Biblio

Nowadays, we have witnessed the early progress on learning the association between voice and face automatically, which brings a new wave of studies to the computer vision community. However, most of the prior arts along this line (a) merely adopt local information to perform modality alignment and (b) ignore the diversity of learning difficulty across different subjects. In this paper, we propose a novel framework to jointly address the above-mentioned issues. Targeting at (a), we propose a two-level modality alignment loss where both global and local information are considered. Compared with the existing methods, we introduce a global loss into the modality alignment process. The global component of the loss is driven by the identity classification. Theoretically, we show that minimizing the loss could maximize the distance between embeddings across different identities while minimizing the distance between embeddings belonging to the same identity, in a global sense (instead of a mini-batch). Targeting at (b), we propose a dynamic reweighting scheme to better explore the hard but valuable identities while filtering out the unlearnable identities. Experiments show that the proposed method outperforms the previous methods in multiple settings, including voice-face matching, verification and retrieval.

Over the past few years computer vision has become the essential aspect of modern era of technology. This computer vision is manly based on image processing whereas the image processing includes three important aspects as image filtering, image compression & image security. The image filtering can be achieved by using various filtering techniques but the PSNR & operating frequency are the most challenging aspects of image filtering. This paper mainly focused on overcoming the challenges appears while removing the salt & pepper noise with conventional median filtering by developing improved adaptive moving window architecture median filter & comparing its performance to have improved performance in terms of PSNR & operating frequency.

The growing need for reliable communication over untrusted networks has caused a renewed interest in adversarial channel models, which often behave much differently than traditional stochastic channel models. Of particular practical use is the assumption of a causal or online adversary who is limited to causal knowledge of the transmitted codeword. In this work, we consider stochastic-adversarial mixed noise models. In the setup considered, a transmit node (Alice) attempts to communicate with a receive node (Bob) over a binary erasure channel (BEC) or binary symmetric channel (BSC) in the presence of an online adversary (Calvin) who can erase or flip up to a certain number of bits at the input of the channel. Calvin knows the encoding scheme and has strict causal access to Bob's reception through feedback snooping. For erasures, we provide a complete capacity characterization with and without transmitter feedback. For bit-flips, we provide converse and achievability bounds.

Advanced persistent threats targeting sensitive corporations, are becoming today stealthier and more complex, coordinating different attacks steps and lateral movements, and trying to stay undetected for long time. Classical security solutions that rely on signature-based detection can be easily thwarted by malware using obfuscation and encryption techniques. More recent solutions are using machine learning approaches for detecting outliers. Nevertheless, the majority of them reason on tabular unstructured data which can lead to missing obvious conclusions. We propose in this paper a novel approach that leverages a combination of both knowledge graphs and machine learning techniques to detect and predict attacks. Using Cyber Threat Intelligence (CTI), we built a knowledge graph that processes event logs in order to not only detect attack techniques, but also learn how to predict them.

The maturity of intelligent transportation system, cloud computing and Internet of Things (IoT) technology has encouraged the rapid growth of vehicular ad-hoc networks (VANETs). Currently, vehicles are supposed to carry relatively more storage, on board computing facilities, increased sensing power and communication systems. In order to cope with real world demands such as low latency, low storage cost, mobility, etc., for the deployment of VANETs, numerous attempts have been taken to integrate fog-computing with VANETs. In the recent past, Ma et al. (IEEE Internet of Things, pp 2327-4662, 10. 1109/JIOT.2019.2902840) designed “An Efficient and Provably Secure Authenticated Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks”. Ma et al. claimed that their protocol offers secure communication in fog-based VANETs and is resilient against several security attacks. However, this comment demonstrates that their scheme is defenseless against vehicle-user impersonation attack and reveals secret keys of vehicle-user and fog-node. Moreover, it fails to offer vehicle-user anonymity and has inefficient login phase. This paper also gives some essential suggestions on strengthening resilience of the scheme, which are overlooked by Ma et al.

Cyber-physical systems are used in many areas of human life. But people do not pay enough attention to ensuring the security of these systems. As a result of the resulting security gaps, an attacker can launch an attack, not only shutting down the system, but also having some negative impact on the environment. The article examines denial of service attacks in ad-hoc networks, conducts experiments and considers the consequences of their successful execution. As a result of the research, it was determined that an attack can be detected by changes in transmitted traffic and processor load. The cyber-physical system operates on stable algorithms, and even if legal changes occur, they can be easily distinguished from those caused by the attack. The article shows that the use of statistical methods for analyzing traffic and other parameters can be justified for detecting an attack. This study shows that each attack affects traffic in its own way and creates unique patterns of behavior change. The experiments were carried out according to methodology with changings in the intensity of the attacks, with a change in normal behavior. The results of this study can further be used to implement a system for detecting attacks on cyber-physical systems. The collected datasets can be used to train the neural network.

The research proposal discloses a novel drone-based ad-hoc network that leverages acoustic information for power plant surveillance and utilizes a secure blockchain model for protecting the integrity of drone communication over the network. The paper presents a vision for the drone-based networks, wherein drones are employed for monitoring the complex power plant machinery. The drones record acoustic information generated by the power plants and detect anomalies or deviations in machine behavior based on collected acoustic data. The drones are linked to distributed network of computing devices in possession with the plant stakeholders, wherein each computing device maintains a chain of data blocks. The chain of data blocks represents one or more transactions associated with power plants, wherein transactions are related to high risk auditory data set accessed by the drones in an event of anomaly or machine failure. The computing devices add at least one data block to the chain of data blocks in response to valid transaction data, wherein the transaction data is validated by the computing devices owned by power plant personnel.

In recent years, biometric authentication, such as fingerprint and face recognition, has become widespread in smartphones. However, fingerprint and face authentication have the problem that they cannot be used depending on the condition of the user's fingers or face. Therefore, we have been investigating a new biometric authentication system using pinna as a personal authentication system for smart phones. We have studied a personal authentication system using the Pinna Related Transfer Function (PRTF), which is an acoustic transfer function measured from the pinna. However, since the position of the smartphone changes every time it is placed on the ear, there is a problem that the authentication rate decreases. In this paper, we propose a multimodal personal authentication system using PRTF, pinna images, and smartphone location information, and verify its effectiveness. The results show that the proposed authentication system can improve the robustness against the fluctuation of the smartphone location.

With the continuous development of Internet technology, people pay more and more attention to private security. In particular, third-party tracking is a major factor affecting privacy security. So far, the most effective way to prevent third-party tracking is to create a blacklist. However, blacklist generation and maintenance need to be carried out manually which is inefficient and difficult to maintain. In order to generate blacklists more quickly and accurately in this era of big data, this paper proposes a machine learning system MFTrackerDetector against third-party tracking. The system is based on the theory of structural hole and only detects third-party trackers. The system consists of two subsystems, DMTrackerDetector and DFTrackerDetector. DMTrackerDetector is a JavaScript-based subsystem and DFTrackerDetector is a Flash-based subsystem. Because tracking code and non-tracking code often call different APIs, DMTrackerDetector builds a classifier using all the APIs in JavaScript as features and extracts the API features in JavaScript through dynamic analysis. Unlike static analysis method, the dynamic analysis method can effectively avoid code obfuscation. DMTrackerDetector eventually generates a JavaScript-based third-party tracker list named Jlist. DFTrackerDetector constructs a classifier using all the APIs in ActionScript as features and extracts the API features in the flash script through static analysis. DFTrackerDetector finally generates a Flash-based third-party tracker list named Flist. DFTrackerDetector achieved 92.98% accuracy in the Flash test set and DMTrackerDetector achieved 90.79% accuracy in the JavaScript test set. MFTrackerDetector eventually generates a list of third-party trackers, which is a combination of Jlist and Flist.

We propose an autoencoder based approach to anomaly detection in smart grid systems. Data collecting sensors within smart home systems are susceptible to many data corruption issues, such as malicious attacks or physical malfunctions. By applying machine learning to a smart home or grid, sensor anomalies can be detected automatically for secure data collection and sensor-based system functionality. In addition, we tested the effectiveness of this approach on real smart home sensor data collected for multiple years. An early detection of such data corruption issues is essential to the security and functionality of the various sensors and devices within a smart home.

Smart grids are being continually adopted as a replacement of the traditional power grid systems to ensure safe, efficient, and cost-effective power distribution. The smart grid is a heterogeneous communication network made up of various devices such as smart meters, automation, and emerging technologies interacting with each other. As a result, the smart grid inherits most of the security vulnerabilities of cyber systems, putting the smart grid at risk of cyber-attacks. To secure the communication between smart grid entities, namely the smart meters and the utility, we propose in this paper a communication infrastructure built on top of a blockchain network, specifically Ethereum. All two-way communication between the smart meters and the utility is assumed to be transactions governed by smart contracts. Smart contracts are designed in such a way to ensure that each smart meter is authentic and each smart meter reading is reported securely and privately. We present a simulation of a sample smart grid and report all the costs incurred from building such a grid. The simulations illustrate the feasibility and security of the proposed architecture. They also point to weaknesses that must be addressed, such as scalability and cost.

The need of more secured and environmental energy is becoming a necessity and priority in an environment suffering from serious problems due to technological development. Since the Smart Grid is a promising alternative that supports green energy and enhances a better management of electricity, the security side has became one of the major and critical associated issues in building the communication network in the microgrid.In this paper we will present the Smart Grid Cyber security challenges and propose a distributed algorithm that face one of the biggest problems threatening the smart grid which is fires.

Demand response (DR) is one of the most economical methods for peak demand reduction, renewable energy integration and ancillary service support. Residential electrical energy consumption takes approximately 33% of the total electricity usage and hence has great potentials in DR applications. However, residential DR encounters various challenges such as small individual magnitude, stochastic consuming patterns and privacy issues. In this study, we propose a stochastic optimal mechanism to tackle these issues and try to reveal the benefits from residential DR implementation. Stochastic residential load (SRL) models, a generation cost prediction (GCP) model and a stochastic optimal load aggregation (SOLA) model are developed. A set of uniformly distributed scalers is introduced into the SOLA model to efficiently avoid the peak demand rebound problem in DR applications. The SOLA model is further transformed into a deterministic LP model. Time-of-Use (TOU) tariff is adopted as the price structure because of its similarity and popularity. Case studies show that the proposed mechanism can significantly reduce the peak-to-average power ratio (PAPR) of the load profile as well as the electrical energy cost. Furthermore, the impacts of consumers' participation levels in the DR program are investigated. Simulation results show that the 50% participation level appears as the best case in terms system stability. With the participation level of 80%, consumers' electrical energy cost is minimized. The proposed mechanism can be used by a residential load aggregator (LA) or a utility to plan a DR program, predict its impacts, and aggregate residential loads to minimize the electrical energy cost.

This study presents an open standards-based information system supporting democratisation and consumer empowerment through flexibility activation. This study describes a functional technical reference infrastructure: a secure, standard-based and viable communication backbone for flexibility activation. The infrastructure allows connection, registering, activation and reporting for different types of granular consumer flexibility. The flexibility sources can be directly controllable set points of chargers and stationary batteries, as well as controllable loads. The proposed communication system sees all these flexibility provisions as distributed energy resources in a wider sense, and the architecture allows consumer-level integration of different energy systems. This makes new flexibility sources fully available to the balancing responsible entities in a viable and realistically implementable manner. The proposed reference architecture, as implemented in the FLEXCoop project, relies on established open standards as it is based on the Open Automated Demand Response (OpenADR) and OAuth2/OpenID standards and the corresponding IEC 62746-10 standard, and it covers interfacing towards other relevant standards. The security and access implications are addressed by the OpenID security layer built on top of the OAuth2 and integrated with the OpenADR standard. To address the data protection and privacy aspects, the architecture is designed on the least knowledge principle.

An adversarial attack is an algorithm that perturbs the input of a machine learning model in an intelligent way in order to change the output of the model. An important property of adversarial attacks is transferability. According to this property, it is possible to generate adversarial perturbations on one model and apply it the input to fool the output of a different model. Our work focuses on studying the transferability of adversarial attacks in sound event classification. We are able to demonstrate differences in transferability properties from those observed in computer vision. We show that dataset normalization techniques such as z-score normalization does not affect the transferability of adversarial attacks and we show that techniques such as knowledge distillation do not increase the transferability of attacks.

Intentional interference such as spoofing is an emerging threat to GPS receivers used in both civilian and defense applications. With the majority of smartphones relying on GPS for positioning and navigation, the vulnerability of these phones to spoofing attacks is an issue of security concern. In this paper, it is demonstrated that is easy to successfully spoof a smartphone using a simplistic spoofing technique. A spoofing signal is generated using open-source signal simulator and transmitted using a low-cost SDR. In view of the tremendously increasing usage of GPS enabled smartphones, it is necessary to develop suitable countermeasures for spoofing. This work carries significance as it would help in understanding the effects of spoofing at various levels of signal processing in the receiver and develop advanced spoofing detection and mitigation techniques.

In recent times, data excessiveness has become a major problem in the field of education, news, blogs, social media, etc. Due to an increase in such a vast amount of text data, it became challenging for a human to extract only the valuable amount of data in a concise form. In other words, summarizing the text, enables human to retrieves the relevant and useful texts, Text summarizing is extracting the data from the document and generating the short or concise text of the document. One of the major approaches that are used widely is Automatic Text summarizer. Automatic text summarizer analyzes the large textual data and summarizes it into the short summaries containing valuable information of the data. Automatic text summarizer further divided into two types 1) Extractive text summarizer, 2) Abstractive Text summarizer. In this article, the extractive text summarizer approach is being looked for. Extractive text summarization is the approach in which model generates the concise summary of the text by picking up the most relevant sentences from the text document. This paper focuses on retrieving the valuable amount of data using the Elmo embedding in Extractive text summarization. Elmo embedding is a contextual embedding that had been used previously by many researchers in abstractive text summarization techniques, but this paper focus on using it in extractive text summarizer.

Deep learning is becoming a basis of decision making systems in many application domains, such as autonomous vehicles, health systems, etc., where the risk of misclassification can lead to serious consequences. It is necessary to know to which extent are Deep Neural Networks (DNNs) robust against various types of adversarial conditions. In this paper, we experimentally evaluate DNNs implemented in embedded device by using laser fault injection, a physical attack technique that is mostly used in security and reliability communities to test robustness of various systems. We show practical results on four activation functions, ReLu, softmax, sigmoid, and tanh. Our results point out the misclassification possibilities for DNNs achieved by injecting faults into the hidden layers of the network. We evaluate DNNs by using several different attack strategies to show which are the most efficient in terms of misclassification success rates. Outcomes of this work should be taken into account when deploying devices running DNNs in environments where malicious attacker could tamper with the environmental parameters that would bring the device into unstable conditions. resulting into faults.

Quantum communication network is a new type of secure communication technique and has drawn a lot of attentions in recent years, it has absolute safety in theory. However, quantum communication networks can still be attacked in different ways, among which the intermediate basis attack based on intercept-resend is a typical eavesdropping strategy. With this method, The probability of the eavesdropper correctly guessing the sender's code value can reach up to 0.854, resulting in the quantum bit error rate (QBER) of 0.25. To improve the security performance of quantum communication networks, we propose a strategy based on attack basis detection for beating the intermediate basis attack named “WN19”. In WN19, we can reduce QBER and the probability of the eavesdropper obtaining information correctly by adjusting the initial state of the quantum state of the sender according to the result of attack basis detection. The simulation results show that if the polarization angle \$þeta\$ of the attack basis is \$\textbackslashtextbackslashpi/8\$, the QBER reduces from 0.25 to 0.1367 and the probability of eavesdropper correctly obtaining information decreases from 0.854 to 0.5811. It effectively improves the security of quantum cryptography under intermediate basis attack and provides a theoretical basis for the healthy development of quantum communication system.

With the increasing deployment of enterprise-scale distributed systems, effective and practical defenses for such systems against various security vulnerabilities such as sensitive data leaks are urgently needed. However, most existing solutions are limited to centralized programs. For real-world distributed systems which are of large scales, current solutions commonly face one or more of scalability, applicability, and portability challenges. To overcome these challenges, we develop a novel dynamic taint analysis for enterprise-scale distributed systems. To achieve scalability, we use a multi-phase analysis strategy to reduce the overall cost. We infer implicit dependencies via partial-ordering method events in distributed programs to address the applicability challenge. To achieve greater portability, the analysis is designed to work at an application level without customizing platforms. Empirical results have shown promising scalability and capabilities of our approach.

Mobile Device Security is an important factor as all the user's sensitive information is stored on the mobile device. The problem of mobile devices getting lost or stolen has only been increasing. There are various systems which provide Online Mobile Device Security which require internet to perform their required functions. Our proposed system SMS Based Offline Mobile Device Security System provides mobile device users with a wide range of security features that help protect the mobile device from theft and also acts as an assistant that helps the users in any problems they may face in their day-to-day lives. The project aims to develop a mobile security system that will allow the user to manipulate his mobile device from any other device through SMS which can be used to get contact information from the user's mobile device remotely, help find the phone by maximizing the volume and playing a tone, trace the current location of the mobile device, get the IMEI No of the device, lock the device, send a message that will be converted to speech and played on the user's mobile device, call forwarding, message forwarding and various other features. It also has an additional security feature that will detect a sim card change and send the new SIM card mobile no to the recovery mobile numbers specified during initial setup automatically. Hence, the user will be able to manipulate his phone even after the SIM card has been changed. Therefore, the SMS-Based Offline Mobile Device Security System provides much more security for the mobile device than the existing online device security methods.

As the application of Internet of Things technology becomes more common, the security problems derived from it became more and more serious. Different from the traditional Internet, the security of the Internet of Things presented new features. This paper introduced the current situation of Internet of Things security, generalized the definitions of situation awareness and network security situation awareness, and finally discussed the methods of establishing security situational awareness of Internet of Things which provided some tentative solutions to the new DDoS attack caused by Internet of Things terminals.

This paper presents an event-triggered statistical estimation strategy and a data collection architecture for situational awareness (SA) in microgrids. An estimation agent structure based on the event-triggered Kalman filter is proposed and implemented for state estimation layer of the SA using long range wide area network (LoRAWAN) protocol. A setup has been developed which provides enormous data collection capabilities from smart meters in order to realize an adequate level of SA in microgrids. Thingsboard Internet of things (IoT) platform is used for the SA visualization with a customized dashboard. It is shown that by using the developed estimation strategy, an adequate level of SA can be achieved with a minimum installation and communication cost to have an accurate average state estimation of the microgrid.

The recent development of distribution-level phasor measurement units, a.k.a. micro-PMUs, has been an important step towards achieving situational awareness in power distribution networks. The challenge however is to transform the large amount of data that is generated by micro-PMUs to actionable information and then match the information to use cases with practical value to system operators. This open problem is addressed in this paper. First, we introduce a novel data-driven event detection technique to pick out valuable portion of data from extremely large raw micro-PMU data. Subsequently, a datadriven event classifier is developed to effectively classify power quality events. Importantly, we use field expert knowledge and utility records to conduct an extensive data-driven event labeling. Moreover, certain aspects from event detection analysis are adopted as additional features to be fed into the classifier model. In this regard, a multi-class support vector machine (multi-SVM) classifier is trained and tested over 15 days of real-world data from two micro-PMUs on a distribution feeder in Riverside, CA. In total, we analyze 1.2 billion measurement points, and 10,700 events. The effectiveness of the developed event classifier is compared with prevalent multi-class classification methods, including k-nearest neighbor method as well as decision-tree method. Importantly, two real-world use-cases are presented for the proposed data analytics tools, including remote asset monitoring and distribution-level oscillation analysis.

Undamped power system oscillations are detrimental to stable and security of the electric grid. Historically, poorly damped low frequency rotor oscillations have caused system blackouts or brownouts. It is required to monitor the oscillation damping controllers such as power system stabilizers' (PSS) performance at energy control centers as well as at power plant control centers. Phasor measurement units (PMUs) based time response and frequency response information on PSS performance is collected. A fuzzy logic system is developed to combine the time and frequency response information to derive the situational awareness on PSS performance on synchronous generator's oscillation(s). A two-area four-machine benchmark power system is simulated on a real-time digital simulator platform. Fuzzy logic system developed is evaluated for different system disturbances. Situational awareness on PSS performance on synchronous generator's oscillation(s) allows the control center operator to enhance the power system operation more stable and secure.