Biblio

The major mode of communication between hearing-impaired or mute people and others is sign language. Prior, most of the recognition systems for sign language had been set simply to recognize hand signs and convey them as text. However, the proposed model tries to provide speech to the mute. Firstly, hand gestures for sign language recognition and facial emotions are trained using CNN (Convolutional Neural Network) and then by training the emotion to speech model. Finally combining hand gestures and facial emotions to realize the emotion and speech.

In this modern era, web security is often required to beware from fraudulent activities. There are several hackers try to build a program that can interact with web pages automatically and try to breach the data or make several junk entries due to that web servers get hanged. To stop the junk entries; CAPTCHA is a solution through which bots can be identified and denied the machine based program to intervene with. CAPTCHA stands for Completely Automated Public Turing test to tell Computers and Humans Apart. In the progression of CAPTCHA; there are several methods available such as distorted text, picture recognition, math solving and gaming based CAPTCHA. Game based turing test is very much popular now a day but there are several methods through which game can be cracked because game is not intellectual. So, there is a required of intrinsic CAPTCHA. The proposed system is based on Intrinsic Decision based Situation Reaction Challenge. The proposed system is able to better classify the humans and bots by its intrinsic problem. It has been considered as human is more capable to deal with the real life problems and machine is bit poor to understand the situation or how the problem can be solved. So, proposed system challenges with simple situations which is easier for human but almost impossible for bots. Human is required to use his common sense only and problem can be solved with few seconds.

The internet has grown increasingly important in everyone's everyday lives due to the availability of numerous web services such as email, cloud storage, video streaming, music streaming, and search engines. On the other hand, attacks by computer programmes such as bots are a common hazard to these internet services. Captcha is a computer program that helps a server-side company determine whether or not a real user is requesting access. Captcha is a security feature that prevents unauthorised access to a user's account by protecting restricted areas from automated programmes, bots, or hackers. Many websites utilise Captcha to prevent spam and other hazardous assaults when visitors log in. However, in recent years, the complexity of Captcha solving has become difficult for humans too, making it less user friendly. To solve this, we propose creating a Captcha that is both simple and engaging for people while also robust enough to protect sensitive data from bots and hackers on the internet. The suggested captcha scheme employs animated artifacts, rotation, and variable fonts as resistance techniques. The proposed captcha technique proves successful against OCR bots with less than 15% accuracy while being easier to solve for human users with more than 98% accuracy.

With the increase of the number of network threats, the knowledge graph is an effective method to quickly analyze the network threats from the mass of network security texts. Named entity recognition in network security domain is an important task to construct knowledge graph. Aiming at the problem that key Chinese entity information in network security related text is difficult to identify, a named entity recognition model in network security domain based on BERT-BiLSTM-CRF is proposed to identify key named entities in network security related text. This model adopts the BERT pre-training model to obtain the word vectors of the preceding and subsequent text information, and the obtained word vectors will be input to the subsequent BiLSTM module and CRF module for encoding and sorting. The test results show that this model has a good effect on the data set of network security domain. The recognition effect of this model is better than that of LSTM-CRF, BERT-LSTM-CRF, BERT-CRF and other models, and the F1=93.81%.

As the mobile Internet is developing rapidly, people who use cell phones to access the Internet dominate, and the mobile Internet has changed the development environment of online public opinion and made online public opinion events spread more widely. In the online environment, any kind of public issues may become a trigger for the generation of public opinion and thus need to be controlled for network supervision. The method in this paper can identify entities from the event texts obtained from mobile Today's Headlines, People's Daily, etc., and informatize security of public opinion in event instances, thus strengthening network supervision and control in mobile, and providing sufficient support for national security event management. In this paper, we present a SW-BiLSTM-CRF model, as well as a model combining the RoBERTa pre-trained model with the classical neural network BiLSTM model. Our experiments show that this approach provided achieves quite good results on Chinese emergency corpus, with accuracy and F1 values of 87.21% and 78.78%, respectively.

Easy Data Augmentation is originally developed for text classification tasks. It consists of four basic methods: Synonym Replacement, Random Insertion, Random Deletion, and Random Swap. They yield accuracy improvements on several deep neural network models. In this study we apply these methods to a new domain. We augment Named Entity Recognition datasets from medical domain. Although the augmentation task is much more difficult due to the nature of named entities which consist of word or word groups in the sentences, we show that we can improve the named entity recognition performance.

In this information age, web crawling on the internet is a prime source for data collection. And with the surface web already being dominated by giants like Google and Microsoft, much attention has been on the Dark Web. While research on crawling approaches is generally available, a considerable gap is present for URL extraction on the dark web. With most literature using the regular expressions methodology or built-in parsers, the problem with these methods is the higher number of false positives generated with the Dark Web, which makes the crawler less efficient. This paper proposes the dedicated parsers methodology for extracting URLs from the dark web, which when compared proves to be better than the regular expression methodology. Factors that make link harvesting on the Dark Web a challenge are discussed in the paper.

Effective identification of Internet of things devices in cyberspace is of great significance to the protection of Cyberspace Security. However, there are a large number of such devices in cyberspace, which can not be identified by the existing methods of identifying IoT devices because of the lack of key information such as manufacturer name and device name in the response message. Their existence brings hidden danger to Cyberspace Security. In order to identify the IoT devices with missing key information in these response messages, this paper proposes an IoT device identification method, IoTCatcher. IoTCatcher uses HTTP response message and the structure and style characteristics of HTML document, and based on natural language processing technology and text similarity technology, classifies and compares the IoT devices whose response message lacks key information, so as to generate their device finger information. This paper proves that the recognition precision of IoTCatcher is 95.29%, and the recall rate is 91.01%. Compared with the existing methods, the overall performance is improved by 38.83%.

Engineering drawings such as Piping and Instrumentation Diagrams contain a vast amount of text data which is essential to identify shapes, pipeline activities, tags, amongst others. These diagrams are often stored in undigitised format, such as paper copy, meaning the information contained within the diagrams is not readily accessible to inspect and use for further data analytics. In this paper, we make use of the benefits of recent deep learning advances by selecting models for both text detection and text recognition, and apply them to the digitisation of text from within real world complex engineering diagrams. Results show that 90% of text strings were detected including vertical text strings, however certain non text diagram elements were detected as text. Text strings were obtained by the text recognition method for 86% of detected text instances. The findings show that whilst the chosen Deep Learning methods were able to detect and recognise text which occurred in simple scenarios, more complex representations of text including those text strings located in close proximity to other drawing elements were highlighted as a remaining challenge.

This article describes the principles of construction, training and use of neural networks. The features of the neural network approach are indicated, as well as the range of tasks for which it is most preferable. Algorithms of functioning, software implementation and results of work of an artificial neural network are presented.

Network security policies contain requirements - including system and software features as well as expected and desired actions of human actors. In this paper, we present a framework for evaluation of textual network security policies as requirements documents to identify areas for improvement. Specifically, our framework concentrates on completeness. We use topic modeling coupled with expert evaluation to learn the complete list of important topics that should be addressed in a network security policy. Using these topics as a checklist, we evaluate (students) a collection of network security policies for completeness, i.e., the level of presence of these topics in the text. We developed three methods for topic recognition to identify missing or poorly addressed topics. We examine network security policies and report the results of our analysis: preliminary success of our approach.

As the accessibility of high-resolution smartphone camera has increased and an improved computational speed, it is now convenient to build Business Card Readers on mobile phones. The project aims to design and develop a Business Card Reader (BCR) Application for iOS devices, using an open-source OCR Engine - Tesseract. The system accuracy was tested and evaluated using a dataset of 55 digital business cards obtained from an online repository. The accuracy result of the system was up to 74% in terms of both text recognition and data detection. A comparative analysis was carried out against a commercial business card reader application and our application performed vastly reasonable.

The majority of applications use a prompt for a username and password. Passwords are recommended to be unique, long, complex, alphanumeric and non-repetitive. These reasons that make passwords secure may prove to be a point of weakness. The complexity of the password provides a challenge for a user and they may choose to record it. This compromises the security of the password and takes away its advantage. An alternate method of security is Keystroke Biometrics. This approach uses the natural typing pattern of a user for authentication. This paper proposes a new method for reducing error rates and creating a robust technique. The new method makes use of multiple sensors to obtain information about a user. An artificial neural network is used to model a user's behavior as well as for retraining the system. An alternate user verification mechanism is used in case a user is unable to match their typing pattern.

Arabic handwritten documents present specific challenges due to the cursive nature of the writing and the presence of diacritical marks. Moreover, one of the largest labeled database of Arabic handwritten documents, the OpenHart-NIST database includes specific noise, namely guidelines, that has to be addressed. We propose several approaches to process these documents. First a guideline detection approach has been developed, based on K-means, that detects the documents that include guidelines. We then propose a series of preprocessing at text-line level to reduce the noise effects. For text-lines including guidelines, a guideline removal preprocessing is described and existing keystroke restoration approaches are assessed. In addition, we propose a preprocessing that combines noise removal and deskewing by removing line fragments from neighboring text lines, while searching for the principal orientation of the text-line. We provide recognition results, showing the significant improvement brought by the proposed processings.