Microblog platforms have been extremely popular in the big data era due to its real-time diffusion of information. It's important to know what anomalous events are trending on the social network and ...be able to monitor their evolution and find related anomalies. In this paper we demonstrate RING, a real-time emerging anomaly monitoring system over microblog text streams. RING integrates our efforts on both emerging anomaly monitoring research and system research. From the anomaly monitoring perspective, RING proposes a graph analytic approach such that (1) RING is able to detect emerging anomalies at an earlier stage compared to the existing methods, (2) RING is among the first to discover emerging anomalies correlations in a streaming fashion, (3) RING is able to monitor anomaly evolutions in real-time at different time scales from minutes to months. From the system research perspective, RING (1) optimizes time-ranged keyword query performance of a full-text search engine to improve the efficiency of monitoring anomaly evolution, (2) improves the dynamic graph processing performance of Spark and implements our graph stream model on it, As a result, RING is able to process big data to the entire Weibo or Twitter text stream with linear horizontal scalability. The system clearly presents its advantages over existing systems and methods from both the event monitoring perspective and the system perspective for the emerging event monitoring task.
In this paper, we deal with the problem of similarity search about crowdedness for participatory-sensing buses for urban transportation. Similarity search is usually applied for measuring ...similarities in heterogeneous information networks. However, many models implement similarity search in a global setting, without taking object attributes into consideration. OCP, a novel OLAP-based crowdedness perception, is an attribute-enriched and meta-path-based model with machine learning to capture similarity based on the object connectivity, visibility and features. A set of common crowdedness attribute dimensions are defined across different types of objects, which can be obtained from the participatory passenger's sensor data through deep-neural-network-based posture recognition. Accordingly, an object can be described as a series of node vectors from different dimensions. In such framework, OLAP is applied in analysing multiple resolutions and improving efficiency of similarity search. In addition, our data sources are based on participatory-sensing instead of using vehicle GPS systems. As more data be collected through participatory-sensing, more accurate crowdedness for a bus can be estimated. The experiment results further demonstrate the efficiency of our analytical approaches.
The increasing demand for fast and accurate gait-impaired disease diagnosis requires a real-time prediction of gait information in order to enable online information access to determining the disease ...progression. In addition, the wearable sensor-based information acquisition meets the new trend of take-home healthcare, the access to the great amount of data enables applying data-driven methods in this scenario. In this paper, we propose to use wearable Electromyography (EMG) and inertial measurement unit (IMU) sensors to make an ahead-of-motion prediction of basic gait information, including lower-limb kinematics and kinetics. Particularly, a novel long short term memory (LSTM)-based algorithm is trained to extract features and continuously predict lower-limb angles. Based on the predicted kinematics, the kinetics of lower limbs are calculated by a dynamic model of human segments. EMG signals recorded from nine lower limb muscles and IMU signals from each lower-limb segment were collected for training the regressor. The experimental results with cross-validation among ten subjects have demonstrated the accuracy of the angle prediction and kinetics calculation. In addition, the optimal prediction time was exploited by testing the different sets of prediction time. The implication of this research work highlights the potential of continuous prediction of kinematics and kinetics, which provides fast and accurate access to basic gait information for smart healthcare applications.
•An ahead-of-time prediction of lower-limb kinematics and kinetics.•Exploring the influence of biological characteristics on algorithm.•A computationally efficient inverse dynamic model for calculating lower-limb kinetics without employing pressure insoles.
With the rise of the Internet of Things, malicious attacks pose serious threats to the massive vulnerable IoT devices. Recently, attackers have initiated increasingly coordinated attack activities ...commonly pertaining to botnets. However, how to effectively detect the botnet based on attacker activities is proven challenging. In this paper, we propose a Machine Learning-based method for modeling attacker activities based on the following intuitive observations: attackers in the same botnet tend to launch temporally close attacks. We then directly model attack temporal patterns using a special class of point process called Multivariate Hawkes Process. Intuitively, Multivariate Hawkes Process identifies the latent influences between attackers by utilizing the mutually exciting properties. We then cluster the attacker activities based on the inferred weighted influence matrix with resort to the graph-based clustering approach. To evaluate the applicability of our method, we deployed 10 honeypots in a real-world environment, and conduct experiments on the collected dataset. The results show that we can identify the activity pattern and the structure of botnets effectively.
Big data sources, such as smart vehicles, IoT devices, and sensor networks, differ from traditional data sources in both output volume and variety. Big data is usually stored on various types of ...network nodes, which is prone to data security and privacy problems, such as virus infection. In particular, the spread of viruses through social networks can cause large-scale destruction and privacy leakage in the network. This paper aims to provide a solution to protect the security of big data. First, the users are divided into five states according to their reactions to data virus: susceptible, contagious, doubt, immune, and recoverable. Then, we propose a novel model for studying the propagation mechanism of data virus. To control the spread of virus and protect data security, an incentive mechanism is introduced. After that, a protection and recovery strategy (PRS) is developed to reduce infected users and increase the immunized. The experimental results on two data sets indicate that our model gives a good description of the data virus propagation process, and PRS is better than both acquaintance immunization and target immunization methods, which validates the privacy preserving strategy for big data in networks.
Recent advancements in acoustic sensing and electronics technologies offer enormous opportunities for engineers to explore underwater and underground realms and deploy a diverse range of applications ...including deep sea natural resource extraction, search mission, and man-made asset monitoring. As a result, acoustic sensor networks (ASNs) are gaining increasing attention in research community. In addition, multimodal sensors incorporating acoustic sensing broaden the sensing scope, enabling the development of more useful and robust Internet of Things (IoT) applications. However, these networks are distinctively different from the traditional wireless sensor networks due to their inherent characteristics, and present a number of challenges to build low complexity and energy-efficient protocols for reliable data delivery and management of large-scale innovative IoT applications while ensuring the security and privacy of data communication. The papers of this special issue address a number of research challenges in applying acoustic sensors in IoT applications, including routing and data delivery protocols, energy harvesting, sensor management in large applications, cryptographic key distribution, and low-cost, high accuracy identification using sonar data.
Internet of Things (IoT) is one of the rapidly developing technologies today that attract huge real-world applications. However, the reality is that IoT is easily vulnerable to numerous types of ...cyberattacks and anomalies. Detecting them is becoming increasingly challenging day by day due to limitations with IoT devices and threat intelligence. Particularly, one of the most challenging problems is to detect the existence of malicious adversaries that continuously adapt or conceal their behaviors in IoT to hide their actions and to make the IoT security protocol ineffective. In this article, we study this problem at the IoT device level that can be a great idea to avoid potential attacks. We present AntiConcealer , an edge-aided IoT framework, and propose an edge artificial intelligence-enabled approach (EdgeAI) for detecting adversary concealed behaviors in the IoT. We first develop an adversary behavior model and use this to identify mid-attack temporal patterns by learning the multivariate Hawkes process (MHP), a kind of point process as a random and finite series of events (e.g., behaviors) controlled by a probabilistic model. Naturally, learning MHP processed on EdgeAI reveals the influence of the concealed behaviors of adversaries in the IoT. These concealed behaviors are then grouped using a nonnegative weighted influence matrix. To observe the performance of the AntiConcealer framework through evaluation, we employ honeypots integrated with edge servers and verify the usability and reliability of adversary behavioral identification.
Term representation methods as computable and semantic tools have been widely applied in social network analysis. This paper provides a new perspective that can incrementally factorize co-occurrence ...matrix to query latest semantic vectors. We divide the streaming social network data into old and updated training tasks respectively, and factorize the training objective function based on stochastic gradient methods to update vectors. We prove that the incremental objective function is convergent. Experimental results demonstrate that our incremental factorizing can save a substantial amount of time by speeding up training convergence. The smaller the updated data is, the faster the update factorizing process can be, even 30 times faster than existing methods in certain cases. To evaluate the correctness of incremental representation, social text similarity/relatedness, linguistic tasks, network event detection, social user multi-label classification and user clustering for social network analysis are employed as benchmarks in this paper.
•An incremental matrix factorization model designed for term representation.•An incremental term representation learning method for social network analysis.•The model convergence is proved based on stochastic gradient method.•Experiments conducted on word similarity, label classification and user clustering.
Nowadays, the Internet of Things (IoT) and cloud computing have become more pervasive in the context of the industry as digitization becomes a business priority for various organizations. Therefore, ...industries outsource their crowdsourced Industrial IoT (IIoT) data in the cloud in order to reduce the cost for sharing data and computation. However, the privacy of such crowdsourced data in this environment has attracted wide attention across the globe. Signcryption is the significant cryptographic primitive that meets both requirement of authenticity and confidentiality of crowdsourced data among users/industries, and thus, it is ideal for ensuring secure authentic data storage and transmission in industrial crowdsourcing environments. In this paper, we introduce a new identity-based signcryption (IBSC) scheme using bilinear pairing for IIoT deployment. Besides, two hard problems are studied, called as, modified bilinear Diffie-Hellman inversion (MBDHI) assumption and modified bilinear strong Diffie-Hellman (MBSDH) assumption. The rigorous security analysis demonstrates that our IBSC scheme for IIoT is provably secure based on the intractability of decisional-MBDHI and MBSDH assumptions under formal security model without considering the concept of the random oracle. The performance comparison with other signcryption schemes shows satisfactory results. Thus, our IBSC scheme is appropriate for IIoT crowdsourcing environments, and also applicable for low-bandwidth communications.
The widespread adoption of technology-enhanced learning in various knowledge disciplines has pushed forward the development of information technology-assisted media for language learning and ...teaching. However, most of the existing electronic-learning (e-learning) solutions have underexplored and under-addressed given specific characteristics of grammar learning, which is one of the most demanding areas of language education. The lack of pedagogically informed instructional design to enhance learning performance on the current system can result in low motivation and engagement due to an imbalance and excessive increase of the cognitive load. This paper attempts to address these deficiencies posed by the existing systems by proposing smart communication networks that are driven by the student learning experience to manage cognitive load in the context of grammar learning. The e-grammar learning networks serve as a collaborative learning platform that combines a pedagogically informed instructional model named attention, relevance, confidence, and satisfaction (ARCS) and cyber interaction among teaching/learning agents. From the technological perspective, our numerical simulations demonstrate the desirable performance indicators of the proposed networks to facilitate information exchange and learning. From the education perspective, our empirical studies show that the overall smart network-enabled e-grammar learning system has desirable characteristics to motivate learners (<inline-formula> <tex-math notation="LaTeX">m = 3.78 </tex-math></inline-formula>) and manage their overall cognitive load ( m = 1.73), which suggest the promising capability of the proposed system.
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