scholarly journals Effect of Cybercrime in Real World

Author(s):  
Raghav Gupta

The Internet is a space that utilises the electronic and electromagnetic range to store, change, and exchange information through the organisation and system related to physical organisations. The Internet is an infinite space known as the Internet. PC exchanges, particularly exchanges between various PCs, can be seen as a space. Pictures and text on the Internet exist on the Internet. The term is used to describe computer-generated reality, naming the non-existent spot where a virtual item exists. There is a chance that a PC makes an image of a structure that permits the designer to "amble" and see the idea of a program. The system is supposed to be on the Internet. Cybercrime is a sequence of coordinated criminal attacks on the Internet and network safety. Cybercrime is like Hacking into the PC, can be through an organisational system and tapping on new connections interfacing with unnoticed Wi-Fi, downloading programming and documents to critical destinations, burning-through energy, electromagnetic radiation waves, and more. Network safety is a major issue and should be treated seriously as it has turned into a public concern. Most electronic gadgets like PCs, workstations and PDAs accompany worked in firewall security programming. PCs are not 100% safe and solid in securing our information.

Author(s):  
Nestor J. Zaluzec

The Information SuperHighway, Email, The Internet, FTP, BBS, Modems, : all buzz words which are becoming more and more routine in our daily life. Confusing terminology? Hopefully it won't be in a few minutes, all you need is to have a handle on a few basic concepts and terms and you will be on-line with the rest of the "telecommunication experts". These terms all refer to some type or aspect of tools associated with a range of computer-based communication software and hardware. They are in fact far less complex than the instruments we use on a day to day basis as microscopist's and microanalyst's. The key is for each of us to know what each is and how to make use of the wealth of information which they can make available to us for the asking. Basically all of these items relate to mechanisms and protocols by which we as scientists can easily exchange information rapidly and efficiently to colleagues in the office down the hall, or half-way around the world using computers and various communications media. The purpose of this tutorial/paper is to outline and demonstrate the basic ideas of some of the major information systems available to all of us today. For the sake of simplicity we will break this presentation down into two distinct (but as we shall see later connected) areas: telecommunications over conventional phone lines, and telecommunications by computer networks. Live tutorial/demonstrations of both procedures will be presented in the Computer Workshop/Software Exchange during the course of the meeting.


Author(s):  
Е.Н. Юдина

интернет-пространство стало частью реального мира современных студентов. В наши дни особенно актуальна проблема активизации использования интернета как дополнительного ресурса в образовательном процессе. В статье приводятся результаты небольшого социологического исследования, посвященного использованию интернета в преподавании социологических дисциплин. Internet space has become a part of the real world of modern students. The problem of increasing the use of the Internet as an additional resource in the educational process is now particularly topical. The article contains the results of a small sociological study on the use of the Internet in teaching sociological disciplines.


2018 ◽  
Vol 3 (1) ◽  
pp. 22-32 ◽  
Author(s):  
Ernest Ezema ◽  
Azizol Abdullah ◽  
Nor Fazlida Binti Mohd

The concept of the Internet of Things (IoT) has evolved over time. The introduction of the Internet of Things and Services into the manufacturing environment has ushered in a fourth industrial revolution: Industry 4.0. It is no doubt that the world is undergoing constant transformations that somehow change the trajectory and history of humanity. We can illustrate this with the first and second industrial revolutions and the information revolution. IoT is a paradigm based on the internet that comprises many interconnected technologies like RFID (Radio Frequency Identification) and WSAN (Wireless Sensor and Actor Networks) to exchange information. The current needs for better control, monitoring and management in many areas, and the ongoing research in this field, have originated the appearance and creation of multiple systems like smart-home, smart-city and smart-grid. The IoT services can have centralized or distributed architecture. The centralized approach provides is where central entities acquire, process, and provide information while the distributed architectures, is where entities at the edge of the network exchange information and collaborate with each other in a dynamic way. To understand the two approaches, it is necessary to know its advantages and disadvantages especially in terms of security and privacy issues. This paper shows that the distributed approach has various challenges that need to be solved. But also, various interesting properties and strengths. In this paper we present the main research challenges and the existing solutions in the field of IoT security, identifying open issues, the industrial revolution and suggesting some hints for future research.


2021 ◽  
pp. 254-267
Author(s):  
John Royce

Good readers evaluate as they go along, open to triggers and alarms which warn that something is not quite right, or that something has not been understood. Evaluation is a vital component of information literacy, a keystone for reading with understanding. It is also a complex, complicated process. Failure to evaluate well may prove expensive. The nature and amount of information on the Internet make evaluation skills ever more necessary. Looking at research studies in reading and in evaluation, real-life problems are suggested for teaching, modelling and discussion, to bring greater awareness to good, and to less good, readers.


2021 ◽  
Vol 5 (1) ◽  
pp. 28-39
Author(s):  
Minami Yoda ◽  
Shuji Sakuraba ◽  
Yuichi Sei ◽  
Yasuyuki Tahara ◽  
Akihiko Ohsuga

Internet of Things (IoT) for smart homes enhances convenience; however, it also introduces the risk of the leakage of private data. TOP10 IoT of OWASP 2018 shows that the first vulnerability is ”Weak, easy to predict, or embedded passwords.” This problem poses a risk because a user can not fix, change, or detect a password if it is embedded in firmware because only the developer of the firmware can control an update. In this study, we propose a lightweight method to detect the hardcoded username and password in IoT devices using a static analysis called Socket Search and String Search to protect from first vulnerability from 2018 OWASP TOP 10 for the IoT device. The hardcoded login information can be obtained by comparing the user input with strcmp or strncmp. Previous studies analyzed the symbols of strcmp or strncmp to detect the hardcoded login information. However, those studies required a lot of time because of the usage of complicated algorithms such as symbolic execution. To develop a lightweight algorithm, we focus on a network function, such as the socket symbol in firmware, because the IoT device is compromised when it is invaded by someone via the Internet. We propose two methods to detect the hardcoded login information: string search and socket search. In string search, the algorithm finds a function that uses the strcmp or strncmp symbol. In socket search, the algorithm finds a function that is referenced by the socket symbol. In this experiment, we measured the ability of our proposed method by searching six firmware in the real world that has a backdoor. We ran three methods: string search, socket search, and whole search to compare the two methods. As a result, all methods found login information from five of six firmware and one unexpected password. Our method reduces the analysis time. The whole search generally takes 38 mins to complete, but our methods finish the search in 4-6 min.


2016 ◽  
Vol 3 (1) ◽  
pp. 23-33
Author(s):  
Stevent Efendi ◽  
Alva Erwin ◽  
Kho I Eng

Social media has been a widespread phenomenon in the recent years. People shared a lot of thought in social media, and these data posted on the internet could be used for study and researches. As one of the fastest growing social network, Twitter is a particularly popular social media to be studied because it allows researchers to access their data. This research will look the correlation between Twitter chatter of a brand and the sales of brands in Indonesia. Factors such as sentiment and tweet rate are expected to be able to predict the popularity of a brand. Being one of the biggest industries in Indonesia, automotive industry is an interesting subject to study. A wide range of people buys vehicles, and even gather as communities based on their car or motorcycle brand preference. The Twitter results of sentiment analysis and tweet rate will be compared with real world sales results published by GAIKINDO and AISI.


Author(s):  
Cao Liu ◽  
Shizhu He ◽  
Kang Liu ◽  
Jun Zhao

By reason of being able to obtain natural language responses, natural answers are more favored in real-world Question Answering (QA) systems. Generative models learn to automatically generate natural answers from large-scale question answer pairs (QA-pairs). However, they are suffering from the uncontrollable and uneven quality of QA-pairs crawled from the Internet. To address this problem, we propose a curriculum learning based framework for natural answer generation (CL-NAG), which is able to take full advantage of the valuable learning data from a noisy and uneven-quality corpus. Specifically, we employ two practical measures to automatically measure the quality (complexity) of QA-pairs. Based on the measurements, CL-NAG firstly utilizes simple and low-quality QA-pairs to learn a basic model, and then gradually learns to produce better answers with richer contents and more complete syntaxes based on more complex and higher-quality QA-pairs. In this way, all valuable information in the noisy and uneven-quality corpus could be fully exploited. Experiments demonstrate that CL-NAG outperforms the state-of-the-arts, which increases 6.8% and 8.7% in the accuracy for simple and complex questions, respectively.


2021 ◽  
Author(s):  
Lyndsay Roach

The study of networks has been propelled by improvements in computing power, enabling our ability to mine and store large amounts of network data. Moreover, the ubiquity of the internet has afforded us access to records of interactions that have previously been invisible. We are now able to study complex networks with anywhere from hundreds to billions of nodes; however, it is difficult to visualize large networks in a meaningful way. We explore the process of visualizing real-world networks. We first discuss the properties of complex networks and the mechanisms used in the network visualizing software Gephi. Then we provide examples of voting, trade, and linguistic networks using data extracted from on-line sources. We investigate the impact of hidden community structures on the analysis of these real-world networks.


1997 ◽  
Vol 78 (3) ◽  
pp. 227-229
Author(s):  
N. V. Nemkova

In 1969, the U.S. created the ARPAnet computer network, linking the computer centers of the Department of Defense and several academic organizations. This network was designed for a narrow purpose: mainly to study how to communicate in the event of a nuclear attack and to help researchers exchange information. As this network grew, many other networks were created and developed.


Author(s):  
Jan Žižka ◽  
František Dařena

The automated categorization of unstructured textual documents according to their semantic contents plays important role particularly linked with the ever growing volume of such data originating from the Internet. Having a sufficient number of labeled examples, a suitable supervised machine learning-based classifier can be trained. When no labeling is available, an unsupervised learning method can be applied, however, the missing label information often leads to worse classification results. This chapter demonstrates a method based on semi-supervised learning when a smallish set of manually labeled examples improves the categorization process in comparison with clustering, and the results are comparable with the supervised learning output. For the illustration, a real-world dataset coming from the Internet is used as the input of the supervised, unsupervised, and semi-supervised learning. The results are shown for different number of the starting labeled samples used as “seeds” to automatically label the remaining volume of unlabeled items.


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