Real-Time Behavioral Biometric Information Security System for Assessment Fraud Detection

The online and blended delivery of courses through the use of technologies has attracted attention and research.The teaching and learning of English for Academic Purposes (EAP) in multilingual contexts such as in Africa has also been given prominence in research, as well as the benefits of technology in EAP programmes. The pressing need for technology - supported educational practices has been evidenced by the COVID-19 pandemic that affected most educational systems around the world. Although there is an increasing body of research on the integration of technology in education emerging from developing contexts, most proposed models still come from high-income countries.Therefore, there is a need for more critical and contextually relevant approaches to the integration of technology in education. Based on the authors’ narratives of their lived experiences as teachers of EAP in a university in East Africa, this article aims to describe and reflect on the practices related to design and delivery of online and blended courses to university students, including in a teacher education programme. It is hoped that this work will contribute to discussions on how to make online and blended teaching practices of EAP more contextually relevant and how exogenous resources can be adapted to the realities of students in multilingual developing contexts.The online and blended delivery of courses through the use of technologies has attracted attention and research. The teaching and learning of English for Academic Purposes (EAP) in multilingual contexts such as in Africa has also been given prominence in research, as well as the benefits of technology in EAP programmes. The pressing need for technology-supported educational practices has been evidenced by the COVID-19 pandemic that affected most educational systems around the world. Although there is an increasing body of research on the integration of technology in education emerging from developing contexts, most proposed models still come from high-income countries. Therefore, there is a need for more critical and contextually relevant approaches to the integration of technology in education. Based on the authors’ narratives of their lived experiences as teachers of EAP in a university in East Africa, this article aims to describe and reflect on the practices related to design and delivery of online and blended courses to university students, including in a teacher education programme. It is hoped that this work will contribute to discussions on how to make online and blended teaching practices of EAP more contextually relevant and how exogenous resources can be adapted to the realities of students in multilingual developing contexts.

In this paper we study the influence of key-scheduling algorithms on the strength of blockciphers. We show that the key-scheduling algorithms of many blockciphers inherit obvious relationships between keys, and use these key relations to attack the blockciphers. Two new types of attacks are described: New chosen plaintext reductions of the complexity of exhaustive search attacks (and the faster variants based on complementation properties), and new low-complexity chosen key attacks. These attacks are independent of the number of rounds of the cryptosystems and of the details of the F-function and may have very small complexities. These attacks show that the key-scheduling algorithm should be carefully designed and that its structure should not be too simple. These attacks are applicable to both variants of LOKI and to Lucifer. DES is not vulnerable to the related keys attacks since the shift pattern in the key-scheduling algorithm is not the same in all the rounds.

Information security awareness is important among users because it can influence user's behavior towards complying with organization information security policies. Therefore, the current study was conducted to examine information security awareness factors affecting user's compliance behavior towards Health Information System (HIS) security policies based on extended Health Belief Model. The questionnaires were distributed to the respondents at selected public hospitals in Malaysia (N = 454). Statistical results confirm that perceived work experience, perceived severity, perceived benefit, cues to action, self-efficacy and perceived barrier were significant predictors of health information system's security policies compliance behaviour while perceived susceptibility was insignificant. Our findings will prove to be beneficial to fellow researchers and policy makers, especially related to the medical sectors in improving user's behavior toward practicing information security.

Intrusion Detection Systems (IDS) is one of the important aspects of cyber security that can detect the anomalies in the network traffic. IDS are a part of Second defense line of a system that can be deployed along with other security measures such as access control, authentication mechanisms and encryption techniques to secure the systems against cyber-attacks. However, IDS suffers from the problem of handling large volume of data and in detecting zero-day attacks (new types of attacks) in a real-time traffic environment. To overcome this problem, an intelligent Deep Learning approach for Intrusion Detection is proposed based on Convolutional Neural Network (CNN-IDS). Initially, the model is trained and tested under a new real-time traffic dataset, CSE-CIC-IDS 2018 dataset. Then, the performance of CNN-IDS model is studied based on three important performance metrics namely, accuracy / training time, detection rate and false alarm rate. Finally, the experimental results are compared with those of various Deep Discriminative models including Recurrent Neural network (RNN), Deep Neural Network (DNN) etc., proposed for IDS under the same dataset. The Comparative results show that the proposed CNN-IDS model is very much suitable for modelling a classification model both in terms of binary and multi-class classification with higher detection rate, accuracy, and lower false alarm rate. The CNN-IDS model improves the accuracy of intrusion detection and provides a new research method for intrusion detection.

Automation of production processes of enterprise today has enormous scales. The spread of automated control systems of technological processes (ACSTP) and their integration with corporate information systems have led to increase in proportion of successfully implemented malicious impacts on ACSTP. Occurrence of new types of attacks and increase in number of successfully implemented attacks cause the need to form protection systems of industrial systems. Designing protection system of ACSTP is multi-stage process, starting with collection of data on object of protection, its main components, information assets, links with other systems, and identification of relevant information security threats to the system. In this article analysis of structure of typical automated control system of technological process is carried out. The most important components of ACSTP from the point of view of information security are identified. The article defines the need to form project of protection system of ACSTP on the basis of monitoring data of events that occur in ACSTP. Main classes of system events from the point of view of information security are defined. The most typical information security threats for this type of automated systems are considered. Neutralization of groups of threats relevant to ACSTP is carried out by means of forming effective protection system. The typical composition of data protection system of ACSTP is defined. According to results of analysis, the correspondence between security events of ACSTP, current threat groups and typical means of information protection is determined. Choosing the composition of information security tools is complex task that requires a lot of time. To achieve greater efficiency in its solving, it is proposed to use decision support system and system of artificial intelligence. Authors developed functional model of selection of protection system's project using intelligent method of decision support with neural network in it.

BeAware: Convolutional neural network(CNN) based user behavior understanding through WiFi channel state information

Intrusion detection system (IDS) using machine learning approach is getting popularity as it has an advantage of getting updated by itself to defend against any new type of attack. Another emerging technology, called internet of things (IoT) is taking the responsibility to make automated system by communicating the devices without human intervention. In IoT based systems, the wireless communication between several devices through the internet causes vulnerability for different security threats. This paper proposes a novel unified intrusion detection system for IoT environment (UIDS) to defend the network from four types of attacks such as: exploit, DoS, probe, and generic. The system is also able to detect normal category of network traffic. Most of the related works on IDS are based on KDD99 or NSL-KDD 99 data sets which are unable to detect new type of attacks. In this paper, UNSW-NB15 data set is considered as the benchmark dataset to design UIDS for detecting malicious activities in the network. The performance analysis proves that the attack detection rate of the proposed model is higher compared to two existing approaches ENADS and DENDRON which also worked on UNSW-NB15 data set.

Improvement of attack detection performance on the internet of things with PSO-search and random forest

The explosive growth of the Internet has led to an unprecedented increase in available information, creating a major challenge in identifying content that aligns with user interests. Recommendation systems (RS) address this challenge by providing personalized content recommendations based on user behavior and preferences. This article systematically reviews the evolution of recommendation systems with a focus on the methodologies of machine learning (ML) and deep learning (DL). It highlights core technologies in ML-based RS, such as content-based recommendation, collaborative filtering (CF), and hybrid filtering (HF). The article evaluates the effectiveness of various DL models, including autoencoders (AE), convolutional neural networks (CNN), and recurrent neural networks (RNN). The review also addresses key challenges faced by RS, such as cold start issues and the need for improved model transparency and interpretability. DL methods are shown to significantly enhance recommendation accuracy by leveraging complex patterns in large-scale data, to improve the user experience. Future research directions include refining data preprocessing, enhancing feature engineering, compressing and accelerating DL models, and improving interpretability through advanced mechanisms. This study provides valuable insights and references for researchers aiming to advance recommendation system design and performance optimization.

Automatic speaker verification (ASV) is a voice biometric technology whose security might be compromised by spoofing attacks. To increase the robustness against spoofing attacks, presentation attack detection (PAD) or anti-spoofing systems for detecting replay, text-to-speech and voice conversion-based spoofing attacks are being developed. However, it was recently shown that adversarial spoofing attacks may seriously fool anti-spoofing systems. Moreover, the robustness of the whole biometric system (ASV + PAD) against this new type of attack is completely unexplored. In this work, a new generative adversarial network for biometric anti-spoofing (GANBA) is proposed. GANBA has a twofold basis: (1) it jointly employs the anti-spoofing and ASV losses to yield very damaging adversarial spoofing attacks, and (2) it trains the PAD as a discriminator in order to make them more robust against these types of adversarial attacks. The proposed system is able to generate adversarial spoofing attacks which can fool the complete voice biometric system. Then, the resulting PAD discriminators of the proposed GANBA can be used as a defense technique for detecting both original and adversarial spoofing attacks. The physical access (PA) and logical access (LA) scenarios of the ASVspoof 2019 database were employed to carry out the experiments. The experimental results show that the GANBA attacks are quite effective, outperforming other adversarial techniques when applied in white-box and black-box attack setups. In addition, the resulting PAD discriminators are more robust against both original and adversarial spoofing attacks.

Cyber attacks have become increasingly dangerous and quite common in world of internet.so the demand of cyber security has been increased to safeguard the information personal data and computer network .now a days everything has been computerized and transaction are done online thus economic reimbursement are increasing due to cyber attacks as the technological evolution comes the progress of cyber-crime increases thus develops the new types of attacks are reported throughout the study the exact number of attack type determination is quite difficult however the most common cyber attacks are described in this article .the aim of this study is to survey and convincing manner review the strength and weakness presented in the field of cyber security. the article aim is to analyze various type of attacks existing to understand the state of being exposed to the possibility of being attacked. The main goal is to handle cyber attacks.

Ind-privacy and unp-privacy, later refined to unp*-privacy, are two different classes of privacy models for RFID authentication protocols. These models have captured the major anonymity and untraceability related attacks regarding RFID authentication protocols with privacy, and existing work indicates that unp*-privacy seems to be a stronger notion when compared with ind-privacy. In this paper, we continue studying the RFID privacy models, and there are two folds regarding our results. First of all, we describe a new traceability attack and show that schemes proven secure in unp*-privacy may not be secure against this new and practical type of traceability attacks. We then propose a new unpredictability-based privacy model to capture this new type of attacks. Secondly, we show that this new model, where we called it the unp τ -privacy, is stronger than both unp*-privacy and ind-privacy.

The emergence of new sophisticated types of attacks forces the community of computer security researchers to constantly improve detection tools and response methods. The present study explores different factors of autoencoders and variational models that influence their effectiveness in identifying novel attack types and malicious network traffic. The general idea of the proposed algorithm is to construct a confidence interval for the reconstruction error of the training sample, based on which a decision is made on the maliciousness of a particular traffic. Additional emphasis was placed on selecting an appropriate error metric to minimize the overlap between the density distributions of reconstruction errors for normal and malicious traffic. In the study of the variational model, the effect of the t-distribution on the quality of detecting new types of attacks was investigated. The studies were conducted on the CIC-IDS2017 dataset of the Canadian Cybersecurity Institute, containing up to 14 types of traffic and attacks. The experimental results show that with a competent selection of the error measure and the threshold values of the confidence interval, our models outperform existing analogues in various performance indicators.

One of the crucial sectors of Indian livelihood is agriculture. The development and stability of the country have been vastly backed by the farming sector. Thus, ultramodern inventions and technologies can support the testing of new approaches and practices in the farming sector. Artificial intelligence (AI) is one of the most important, useful, and current technologies. Due to its capacity to acquire dependable interpretations from images, Deep learning (DL) in particular has numerous uses. The most common DL architecture for image categorization is Convolutional Neural Networks (CNN). Classifying fruits and vegetables using deep learning is the main emphasis of this work. The model is also compared to some ML classifiers like Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Decision Tree (DT) whereas ResNet the Pretrained Model, Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP) in deep literacy. It was derived that SVM and MLP both showed an accuracy of97.36 on the fruit dataset.

Graph convolutional network – Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation