Academic literature on the topic 'Malware strain'

Encryption has protected the Internet for some time now and it has come to raise user trust on the otherwise unsecure Internet. However, recent years have seen the use of robust encryption as stepping stone for cyber-criminal activities. Ransomware has not escaped the headlines even as it has attacked almost every sector of the society using a myriad of infection vectors. Mission critical data has been held to ransom and victims have had to part away with millions of dollars. The advent of the anonymous Bitcoin network has made matters worse where it’s been virtually infeasible to trace the perpetrators. In this paper, we endeavor to perform dynamic analysis of WannaCry ransomware samples based on malwarefree infection vectors. Further, we perform reverse-engineering to dissect the ransomware code for further analysis. Results show that despite the use of resilient encryption, the ransomware like other families in the wild uses the same attack structure and cryptographic primitives. Our analysis leads us to the conclusion that this ransomware strain isn't as complex as previously reported. This detailed practical analysis tries to raise awareness to the business community on the realities and importance of IT security whilst hinting on prevention, recovery and the limitations thereof.

The advancement in recent IoT devices has led to catastrophic attacks on the devices resulting in breaches in user privacy and exhausting resources of various organizations, so that users and organizations expend increased time and money. One such harmful malware is Mirai, which has created worldwide recognition by impacting the digital world. There are several ways to detect Mirai, but the Machine Learning approach has proved to be accurate and reliable in detecting malware. In this research, a novel-based approach of detecting Mirai using Machine Learning Algorithm is proposed and implemented in Matlab and Python. To evaluate the proposed approaches, Mirai and Benign datasets are considered and training is performed on the dataset comprised of a Training set, Cross-Validation set and Test set using Artificial Neural Network (ANN) consisting of neurons in the hidden layer, which provides consistent accuracy, precision, recall and F-1 score. In this research, an accurate number of hidden layers and neurons are chosen to avoid the problem of Overfitting. This research provides a comparative analysis between ANN and Random Forest models of the dataset formed by merging Mirai and benign datasets of the Mirai malware detection pertaining to seven IoT devices. The dataset used in this research is “N-BaIoT” dataset, which represents data in the features infected by Mirai Malware. The results are found to be accurate and reliable as the best performance was achieved with an accuracy of 92.8% and False Negative rate of 0.3% and F-1 score of 0.99. The expected outcomes of this project, include major findings towards cost-effective Learning solutions in detecting Mirai Malware strains.

Soybean cultivation in many zones of India shows occurrence of native rhizobia besides other exotically adapted strains. In the current study, 22 rhizobial isolates (recovered from 12 different soybean growing sites) and 8 reference strains were selected for biochemical and metabolic characterization. Of 22 isolates, 18 were recovered as fast growing isolates while the rest were slow growing based on bromothymol blue (BTB) test. Unlike earlier belief that rhizobia have no ability to grow on glucose peptone agar medium, in this study, some isolates and some reference strains grew well on this medium. Similarly, when all the isolates were subjected to ketolactose test, some of the isolates were found to show growth on the medium. In contrast, based on C-utilization pattern (15 carbohydrates) a remarkable metabolic diversity was observed among the rhizobial isolates recovered in the study. The clustering and matching analysis showed that most of isolates were matching with slow growing reference strains, a few were with fast growing reference strains and some were found to be unique and hence not matching with any of reference strains. Such analysis suggests the occurrence of metabolically distinct types of rhizobia besides commonly known types (B. japonicum, B. elkanii and S. fredii) of soybean rhizobia and further validation is suggested through 16SrRNA gene sequencing technique.

<p><strong>Background:</strong> Although dermatophytes are found throughout the world, the most prevalent strains and the most common sites of infection vary by region. Hot, humid climates and overcrowding predispose populations to skin diseases; including tinea infections. This study highlights the clinical pattern and epidemiological characteristics of dermatophyte infection in the Malwa region of Punjab.</p><p><strong>Methods:</strong> A cross sectional prospective study was carried out over a period of two years depending on pre-structured questionnaire on a total of 272 patients clinically suspected cases with dermatophyte infection. Socio-demographic and clinical information was collected and sample was taken. The collected samples were subjected to direct microscopy with potassium hydroxide and cultured on Sabourauds dextrose agar to identify the fungal species</p><p><strong>Results:</strong> A total of 272 patients were included in the study in which 147 were males and 125 were females. Occupation wise housewives were the most affected group constituting 114 cases. <em>Tinea corporis</em> was the most common type found in 159 cases and<em> Trichophyton mentagrophytes </em>was the most common isolated fungi. Maximum number of dermatophyte cases was in the age group of 21-30 years with 69 cases.</p><p><strong>Conclusions:</strong> In current research we observed that hot and humid environment of Malwa region of Punjab increases the prevalence of fungal infection especially in the month of June to September and the occupation of people also plays an important role in prevalence of infection. We also observed that dermatophyte fungal infection is more common in adult males as compare to females.</p>

Introduction: There are many types of coronaviruses that causes respiratory and intestinal infection in humans. Among these coronaviruses, is a group of Severe Acute Respiratory Syndrome (SARS)-like bat coronavirus, including both SARS-CoV and SARS-Cov-2 that comprise a unique clade under the subgenus Sarbecovirus. The SARS-CoV-2 strains in India are more closely related to bat-CoVRaTG13 (93% homology) than pangolin CoV (83.5% homology). India reported its first case of Coronavirus Disease 19 (COVID-19) on January 30, 2020. Punjab, a state in northwestern India comprises of 22 districts which are classified into three major distinct regions viz. Majha, Malwa and Doaba. Four districts of Majha region of Indian state of Punjab are Amritsar, Tarn Taran, Gurdaspur and Pathankot. Aim: The present study describes the SARS-CoV-2 epidemiology based on samples tested for this virus; received at Viral Research and Diagnostic Laboratory (VRDL), Government Medical College, Amritsar-Punjab, India. Materials and Methods: This epidemiological study was designed taking into consideration three main factors i.e. the gender, the age groups and the factor whether the individual has any symptoms or not. To study their context of applicability and its interpretation, a total of 3,47,418 samples from period of 01st April 2020 to 31st October 2020 were included in this study. Ribonucleic Acid amplification by Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) of SARS-CoV-2 from nasopharyngeal/oropharyngeal swabs of all individuals revealed confirmation of 17,920 positive samples. Total 2,29,310 males and 1,18,108 females were tested for the COVID-19 infection. Social science statistics online tool (https://www.socscistatistics.com/tests/) was used for χ2 test to analyse different parameters. Results: Maximum number of COVID-19 positive patients was detected in 21 to 30 years age group (p<0.05). Highest positivity ratio of samples was observed in individuals exceeding the age of 50 years. Number of asymptomatic individuals was found to be quite higher than symptomatic individuals in total population. Overall percent positivity of asymptomatic individuals was 4.81% (p<0.00001), which accounts to 4.71% of total number of tested individuals in this study. Conclusion: COVID-19 is an evolving disease and data from this study elucidates the epidemiological profile of the SARS-CoV-2 infection among population of Majha region of Punjab state of Indian subcontinent.

Za posledné roky je zaznamenaný nárast prác zahrňujúcich komplexnú detekciu malvéru. Pre potreby zachytenia správania je často vhodné pouziť formát grafov. To je prípad antivírusového programu Avast, ktorého behaviorálny štít deteguje škodlivé správanie a ukladá ich vo forme grafov. Keďže sa jedná o proprietárne riešenie a Avast antivirus pracuje s vlastnou sadou charakterizovaného správania bolo nutné navrhnúť vlastnú metódu detekcie, ktorá bude postavená nad týmito grafmi správania. Táto práca analyzuje grafy správania škodlivého softvéru zachytené behavioralnym štítom antivírusového programu Avast pre proces hlbšej detekcie škodlivého softvéru. Detekcia škodlivého správania sa začína analýzou a abstrakciou vzorcov z grafu správania. Izolované vzory môžu efektívnejšie identifikovať dynamicky sa meniaci malware. Grafy správania sú uložené v databáze grafov Neo4j a každý deň sú zachytené tisíce z nich. Cieľom tejto práce bolo navrhnúť algoritmus na identifikáciu správania škodlivého softvéru s dôrazom na rýchlosť skenovania a jasnosť identifikovaných vzorcov správania. Identifikácia škodlivého správania spočíva v nájdení najdôležitejších vlastností natrénovaných klasifikátorov a následnej extrakcie podgrafu pozostávajúceho iba z týchto dôležitých vlastností uzlov a vzťahov medzi nimi. Následne je navrhnuté pravidlo pre hodnotenie extrahovaného podgrafu. Diplomová práca prebehla v spolupráci so spoločnosťou Avast Software s.r.o.

“Ransomware” is a type of malware that tries to extort money from its victims by hijacking the victim's computer in some way and holding it to ransom. It renders the computer unusable until a ransom amount is paid to the extortionist. And it is very unfortunate that, in many cases, even after the ransom is paid, the victim's computer is not released. Ransomware, as we know it, is gaining popularity at an immense rate among the most notorious cyber-criminals of the world. It isn't just a virus, it's a damned good game plan to becoming a millionaire in a matter of months! This chapter details what ransomware is, how it originated, which families of attacks it falls under, how ransomware is classified, the impact of ransomware on a global scale, the life cycle of a typical ransomware attack, mitigation and containment strategies, trending strains of 2017, and future trends in ransomware attacks. In future, these attack types will dominate our industries and wreak havoc, destroying life and property, if left unchecked.