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The challenge of securing critical data increases year after year. Evolving technology developments, involving the growth in cloud and the Internet of Things adoption make businesses' confidential data more vulnerable to sophisticated attackers. Protect the Whole Organization by using the Industry's First Extended Detection and Response (XDR) Platform Security teams have been inundated with inaccurate, inadequate alerts. As a result of today's siloed security tools, specialists should pivot from the console to the console to piece together investigative clues, which will result in horribly slow investigations. Although they've implemented countless tools, teams still lack enterprise-wide visibility as well as the deep analytics necessary to locate threats. Confronted with a lack of security professionals, teams need to streamline operations. Extended Detection and Response is the world's very first extended detection and response platform which integrates endpoint, network, as well as cloud data to halt advanced attacks. It combines prevention, investigation, detection, and response in a single platform for unparalleled security and operational effectiveness. In combination with a Managed Threat Hunting assistance, XDR offers continuous protection and industry-leading coverage. A new and more comprehensive approach to detection and response is clearly needed, one which not just includes traditional endpoints but then also includes the enhanced attack surface like the network and cloud. Luckily, these are only a few of the difficulties XDR was intended to solve. XDR unites and extends detection and response capacity through multiple security layers, offering security teams along with centralized end-to-end enterprise visibility, strong analytics, automatic response across the entire technology stack. XDR, clients can get integrated and proactive security measures designed to protect the whole technology stack, which makes it easier for security analysts to detect and stop attacks in progress prior to the impact to the business. Companies of all sizes and types, irrespective of their levels of cybersecurity expertise, is necessary to be considered sophisticated detection, improved visibility, and immediate response to sophisticated threats. The goal here is to explain what XDR is and how it empowers Information Technology, security teams, to stop threats and put them on the defensive. And also show how it provides superior extensibility and analytics which will fit the needs of the future. In the present article, we'll describe the fundamentals of XDR, and demonstrate how it help out for organizations as well as how it facilitates new security challenges. Moreover, this research paper will be useful for organizations to understand XDR in-depth, as well as how XDR can assist organizations in preventing cyberattacks as well as simplifying and improving security processes. In addition, this paper explains XDR, the capability of current and emerging technologies to offer greater visibility, collect and correlate threat information, and utilize analytics and automation to detect today and future attacks.

A potential to extract detailed textual image texture features is a key characteristic of the suggested approach, instead of using a single spatial texture feature. For the generation of MCs, four textured characteristics (including horizontal and vertical) are assumed in this paper that are content, coarseness, contrast, and directionality. The morphological parts of a clandestine text-based image were further segmented and then usually inserted into the least significant bit in cover pixels utilising spatial steganography. This same reverse process for steganography and MCA is conducted on the recipient side after transmission. The results demonstrate that the proposed method based on fusion of MCA and steganography provides a higher performance measure, for instance peak signal-to-noise ratio, SSIM, than the previous method.

The principle objective of this paper is to build up a Computational Private Information Retrieval conventions on cloud design utilizing deletion code for made sure about information sending. These conventions are too costly in view of the fact that they are combining complicated numerical operations for the whole database. Essentially distributed storage engineering would provide a range of higher performing capacity staff that provides long-term internet storage management and even the distributed storage architecture. Putting information away and retrieving it in an external cloud system and public review plan creates difficulties and disputes about classification of information during transfers. On any outsider's case, this dispute is usually a massive amount of knowledge stored with the cloud worker. Also you can defeat this problem by means of various methods such as encryption, key encryption, etc. In either case, overall encryption plans maintain confidentiality of information during the exchange, thus restricting the utility of the capability system in parallel with this cycle. This is because a few activities have only been supported by scrambled knowledge. These techniques will cause disappointment. To building a protected stockpiling framework that underpins different capacities is testing when the capacity framework is appropriated and has no focal power.

The world as we know it has changed over a short period of time, with the rise and spread of the deadly novel Corona virus known as COVID-19, the world will never be the same again. This study explores the devastating effects of the novel virus pandemic, the resulting lockdown, thus the need to transform the offline classroom into an online classroom. It explores and describes the numerous online teaching platforms, study materials, techniques, and technologies’ being used to ensure that educating the students does not stop. Furthermore, it identifies the platforms, technologies which can be used to conduct online examination in a safe environment devoid of cheating. Additionally, it explores the challenges facing the deployment of online teaching methods. On the basis of literature review, a framework was proposed to deliver superior online class room experience for the students, so that online classroom is as effective as or even better than offline classrooms. The identified variables were empirically tested with the aid of a structured questionnaire; there were 487(according to Craitier and Morgan)150 number of respondents who were purposefully sampled. The results indicate that students prefer the multimedia means of studies. As a result of binary logistic regression, poor internet connection, awareness on COVID-19, enough sources of materials, recommends massive open online course, favourite online methods, and satisfaction with online study are significant in the model or attitudes towards delivering of online classes during lockdown COVID-19 pandemic at 5% level of significance. The study recommends online teaching methods, but finally, the study concludes that satisfaction with online study is significant in the model or attitudes towards delivering of online classes during lockdown COVID-19 pandemic at 5% level of significance.

Blind people can benefit greatly from a system capable of localising and reading comprehension text embedded in natural scenes and providing useful information that boosts their self-esteem and autonomy in everyday situations. Regardless of the fact that existing optical character recognition programmes seem to be quick and effective, the majority of them are not able to correctly recognise text embedded in usual panorama images. The methodology described in this paper is to localise textual image regions and pre-process them using the naïve Bayesian algorithm. A weighted reading technique is used to generate the correct text data from the complicated image regions. Usually, images hold some disturbance as a result of the fact that filtration is proposed during the early pre-processing step. To restore the image's quality, the input image is processed employing gradient and contrast image methods. Following that, the contrast of the source images would be enhanced using an adaptive image map. The stroke width transform, Gabor’s transform, and weighted naïve Bayesian classifier methodologies have been used in complicated degraded images to segment, feature extraction, and detect textual and non-textual elements. Finally, to identify categorised textual data, the confluence of deep neural networks and particle swarm optimisation is being used. The text in the image is transformed into an acoustic output after identification. The dataset IIIT5K is used for the development portion, and the performance of the suggested come up is evaluated using parameters such as accuracy, recall, precision, and F1-score.

In the future, the majority of vehicles could be intelligent and autonomous. In order to achieve this, the 6G will play a predominant role in vehicular technology. Moreover, the next generation 6G wireless communication is an emerging technology that will revolutionize vehicular technology. In future, the new 6G technology is expected to connect more devices in our world. So, it needs to provide excellent performance over recent state of the art communication technology like 5G. The primary features of 6G technology are heterogeneous object connectivity, low power consumption, high throughput, high data reliability, ultra-low latency. Furthermore, it will provide intelligent and secure communication with the help of Artificial Intelligence (AI) and Block-chain technology. First, this paper presents the introduction of 6G technology. Later, we discuss the 6G enabling technology, which includes Internet of Everything (IoE), AI and Edge intelligence. Then, this paper mainly concentrates on how to help 6G based vehicular communication in various vehicular applications. Finally, we address the various research challenges on 6G based vehicular communication. Thus, it will be helpful for future research to carry out in the right direction.

The nation has already had to contend with such a major public health issue when this COVID-19 disease was propagated in March 2020. The corona's propagation does have an effect on all areas of people's lives. The hostile effect on education systems throughout all locations is indeed a diverse expansion impact. A complete shutdown keeps adding fresh problems for students to learn and also for educators to effectively manage the class, likely to result in the conversion of such an offline education system into a virtual class. This study investigated and described various online learning platforms, as well as teaching resource distribution technology and modern technologies used to ensure that students can study. Overall, this relates to channels that can be used to create a confidential environment in order to prevent copying in online tests. This also outlines so many difficulties in managing online teaching methods, like learners' perceptions of e-learning being more traumatic and having a negative impact on general wellbeing and social relationships. So, a technique has been proposed to provide learners with a superior online classroom teaching experience, allowing the online classroom to be as good as, if not better than, a single online classroom. This study focused on daily teaching methods that employ online learning supported by a machine teaching approach to provide an individual with a relevant stress-free solution.

In Santo Domingo de los Tsa'chilas province, Ecuador, the population grows proportionally to the territorial extension in urban and rural parishes; therefore, the conception of domestic solid waste has increased exponentially. In this context, in recent years, the distribution of routes for waste collection has not been dealt with or technically explored. The research objective is to apply the theory of graphs to the sector and use the exact method of the Travel Agent Problem (TSP) in dynamic programming to generate optimal routes by sectors. In addition to measuring the variables longitudinally, we test the researcher's hypothesis using parametric techniques for independent samples in the variable's travel time and distance between the usual route and the new route in the Río Verde parish of Santo Domingo Canton.

The immense measure of classified information has been moved on the internet. Information security turns out to be progressively significant for some applications, for instance, private transmission, video observation, military, and clinical applications. Lately, there has been a great deal of enthusiasm for steganography and steganalysis. Steganography is the specialty of covering up and transmitting information through clearly harmless transporters with an end goal to disguise the presence of information. The advanced picture information, for example, BMP, JPEG, and GIF, are generally utilized as a transporter for steganography. Here the mystery message is implanted into a picture (or any media) called spread picture and afterward sent to the beneficiary who extricates the mystery message from the spread message. This picture ought not to be discernible from the spread picture, with the goal that the aggressor can't find any implanted message. The authors have proposed three approaches of steganography that can easily support message privacy.

In the information technology community, communication is a vital issue. And image transfer creates a major role in the communication of data through various insecure channels. Security concerns may forestall the direct sharing of information and how these different gatherings cooperatively direct data mining without penetrating information security presents a challenge. Cryptography includes changing over a message text into an unintelligible figure and steganography inserts message into a spread media and shroud its reality. Both these plans are successfully actualized in images. To facilitate a safer transfer of image, many cryptosystems have been proposed for the image encryption scheme. This chapter proposes an innovative image encryption method that is quicker than the current researches. The secret key is encrypted using an asymmetric cryptographic algorithm and it is embedded in the ciphered image using the LSB technique. Statistical analysis of the proposed approach shows that the researcher's approach is faster and has optimal accuracy.

Digital enterprise transformation is the amalgamation of digital techniques into the scopes of a business enterprise, fundamentally altering how one can employ and furnish ethics to clients. An organization can be authorized to take digital transformation due to several reasons. But the most important reason is that it is the survival issuance for many people. Digital transformation considers dissimilarity in every organization. Generally, it is the amalgamation of digital technology into all quarters of business. That consolidation brings about major alterations in how the business functions and conveys usefulness to its clients. Here, steganography and cryptography are used to facilitate digital transformation in any business.

This work's primary goal is to secure the transmission of text hidden within the cover image using steganography over a public network of computers. Steganography is a powerful tool for concealing information within a cover image so that the concealed message remains undetectable. As a result, steganography refers to concealed writing. The secure transmission of information over a public network communication channel using steganography occurs in two stages, the first on the sender side and the second on the receiver side. In the first phase, steganography is normally applied to conceal the encrypted information within the image as a cover. The encrypted data is implanted inside the cover image using an improved least significant bit steganography method. The secret key obtained by the embedding algorithm is shared with the message retrieval algorithm on the receiver side to retrieve the message. Finally, the embedded text message is identified using a hybrid convolution regression adaptive integrated neural network (CRAINN) approach.

In this digital world, a set of information about the real-world entities is collected and stored in a common place for extraction. When the information generated has no meaning, it will convert into meaningful information with a set of rules. Those data have to be converted from one form to another form based on the attributes where it was generated. Storing these data with huge volume in one place and retrieving from the repository reveals complications. To overcome the problem of extraction, a set of rules and algorithms was framed by the standards and researchers. Mining the data from the repository by certain principles is called data mining. It has a lot of algorithms and rules for extraction from the data warehouses. But when the data is stored under a common structure on the repository, the values derived from that huge volume are complicated. Computing statistical data using data mining provides the exact information about the real-world applications like population, weather report, and probability of occurrences.

SARS-CoV-2 also known as COVID-19 is a novel corona virus which originated from China. Corona virus disease COVID-19 is the official name given by WHO is caused by SARS (Severe Acute Respiratory Syndrome) which have very mild symptoms in the beginning but slowly progress to failure of multiple organs followed by death. The emergency committee of WHO declared COVID-19 as a pandemic. The disease is spread from person-to-person and the spread is very fast making serious impacts on the lives of the people. CT scan and PCR (Polymerase Chain Reaction) are the tests for the diagnosis of COVID-19 virus in the human body. The symptoms of this disease are very common which include fever, breath shortness, dry cough and pain in the muscles. The solutions which are available currently have less accuracy, take longer time and are costly. In this paper, a framework is proposed based on smart phones and artificial intelligence. The benefits are increased productivity, reliability and availability of advanced infrastructure. There are various sensors embedded in the smart phones such as proximity sensor, light sensor, accelerometer, gyroscope and fingerprint sensors which have very fast processors and memory space making it easy to read the sensors and scan the CT images, the signal measurements in the sensors of the smart phones are read which scan the CT images for reliable diagnosis of the disease. The proposed framework is based on artificial intelligence, cloud network and machine learning.

Steganography deals with the craft of obscuring private data inside a spread media. In confidential data communication security is a vital issue. In this paper, we use a two-layer security. At first, data encryption is achieved by the method of RSA algorithm of asymmetric cryptography, and later the ciphered data is hidden into host image by an innovative embedding technique. To hide our ciphered data into host image, we modify the existing LSB technique and use a mapping function that ensures a secure and confidential image steganography resulting in a stego image. Here cryptography is blended with steganography and provides two level security in the confidential data transmission over the internet.

INTRODUCTION: The quantity of audio and visual data is increasing exponentially due to the internet's rapid growth. The digital information in images and videos could be used for fully automated captions, indexing, and image structuring. The online image and video data system has seen a significant increase. In such a dataset, images and videos must be retrieved, explored, as well as inspected. OBJECTIVES: Text extraction is crucial for locating critical as well as important data. Disturbance is indeed a critical factor that affects image quality, and this is primarily generated during image acquisition and communication operations. An image can be contaminated by a variety of noise-type disturbances. A text in the complex image includes a variety of information which is used to recognise textual as well as non-textual particulars. The particulars in the complicated corrupted images have been considered important for individuals seeing the entire issue. However, text in complicated degraded images exhibits a rapidly changing form in an unconstrained circumstance, making textual data recognition complicated.METHODS: The naïve bayes algorithm is a weighted reading technique is used to generate the correct text data from the complicated image regions. Usually, images hold some disturbance as a result of the fact that filtration is proposed during the early pre-processing step. To restore the image's quality, the input image is processed employing gradient and contrast image methods. Following that, the contrast of the source images would be enhanced using an adaptive image map. Stroke width transform, Gabor transform, and weighted naïve bayes classifier methodologies have been used in complicated degraded images to segment, features extraction, and detect textual and non-textual elements. RESULTS: Finally, to identify categorised textual data, the confluence of deep neural networks and particle swarm optimization is being used. The dataset IIIT5K is used for the development portion, and also the performance of the suggested methodology is assessed by utilizing parameters like as accuracy, recall, precision, and F1 score. It performs well enough for record collections such as articles, even when significantly distorted, and is thus suitable for creating library information system databases.CONCLUSION: A combination of deep neural network and particle swarm optimization is being used to recognise classified text. The dataset IIIT5K is used for the development portion, and while high performance is achieved with parameters such as accuracy, recall, precision, and F1 score, characters may occasionally deviate. Alternatively, the same character is frequently extracted [3] multiple times, which may result in incorrect textual data being extracted from natural images. As a result, an efficient technique for avoiding such flaws in the text retrieval process must be implemented in the near future.

Based on bioinformatics algorithm, there are a wide range of implementations. With the urge for program speed, many applications take the heuristic approach to compensate running time. One of the most critical shortcomings of this technique is the loss of optimality, i.e. the desired results may not always be found. To overcome this problem, many different hardware architectures have been experimented for bioinformatics algorithm such as cell broadband engine, cluster and compute unified device architecture where, the main technique for obtaining high performance is to parallelize the task to be run simultaneously by multiple vector execution units with single instruction multiple data and by multiple processors with multiple instruction multiple data. In this paper we presents a survey of data intensive bioinformatics applications on variety of parallel Architecture that are available for accelerating the processing of large biological data set .