Search

Modern cloud services are monitored by numerous multidomain and multivendor monitoring tools, which generate massive numbers of alerts and events that are not actionable. These alerts usually carry isolated messages that are missing service contexts. Administrators become inundated with tickets caused by such alert events when they are routed directly to incident management systems. Noisy alerts increase the risk of crucial warnings going undetected and leading to service outages. One of the feasible ways to cope with the above problems involves revealing the correlations behind a large number of alerts and then aggregating the related alerts according to their correlations. Based on these guidelines, AlertInsight, a framework for alert event reduction, is proposed in this paper. In AlertInsight, the correlations among event sources are found by mining a sequence of historical events. Then, event correlation knowledge is employed to build an online detector targeting the correlated events that are hidden in the event stream. Finally, the correlated events are aggregated into a single high-level event for alert reduction. Because of theweaknesses of the commonly used pairwise correlation analysis methods in complex environments, an innovative approach for multiple correlation mining, which overcomes computational complexity challenges by scanning panoramic views of historical episodes from the perspective of holism, is proposed in this paper. In addition, a neural network-based correlated event detector that can learn the event correlation knowledge generated from correlation mining and then detect the correlated events in a sequence online is proposed. Experiments are conducted to test the effectiveness of AlertInsight. The experimental results (precision=0.92, recall=0.93, and F1-score=0.93) demonstrate the performance of AlertInsight for the recognition of multiple correlated alerts and its competence for alert reduction. [ABSTRACT FROM AUTHOR]

One of the elementary operations in computing systems is multiplication. Therefore, high-speed and low-power multipliers design is mandatory for efficient computing systems. In designing low-energy dissipation circuits, reversible logic is more efficient than irreversible logic circuits but at the cost of higher complexity. This paper introduces an efficient signed/unsigned 4 × 4 reversible Vedic multiplier with minimum quantum cost. The Vedic multiplier is considered fast as it generates all partial product and their sum in one step. This paper proposes two reversible Vedic multipliers with optimized quantum cost and garbage output. First, the unsigned Vedic multiplier is designed based on the Urdhava Tiryakbhyam (UT) Sutra. This multiplier consists of bitwise multiplication and adder compressors. Compared with Vedic multipliers in the literature, the proposed design has a quantum cost of 111 with a reduction of 94% compared to the previous design. It has a garbage output of 30 with optimization of the best-compared design. Second, the proposed unsigned multiplier is expanded to allow the multiplication of signed numbers as well as unsigned numbers. Two signed Vedic multipliers are presented with the aim of obtaining more optimization in performance parameters. DesignI has separate binary two's complement (B2C) and MUX circuits, while DesignII combines binary two's complement and MUX circuits in one circuit. DesignI shows the lowest quantum cost, 231, regarding state-of-the-art. DesignII has a quantum cost of 199, reducing to 86.14% of DesignI. The functionality of the proposed multiplier is simulated and verified using XILINX ISE 14.2. [ABSTRACT FROM AUTHOR]

Most user authentication mechanisms of cloud systems depend on the credentials approach in which a user submits his/her identity through a username and password. Unfortunately, this approach has many security problems because personal data can be stolen or recognized by hackers. This paper aims to present a cloud-based biometric authentication model (CBioAM) for improving and securing cloud services. The research study presents the verification and identification processes of the proposed cloud-based biometric authentication system (CBioAS), where the biometric samples of users are saved in database servers and the authentication process is implemented without loss of the users' information. The paper presents the performance evaluation of the proposed model in terms of three main characteristics including accuracy, sensitivity, and specificity. The research study introduces a novel algorithm called "Bio_Authen_as_a_Service" for implementing and evaluating the proposed model. The proposed system performs the biometric authentication process securely and preserves the privacy of user information. The experimental result was highly promising for securing cloud services using the proposed model. The experiments showed encouraging results with a performance average of 93.94%, an accuracy average of 96.15%, a sensitivity average of 87.69%, and a specificity average of 97.99%. [ABSTRACT FROM AUTHOR]

With the growth of the discipline of digital communication, the topic has acquiredmore attention in the cybersecuritymedium. The Intrusion Detection (ID) system monitors network traffic to detect malicious activities. The paper introduces a novel Feature Selection (FS) approach for ID. Reptile Search Algorithm (RSA)--is a new optimization algorithm; in this method, each agent searches a new region according to the position of the host, which makes the algorithm suffers from getting stuck in local optima and a slow convergence rate. To overcome these problems, this study introduces an improved RSA approach by integrating Cauchy Mutation (CM) into the RSA's structure. Thus, the CM can effectively expand search space and enhance the performance of the RSA. The developed RSA-CM is assessed on five publicly available ID datasets: KDD-CUP99, NSL-KDD, UNSW-NB15, CIC-IDS2017, and CIC-IDS2018 and two engineering problems. The RSA-CM is compared with the original RSA, and three other state-of-the-art FS methods, namely particle swarm optimization, grey wolf optimization, and multi-verse optimizer, and quantitatively is evaluated using fitness value, the number of selected optimum features, accuracy, precision, recall, and F1- score evaluationmeasures. The results reveal that the developed RSA-CMgot better results than the other competitive methods applied for FS on the ID datasets and the examined engineering problems. Moreover, the Friedman test results confirm that RSA-CMhas a significant superiority compared to other methods as an FS method for ID. [ABSTRACT FROM AUTHOR]

The healthcare IoT system is considered to be a significant and modern medical system. There is broad consensus that these systems will play a vital role in the achievement of economic growth in numerous growth countries. Among the major challenges preventing the fast and widespread adoption of such systems is the failure to maintain the data privacy of patients and the integrity of remote clinical diagnostics. Recently, the author proposed an end-to-end authentication scheme for healthcare IoT systems (E2EA), to provide a mutual authentication with a high data rate between the communication nodes of the healthcare IoT systems. Although the E2EA authentication scheme supports numerous attractive security services to resist various types of attack, there is an ambiguous view of the impact of the desynchronization attack on the E2EA authentication scheme. In general, the performance of the authentication scheme is considered a critical issue when evaluating the applicability of such schemes, along with the security services that can be achieved. Therefore, this paper discusses how the E2EA authentication scheme can resist the desynchronization attack through all possible attack scenarios. Additionally, the effect of the desynchronization attack on the E2EA scheme performance is analyzed in terms of its computation and communication costs, based on a comparison with the recently related authentication schemes that can prevent such attack. Moreover, this research paper finds that the E2EA authentication scheme can not only prevent the desynchronization attack, but also offers a low cost in terms of computations and communications, and can maintain consistency and synchronization between the communication nodes of the healthcare IoT systems during the next authentication sessions. [ABSTRACT FROM AUTHOR]

In recent years, blockchain technology has been applied in the educational domain because of its salient advantages, i.e., transparency, decentralization, and immutability. Available systems typically use public blockchain networks such as Ethereum and Bitcoin to store learning results. However, the cost of writing data on these networks is significant, making educational institutions limit data sent to the target network, typically containing only hash codes of the issued certificates. In this paper, we present a system based on a private blockchain network for lifelong learning data authentication and management named B4E (Blockchain For Education). B4E stores not only certificates but also learners' training data such as transcripts and educational programs in order to create a complete record of the lifelong education of each user and verify certificates that they have obtained. As a result, B4E can address two types of fake certificates, i.e., certificates printed by unlawful organizations and certificates issued by educational institutions for learners who have not met the training requirements. In addition, B4E is designed to allow all participants to easily deploy software packages to manage, share, and check stored information without depending on a single point of access. As such, the system enhances the transparency and reliability of the stored data. Our experiments show that B4E meets expectations for deployment in reality. [ABSTRACT FROM AUTHOR]

Mental workload is considered to be strongly linked to human performance, and the ability to measure it accurately is key for balancing human health and work. In this study, brain signals were elicited by mental arithmetic tasks of varying difficulty to stimulate different levels of mental workload. In addition, a finite impulse response (FIR) filter, independent component analysis (ICA), and multiple artifact rejection algorithms (MARAs) were used to filter event-related potentials (ERPs). Then, the data consisting of ERPs, subjective ratings of mental workload, and task performance, were analyzed through the use of variance and Spearman's correlation during a simulated computer task. We found that participants responded faster and performed better in the easy task condition, followed by the medium and high-difficulty conditions, which verifies the validity of the ERP filtering. Moreover, larger P2 and P3 waveforms were evoked as the task difficulty increased, and a higher task difficulty elicited a more enhanced N300. Correlation analysis revealed a negative relationship between the amplitude of P3 and the subjective ratings, and a positive relationship between the P3 amplitude and accuracy. The results presented in this paper demonstrate that a combination of FIR, ICA, and MARA methods can filter ERPs in the non-invasive real-time measurement of workload. Additionally, frontocentral P2, N3, and parietal P3 components showed differences between genders. The proposed measurement of mental workload can be useful for real-time identification of mental states and can be applied to human-computer interaction in the future. [ABSTRACT FROM AUTHOR]

The primary goal of cloth simulation is to express object behavior in a realistic manner and achieve real-time performance by following the fundamental concept of physic. In general, the mass-spring system is applied to real-time cloth simulation with three types of springs. However, hard spring cloth simulation using the mass-spring system requires a small integration time-step in order to use a large stiffness coefficient. Furthermore, to obtain stable behavior, constraint enforcement is used instead of maintenance of the force of each spring. Constraint force computation involves a large sparse linear solving operation. Due to the large computation, we implement a cloth simulation using adaptive constraint activation and deactivation techniques that involve the mass-spring system and constraint enforcement method to prevent excessive elongation of cloth. At the same time, when the length of the spring is stretched or compressed over a defined threshold, adaptive constraint activation and deactivation method deactivates the spring and generate the implicit constraint. Traditional method that uses a serial process of the Central Processing Unit (CPU) to solve the system in every frame cannot handle the complex structure of cloth model in real-time. Our simulation utilizes the Graphic Processing Unit (GPU) parallel processing with compute shader in OpenGL Shading Language (GLSL) to solve the system effectively. In this paper, we design and implement parallel method for cloth simulation, and experiment on the performance and behavior comparison of the mass-spring system, constraint enforcement, and adaptive constraint activation and deactivation techniques the using GPU-based parallel method. [ABSTRACT FROM AUTHOR]

The subtitle recognition under multimodal data fusion in this paper aims to recognize text lines from image and audio data. Most existing multimodal fusion methods tend to be associated with pre-fusion as well as post-fusion, which is not reasonable and difficult to interpret. We believe that fusing images and audio before the decision layer, i.e., intermediate fusion, to take advantage of the complementary multimodal data, will benefit text line recognition. To this end, we propose: (i) a novel cyclic autoencoder based on convolutional neural network. The feature dimensions of the two modal data are aligned under the premise of stabilizing the compressed image features, thus the high-dimensional features of different modal data are fused at the shallow level of the model. (ii) A residual attention mechanism that helps us improve the performance of the recognition. Regions of interest in the image are enhanced and regions of disinterest are weakened, thus we can extract the features of the text regions without further increasing the depth of the model (iii) a fully convolutional network for video subtitle recognition. We choose DenseNet-121 as the backbone network for feature extraction, which effectively enabling the recognition of video subtitles in complex backgrounds. The experiments are performed on our custom datasets, and the automatic and manual evaluation results show that our method reaches the state-of-the-art. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) is a pandemic disease that is currently affecting over 200 countries around the world and impacting billions of people. The first step to mitigate and control its spread is to identify and isolate the infected people. But, because of the lack of reverse transcription polymerase chain reaction (RT-CPR) tests, it is important to discover suspected COVID-19 cases as early as possible, such as by scan analysis and chest X-ray by radiologists. However, chest X-ray analysis is relatively time-consuming since it requires more than 15 minutes per case. In this paper, an automated novel detection model of COVID-19 cases is proposed to perform real-time detection of COVID-19 cases. The proposed model consists of three main stages: image segmentation using Harris Hawks optimizer, synthetic image augmentation using an enhanced Wasserstein And Auxiliary Classifier Generative Adversarial Network, and image classification using Conventional Neural Network. Raw chest X-ray images datasets are used to train and test the proposed model. Experiments demonstrate that the proposed model is very efficient in the automatic detection of COVID-19 positive cases. It achieved 99.4% accuracy, 99.15% precision, 99.35% recall, 99.25% F-measure, and 98.5% specificity. [ABSTRACT FROM AUTHOR]