Biblio

Future that IoT has to enhance the productivity on healthcare applications.

Future that IoT has to enhance the productivity on healthcare applications.

Intelligent voice assistants (IVAs) and other voice-enabled devices already form an integral component of the Internet of Things and will continue to grow in popularity. As their capabilities evolve, they will move beyond relying on the wake-words today’s IVAs use, engaging instead in continuous listening. Though potentially useful, the continuous recording and analysis of speech can pose a serious threat to individuals’ privacy. Ideally, users would be able to limit or control the types of information such devices have access to. But existing technical approaches are insufficient for enforcing any such restrictions. To begin formulating a solution, we develop a system- atic methodology for studying continuous-listening applications and survey architectural approaches to designing a system that enhances privacy while preserving the benefits of always-listening assistants.

In recent trends, privacy preservation is the most predominant factor, on big data analytics and cloud computing. Every organization collects personal data from the users actively or passively. Publishing this data for research and other analytics without removing Personally Identifiable Information (PII) will lead to the privacy breach. Existing anonymization techniques are failing to maintain the balance between data privacy and data utility. In order to provide a trade-off between the privacy of the users and data utility, a Mondrian based k-anonymity approach is proposed. To protect the privacy of high-dimensional data Deep Neural Network (DNN) based framework is proposed. The experimental result shows that the proposed approach mitigates the information loss of the data without compromising privacy.

Software has become an essential component of modern life, but when software vulnerabilities threaten the security of users, new ways of analyzing for software security must be explored. Using the National Institute of Standards and Technology's Juliet Java Suite, containing thousands of examples of defective Java methods for a variety of vulnerabilities, a prototype tool was developed implementing an array of Long-Short Term Memory Recurrent Neural Networks to detect vulnerabilities within source code. The tool employs various data preparation methods to be independent of coding style and to automate the process of extracting methods, labeling data, and partitioning the dataset. The result is a prototype command-line utility that generates an n-dimensional vulnerability prediction vector. The experimental evaluation using 44,495 test cases indicates that the tool can achieve an accuracy higher than 90% for 24 out of 29 different types of CWE vulnerabilities.

Conversational systems are computer programs that interact with users using natural language. Considering the complexity and interaction of the different components involved in building intelligent conversational systems that can perform diverse tasks, a promising approach to facilitate their development is by using multiagent systems (MAS). This paper reviews the main concepts and history of conversational systems, and introduces an architecture based on MAS. This architecture was designed to support the development of conversational systems in the domain chosen by the developer while also providing a reusable built-in dialogue control. We present a practical application in the healthcare domain. We observed that it can help developers to create conversational systems in different domains while providing a reusable and centralized dialogue control. We also present derived lessons learned that can be helpful to steer future research on engineering domain-specific conversational systems.

In this paper, we develop a statistical framework for image steganography in which the cover and stego messages are modeled as multivariate Gaussian random variables. By minimizing the detection error of an optimal detector within the generalized adopted statistical model, we propose a novel Gaussian embedding method. Furthermore, we extend the formulation to cost-based steganography, resulting in a universal embedding scheme that works with embedding costs as well as variance estimators. Experimental results show that the proposed approach avoids embedding in smooth regions and significantly improves the security of the state-of-the-art methods, such as HILL, MiPOD, and S-UNIWARD.

Originally implemented by Google, QUIC gathers a growing interest by providing, on top of UDP, the same service as the classical TCP/TLS/HTTP/2 stack. The IETF will finalise the QUIC specification in 2019. A key feature of QUIC is that almost all its packets, including most of its headers, are fully encrypted. This prevents eavesdropping and interferences caused by middleboxes. Thanks to this feature and its clean design, QUIC is easier to extend than TCP. In this paper, we revisit the reliable transmission mechanisms that are included in QUIC. More specifically, we design, implement and evaluate Forward Erasure Correction (FEC) extensions to QUIC. These extensions are mainly intended for high-delays and lossy communications such as In-Flight Communications. Our design includes a generic FEC frame and our implementation supports the XOR, Reed-Solomon and Convolutional RLC error-correcting codes. We also conservatively avoid hindering the loss-based congestion signal by distinguishing the packets that have been received from the packets that have been recovered by the FEC. We evaluate its performance by applying an experimental design covering a wide range of delay and packet loss conditions with reproducible experiments. These confirm that our modular design allows the protocol to adapt to the network conditions. For long data transfers or when the loss rate and delay are small, the FEC overhead negatively impacts the download completion time. However, with high packet loss rates and long delays or smaller files, FEC allows drastically reducing the download completion time by avoiding costly retransmission timeouts. These results show that there is a need to use FEC adaptively to the network conditions.

The fast growing of ransomware attacks has become a serious threat for companies, governments and internet users, in recent years. The increasing of computing power, memory and etc. and the advance in cryptography has caused the complicating the ransomware attacks. Therefore, effective methods are required to deal with ransomwares. Although, there are many methods proposed for ransomware detection, but these methods are inefficient in detection ransomwares, and more researches are still required in this field. In this paper, we have proposed a novel method for identify ransomware from benign software using process mining methods. The proposed method uses process mining to discover the process model from the events logs, and then extracts features from this process model and using these features and classification algorithms to classify ransomwares. This paper shows that the use of classification algorithms along with the process mining can be suitable to identify ransomware. The accuracy and performance of our proposed method is evaluated using a study of 21 ransomware families and some benign samples. The results show j48 and random forest algorithms have the best accuracy in our method and can achieve to 95% accuracy in detecting ransomwares.

keystroke dynamics authenticates the system user by analyzing his typing rhythm. Given that each of us has his own typing rhythm and that the method is based on the keyboard makes it available in all computer machines, these two reasons (uniqueness and reduced cost) have made the method very solicit by administrators of security. In addition, the researchers used the method in different fields that are listed later in the paper.

We propose a new spam detection approach based solely on meta data features gained from email headers. The approach achieves above 99 % classification accuracy on the CSDMC2010 dataset, which matches or surpasses state-of-the-art spam classifiers. We utilize a static set of engineered features, supplemented with automatically extracted features. The approach is just as effective for spam detection in end-to-end encryption, as our feature set remains unchanged for encrypted emails. In contrast to most established spam detectors, we disregard the email body completely and can therefore deliver very high classification speeds, as computationally expensive text preprocessing is not necessary.

Untethered microrobots actuated by external magnetic fields have drawn extensive attention recently, due to their potential advantages in real-time tracking and targeted delivery in vivo. To control a swarm of microrobots with external fields, however, is still one of the major challenges in this field. In this work, we present new methods to generate ribbon-like and vortex-like microrobotic swarms using oscillating and rotating magnetic fields, respectively. Paramagnetic nanoparticles with a diameter of 400 nm serve as the agents. These two types of swarms exhibits out-of-equilibrium structure, in which the nanoparticles perform synchronised motions. By tuning the magnetic fields, the swarming patterns can be reversibly transformed. Moreover, by increasing the pitch angle of the applied fields, the swarms are capable of performing navigated locomotion with a controlled velocity. This work sheds light on a better understanding for microrobotic swarm behaviours and paves the way for potential biomedical applications.

For the task with complicated manipulation in unstructured environments, traditional hand-coded methods are ineffective, while reinforcement learning can provide more general and useful policy. Although the reinforcement learning is able to obtain impressive results, its stability and reliability is hard to guarantee, which would cause the potential safety threats. Besides, the transfer from simulation to real-world also will lead in unpredictable situations. To enhance the safety and reliability of robots, we introduce the force and haptic perception into reinforcement learning. Force and tactual sensation play key roles in robotic dynamic control and human-robot interaction. We demonstrate that the force-based reinforcement learning method can be more adaptive to environment, especially in sim-to-real transfer. Experimental results show in object pushing task, our strategy is safer and more efficient in both simulation and real world, thus it holds prospects for a wide variety of robotic applications.

The relative permittivity (also known as dielectric constant) is one of the physical properties that characterize a substance. The measurement of its magnitude can be useful in the analysis of several fluids, playing an important role in many industrial processes. This paper presents a method for measuring the relative permittivity of fluids, with the possibility of real-time monitoring. The method comprises the immersion of a capacitive sensor inside a tank or duct, in order to have the inspected substance as its dielectric. An electronic circuit is responsible for exciting this sensor, which will have its capacitance measured through a quick analysis of two analog signals outputted by the circuit. The developed capacitance meter presents a novel topology derived from the well-known Howland current source. One of its main advantages is the capacitance-selective behavior, which allows the system to overcome the effects of parasitic resistive and inductive elements on its readings. In addition to an adjustable current output that suits different impedance magnitudes, it exhibits a steady oscillating behavior, thus allowing continuous operation without any form of external control. This paper presents experimental results obtained from the proposed system and compares them to measurements made with proven and calibrated equipment. Two initial capacitance measurements performed with the system for evaluating the sensor's characteristics exhibited relative errors of approximately 0.07% and 0.53% in comparison to an accurate workbench LCR meter.

Research on keystroke dynamics has the good potential to offer continuous authentication that complements conventional authentication methods in combating insider threats and identity theft before more harm can be done to the genuine users. Unfortunately, the large amount of data required by free-text keystroke authentication often contain personally identifiable information, or PII, and personally sensitive information, such as a user's first name and last name, username and password for an account, bank card numbers, and social security numbers. As a result, there are privacy risks associated with keystroke data that must be mitigated before they are shared with other researchers. We conduct a systematic study to remove PII's from a recent large keystroke dataset. We find substantial amounts of PII's from the dataset, including names, usernames and passwords, social security numbers, and bank card numbers, which, if leaked, may lead to various harms to the user, including personal embarrassment, blackmails, financial loss, and identity theft. We thoroughly evaluate the effectiveness of our detection program for each kind of PII. We demonstrate that our PII detection program can achieve near perfect recall at the expense of losing some useful information (lower precision). Finally, we demonstrate that the removal of PII's from the original dataset has only negligible impact on the detection error tradeoff of the free-text authentication algorithm by Gunetti and Picardi. We hope that this experience report will be useful in informing the design of privacy removal in future keystroke dynamics based user authentication systems.

The recent success of brain-inspired deep neural networks (DNNs) in solving complex, high-level visual tasks has led to rising expectations for their potential to match the human visual system. However, DNNs exhibit idiosyncrasies that suggest their visual representation and processing might be substantially different from human vision. One limitation of DNNs is that they are vulnerable to adversarial examples, input images on which subtle, carefully designed noises are added to fool a machine classifier. The robustness of the human visual system against adversarial examples is potentially of great importance as it could uncover a key mechanistic feature that machine vision is yet to incorporate. In this study, we compare the visual representations of white- and black-box adversarial examples in DNNs and humans by leveraging functional magnetic resonance imaging (fMRI). We find a small but significant difference in representation patterns for different (i.e. white- versus black-box) types of adversarial examples for both humans and DNNs. However, human performance on categorical judgment is not degraded by noise regardless of the type unlike DNN. These results suggest that adversarial examples may be differentially represented in the human visual system, but unable to affect the perceptual experience.

Before accessing Internet websites or applications, network users first ask the Domain Name System (DNS) for the corresponding IP address, and then the user's browser or application accesses the required resources through the IP address. The server log of DNS keeps records of all users' requesting queries. This paper analyzes the user network accessing behavior by analyzing network DNS log in campus, constructing a behavior fingerprint model for each user. Different users and even same user's fingerprints in different periods can be used to determine whether the user's access is abnormal or safe, whether it is infected with malicious code. After detecting the behavior of abnormal user accessing, preventing the spread of viruses, Trojans, bots and attacks is made possible, which further assists the protection of users' network access security through corresponding techniques. Finally, analysis of user behavior fingerprints of campus network access is conducted.

In order to improve the information security level of intelligent substation, this paper proposes an intelligent substation information security assessment tool through the research and analysis of intelligent substation information security risk and information security assessment method, and proves that the tool can effectively detect it. It is of great significance to carry out research on industrial control systems, especially intelligent substation information security.

This paper studies and describes encrypted communication between IoT cloud and IoT embedded systems. It uses encrypted MQTTS protocol with SSL/TLS certificate. A JSON type data format is used between the cloud structure and the IoT device. The embedded system used in this experiment is Esp32 Wrover. The IoT embedded system measures temperature and humidity from a sensor DHT22. The architecture and software implementation of the experimental stage are also presented.

The difficult of detecting, response, tracing the malicious behavior in cloud has brought great challenges to the law enforcement in combating cybercrimes. This paper presents a malicious behavior oriented framework of detection, emergency response, traceability, and digital forensics in cloud environment. A cloud-based malicious behavior detection mechanism based on SDN is constructed, which implements full-traffic flow detection technology and malicious virtual machine detection based on memory analysis. The emergency response and traceability module can clarify the types of the malicious behavior and the impacts of the events, and locate the source of the event. The key nodes and paths of the infection topology or propagation path of the malicious behavior will be located security measure will be dispatched timely. The proposed IaaS service based forensics module realized the virtualization facility memory evidence extraction and analysis techniques, which can solve volatile data loss problems that often happened in traditional forensic methods.

Person re-identification(Person Re-ID) means that images of a pedestrian from cameras in a surveillance camera network can be automatically retrieved based on one of this pedestrian's image from another camera. The appearance change of pedestrians under different cameras poses a huge challenge to person re-identification. Person re-identification systems based on deep learning can effectively extract the appearance features of pedestrians. In this paper, the feature enhancement experiment is conducted, and the result showed that the current person reidentification datasets are relatively small and cannot fully meet the need of deep training. Therefore, this paper studied the method of using generative adversarial network to extend the person re-identification datasets and proposed a label smoothing regularization for outliers with weight (LSROW) algorithm to make full use of the generated data, effectively improved the accuracy of person re-identification.

In transient distributed cloud computing environment, software is vulnerable to attack, which leads to software functional completeness, so it is necessary to carry out functional testing. In order to solve the problem of high overhead and high complexity of unsupervised test methods, an intelligent evaluation method for transient analysis software function testing based on active depth learning algorithm is proposed. Firstly, the active deep learning mathematical model of transient analysis software function test is constructed by using association rule mining method, and the correlation dimension characteristics of software function failure are analyzed. Then the reliability of the software is measured by the spectral density distribution method of software functional completeness. The intelligent evaluation model of transient analysis software function testing is established in the transient distributed cloud computing environment, and the function testing and reliability intelligent evaluation are realized. Finally, the performance of the transient analysis software is verified by the simulation experiment. The results show that the accuracy of the software functional integrity positioning is high and the intelligent evaluation of the transient analysis software function testing has a good self-adaptability by using this method to carry out the function test of the transient analysis software. It ensures the safe and reliable operation of the software.

With the rapid development of the contemporary society, wide use of smart phone and vehicle sensing devices brings a huge influence on the extensive data collection. Network coding can only provide weak security privacy protection. Aiming at weak secure feature of network coding, this paper proposes an information transfer mechanism, Weak Security Network Coding with Homomorphic Encryption (HE-WSNC), and it is integrated into routing policy. In this mechanism, a movement model is designed, which allows information transmission process under Wi-Fi and Bluetooth environment rather than consuming 4G data flow. Not only does this application reduce the cost, but also improve reliability of data transmission. Moreover, it attracts more users to participate.

SSL certificates are a core component of the public key infrastructure that underpins encrypted communication in the Internet. In this paper, we report the results of a longitudinal study of the characteristics of SSL certificate chains presented to clients during secure web (HTTPS) connection setup. Our data set consists of 23B SSL certificate chains collected from a global panel consisting of over 2M residential client machines over a period of 6 months. The data informing our analyses provide perspective on the entire chain of trust, including root certificates, across a wide distribution of client machines. We identify over 35M unique certificate chains with diverse relationships at all levels of the PKI hierarchy. We report on the characteristics of valid certificates, which make up 99.7% of the total corpus. We also examine invalid certificate chains, finding that 93% of them contain an untrusted root certificate and we find they have shorter average chain length than their valid counterparts. Finally, we examine two unintended but prevalent behaviors in our data: the deprecation of root certificates and secure traffic interception. Our results support aspects of prior, scan-based studies on certificate characteristics but contradict other findings, highlighting the importance of the residential client-side perspective.

Functionally safe control logic design without full duplication is difficult due to the complexity of random control logic. The Reorder buffer (ROB) is a control logic function commonly used in high performance computing systems. In this study, we focus on a safe ROB design used in an industry quality Network-on-Chip (NoC) Advanced eXtensible Interface (AXI) Network Interface (NI) block. We developed and applied area efficient safe design techniques including partial duplication, Error Detection Code (EDC) and invariance checking with formal proofs and showed that we can achieve a desired safe Diagnostic Coverage (DC) requirement with small area and power overheads and no performance degradation.