papers. The purpose of this work was to analyse the impact of different parameters on VIG panels' mechanical properties. Parameter diversity refers to both geometrical and material characteristics. Static and dynamic analyses using the finite element method (ABAQUS program) were conducted. In addition, 101 various numerical models, created with the use of Python language, were tested. The changes of geometrical parameters were made with constant material parameters and the reverse. It has been concluded that pillars' material and geometrical properties are crucial considering not only the static response of the VIG plates, but also the dynamic one. Moreover, it was proven that getting rid of the first row of pillars near every edge seal led to an increase in deflection of the glass panes. Additionally, considering results for dynamic response associated with out-of-phase vibrations, spacing between support pillars should be large enough in order to avoid possible damage to the glass pane due to rapidly decreasing distance between them. Further research opportunities have been described.

Collection of the Main Anti-Virus Detection and Bypass Techniques

A large amount a new threats, technologies and business models have emerged in the cybersecurity area through the COVID-19 pandemic. The remote work involved unplanned cloud migrations and swift procurement of IT products and services the remote landscape. In this context, the role of anti-viruses is crucial for the private life and work. In this paper, we study the workings of anti-viruses as to understand how to avoid them. We created a collection of the main bypass techniques whilst analyzing their respective advantages and drawbacks. We show that it is possible to avoid both static and emulation analyses, while enunciating the techniques and approaches being used. © 2021, Springer Nature Switzerland AG.

Tax compliance, public spending and happiness in Europe

Purpose This study aims to analyze whether tax compliance is the basis for the short-run dynamics of the development of welfare and happiness. The strengthening of tax compliance of corporates and citizens is not only important to achieve the goals assumed by fiscal policy but also is part of the values that can generate a higher level of welfare and happiness in Europe. Design/methodology/approach This study uses a dynamic factor model to offer new indexes that allow to monitor tax compliance, public spending and happiness trajectories and to evaluate their short-run relationships. Next, an analysis of the cyclical characteristics in terms of duration, amplitude and intensity is provided using the Harding and Pagan method (2002). Findings The empirical findings show that the European countries were able to reinforce tax compliance during the expansionary periods of the economy, and this has made it possible to increase public spending, and indirectly, happiness. Otherwise, this paper shows that the contractions of public resources during the global crisis, such as the case in the COVID-19, reduced the possibilities of well-being in Europe and made it more difficult to increase public spending and happiness. Research limitations/implications This study tries to analyze the transmission channels and relationships of three very complex variables: tax compliance, public spending and happiness. Incorporating these three variables into this research, with a short-run perspective, the authors have opened a new line of research that enriched the previous analysis. Therefore, the authors' results should be considered the first step, that this study is going to continue to unravel the complexity of these relationships. Practical implications The design of policies aimed at improving individual, corporate and the well-being of nations needs them to incorporate elements of tax compliance as an objective that has economic and social implications. Individuals and corporates contribute to a fairer and more equitable society through compliance with tax obligations. Originality/value To the best of the authors' knowledge, this is the first paper that offers evidence on the short-run dynamics of tax revenue, public spending and happiness for a better understanding of their relationships and behavior during the different periods of the economy.

Design of sustainable intelligent food information system through computer dynamic system model

Since COVID-19, the already fragile food system has become even more overburdened, and food security has become an urgent issue. Due to the excessive pursuit of efficiency and profit, the food system in the past has created various problems, the most prominent problem is the inability to find a balance between profit and ecological environment. Therefore, this article focuses on how to build a model that includes both aspects. In order to analyse the existing food system and introduce new ones, firstly, divide the food system into four subsystems of profitability, efficiency, sustainability, and equity, and set up several secondary indicators under each subsystem, and select appropriate countries as samples. Use the AHP, EWM, and CEM to calculate the corresponding index of each subsystem to form a new comprehensive evaluation model for the food system. © 2021 ACM.