Biblio

The deceleration of transistor feature size scaling has motivated growing adoption of specialized accelerators implemented as GPUs, FPGAs, ASICs, and more recently new types of computing such as neuromorphic, bio-inspired, ultra low energy, reversible, stochastic, optical, quantum, combinations, and others unforeseen. There is a tension between specialization and generalization, with the current state trending to master slave models where accelerators (slaves) are instructed by a general purpose system (master) running an Operating System (OS). Traditionally, an OS is a layer between hardware and applications and its primary function is to manage hardware resources and provide a common abstraction to applications. Does this function, however, apply to new types of computing paradigms? This paper revisits OS functionality for memristor-based accelerators. We explore one accelerator implementation, the Dot Product Engine (DPE), for a select pattern of applications in machine learning, imaging, and scientific computing and a small set of use cases. We explore typical OS functionality, such as reconfiguration, partitioning, security, virtualization, and programming. We also explore new types of functionality, such as precision and trustworthiness of reconfiguration. We claim that making an accelerator, such as the DPE, more general will result in broader adoption and better utilization.

This study proposes to apply an efficient formulation to solve the stochastic security-constrained generation capacity expansion planning (GCEP) problem using an improved method to directly compute the generalized generation distribution factors (GGDF) and the line outage distribution factors (LODF) in order to model the pre- and the post-contingency constraints based on the only application of the partial transmission distribution factors (PTDF). The classical DC-based formulation has been reformulated in order to include the security criteria solving both pre- and post-contingency constraints simultaneously. The methodology also takes into account the load uncertainty in the optimization problem using a two-stage multi-period model, and a clustering technique is used as well to reduce load scenarios (stochastic problem). The main advantage of this methodology is the feasibility to quickly compute the LODF especially with multiple-line outages (N-m). This idea could speed up contingency analyses and improve significantly the security-constrained analyses applied to GCEP problems. It is worth to mentioning that this approach is carried out without sacrificing optimality.

Companies want to offer chat bots to their customers and employees which can answer questions, enable self-service, and showcase their products and services. Implementing and maintaining chat bots by hand costs time and money. Companies typically have web APIs for their services, which are often documented with an API specification. This paper presents a compiler that takes a web API specification written in Swagger and automatically generates a chat bot that helps the user make API calls. The generated bot is self-documenting, using descriptions from the API specification to answer help requests. Unfortunately, Swagger specifications are not always good enough to generate high-quality chat bots. This paper addresses this problem via a novel in-dialogue curation approach: the power user can improve the generated chat bot by interacting with it. The result is then saved back as an API specification. This paper reports on the design and implementation of the chat bot compiler, the in-dialogue curation, and working case studies.

A wireless sensor network (WSN) is composed of sensor nodes and a base station. In WSNs, constructing an efficient key-sharing scheme to ensure a secure communication is important. In this paper, we propose a new key-sharing scheme for groups, which shares a group key in a single broadcast without being dependent on the number of nodes. This scheme is based on geometric characteristics and has information-theoretic security in the analysis of transmitted data. We compared our scheme with conventional schemes in terms of communication traffic, computational complexity, flexibility, and security, and the results showed that our scheme is suitable for an Internet-of-Things (IoT) network.

In this paper, we present a framework for graph-based representation of relation between sensors and alert types in a security alert sharing platform. Nodes in a graph represent either sensors or alert types, while edges represent various relations between them, such as common type of reported alerts or duplicated alerts. The graph is automatically updated, stored in a graph database, and visualized. The resulting graph will be used by network administrators and security analysts as a visual guide and situational awareness tool in a complex environment of security alert sharing.

The main goal of introducing an identity-based cryptosystem and certificateless cryptosystem was avoiding certificates' management costs. In turn, the goal of introducing a certificate-based cryptosystem was to solve the certificate revocation problem. In this paper, we propose a new digital Implicit and Explicit Certificates-Based Hess's Signature (IE-CBHS) scheme that combines the features of a standard public key infrastructure (PKI) and certificate-based cryptosystem. Our IE-CBHS scheme is an efficient certificates-based signature. The security analysis proves that the scheme is secure against two game attacks in the random oracle model. The security is closely related to the difficulty of solving the computational Diffie–Hellman and discrete logarithm problems. The IE-CBHS scheme, when compared with other signature schemes, has similar efficiency and is both more flexible and more useful in practice. It is possible to revoke the explicit certificate and use that fact during digital signature verification. Thus, our scheme is useful in applications where typical mechanisms of standard PKI are used. One of many important security features is resistance to denial of signature verification attack. Also, it is impossible for a trusted authority to recreate a partial private key, even with cooperation with the signer.

Recently, the researches utilizing environmentally friendly new and renewable energy and various methods have been actively pursued to solve environmental and energy problems. The trend of the technology is converged with the latest ICT technology and expanded to the cloud of share and two-way system. In the center of this tide of change, new technologies such as IoT, Big Data and AI are sustaining to energy technology. Now, the cloud concept which is a universal form in IT field will be converged with energy field to develop Energy Cloud, manage zero energy towns and develop into social infrastructure supporting smart city. With the development of social infrastructure, it is very important as a security facility. In this paper, it is discussed the concept and the configuration of the Energy Cloud, and present a basic design method of the Energy Cloud's security that can examine and respond to the risk factors of information security in the Energy Cloud.

We show high confinement thermally tunable, low loss (0.27 ± 0.04 dB/cm) Si3N4waveguides that are 42 cm long. We show that this platform can enable the miniaturization of traditionally bulky active OCT components.

Recently, Ransomware has been rapidly increasing and is becoming far more dangerous than other common malware types. Unlike previous versions of Ransomware that infect email attachments or access certain sites, the new Ransomware, such as WannaCryptor, corrupts data even when the PC is connected to the Internet. Therefore, many studies are being conducted to detect and defend Ransomware. However, existing studies on Ransomware detection cannot effectively detect and defend the new Ransomware because it detects Ransomware using signature databases or monitoring specific activities of processes. In this paper, we propose a method to make decoy files for detecting Ransomwares efficiently. The proposed method is based on the analysis of the behaviors of existing Ransomwares at the source code level.

Given the complexities involved in the sensing, navigational and positioning environment on board automated vehicles we conduct an exploratory survey and identify factors capable of influencing the users' trust in such system. After the analysis of the survey data, the Situational Awareness of the Vehicle (SAV) emerges as an important factor capable of influencing the trust of the users. We follow up on that by conducting semi-structured interviews with 12 experts in the CAV field, focusing on the importance of the SAV, on the factors that are most important when talking about it as well as the need to keep the users informed regarding its status. We conclude that in the context of Connected and Automated Vehicles (CAVs), the importance of the SAV can now be expanded beyond its technical necessity of making vehicles function to a human factors area: calibrating users' trust.

Explosive naval mines pose a threat to ocean and sea faring vessels, both military and civilian. This work applies deep neural network (DNN) methods to the problem of detecting minelike objects (MLO) on the seafloor in side-scan sonar imagery. We explored how the DNN depth, memory requirements, calculation requirements, and training data distribution affect detection efficacy. A visualization technique (class activation map) was incorporated that aids a user in interpreting the model's behavior. We found that modest DNN model sizes yielded better accuracy (98%) than very simple DNN models (93%) and a support vector machine (78%). The largest DNN models achieved textless;1% efficacy increase at a cost of a 17x increase of trainable parameter count and computation requirements. In contrast to DNNs popularized for many-class image recognition tasks, the models for this task require far fewer computational resources (0.3% of parameters), and are suitable for embedded use within an autonomous unmanned underwater vehicle.

Network traffic identification has been a hot topic in network security area. The identification of abnormal traffic can detect attack traffic and helps network manager enforce corresponding security policies to prevent attacks. Support Vector Machines (SVMs) are one of the most promising supervised machine learning (ML) algorithms that can be applied to the identification of traffic in IP networks as well as detection of abnormal traffic. SVM shows better performance because it can avoid local optimization problems existed in many supervised learning algorithms. However, as a binary classification approach, SVM needs more research in multiclass classification. In this paper, we proposed an abnormal traffic identification system(ATIS) that can classify and identify multiple attack traffic applications. Each component of ATIS is introduced in detail and experiments are carried out based on ATIS. Through the test of KDD CUP dataset, SVM shows good performance. Furthermore, the comparison of experiments reveals that scaling and parameters has a vital impact on SVM training results.

This paper presents our results from identifying anddocumenting false positives generated by static code analysistools. By false positives, we mean a static code analysis toolgenerates a warning message, but the warning message isnot really an error. The goal of our study is to understandthe different kinds of false positives generated so we can (1)automatically determine if an error message is truly indeed a truepositive, and (2) reduce the number of false positives developersand testers must triage. We have used two open-source tools andone commercial tool in our study. The results of our study haveled to 14 core false positive patterns, some of which we haveconfirmed with static code analysis tool developers.

The high mobility of Army tactical networks, combined with their close proximity to hostile actors, elevates the risks associated with short-range network attacks. The connectivity model for such short range connections under active operations is extremely fluid, and highly dependent upon the physical space within which the element is operating, as well as the patterns of movement within that space. To handle these dependencies, we introduce the notion of "key cyber-physical terrain": locations within an area of operations that allow for effective control over the spread of proximity-dependent malware in a mobile tactical network, even as the elements of that network are in constant motion with an unpredictable pattern of node-to-node connectivity. We provide an analysis of movement models and approximation strategies for finding such critical nodes, and demonstrate via simulation that we can identify such key cyber-physical terrain quickly and effectively.

Since the first whole-genome sequencing, the biomedical research community has made significant steps towards a more precise, predictive and personalized medicine. Genomic data is nowadays widely considered privacy-sensitive and consequently protected by strict regulations and released only after careful consideration. Various additional types of biomedical data, however, are not shielded by any dedicated legal means and consequently disseminated much less thoughtfully. This in particular holds true for DNA methylation data as one of the most important and well-understood epigenetic element influencing human health. In this paper, we show that, in contrast to the aforementioned belief, releasing one's DNA methylation data causes privacy issues akin to releasing one's actual genome. We show that already a small subset of methylation regions influenced by genomic variants are sufficient to infer parts of someone's genome, and to further map this DNA methylation profile to the corresponding genome. Notably, we show that such re-identification is possible with 97.5% accuracy, relying on a dataset of more than 2500 genomes, and that we can reject all wrongly matched genomes using an appropriate statistical test. We provide means for countering this threat by proposing a novel cryptographic scheme for privately classifying tumors that enables a privacy-respecting medical diagnosis in a common clinical setting. The scheme relies on a combination of random forests and homomorphic encryption, and it is proven secure in the honest-but-curious model. We evaluate this scheme on real DNA methylation data, and show that we can keep the computational overhead to acceptable values for our application scenario.

With the amount of user-contributed image data increasing, it is a potential threat for users that everyone may have the access to gain privacy information. To reduce the possibility of the loss of real information, this paper combines homomorphic encryption scheme and image feature extraction to provide a guarantee for users' privacy. In this paper, the whole system model mainly consists of three parts, including social network service providers (SP), the Interested party (IP) and the applications. Except for the image preprocessing phase, the main operations of feature extraction are conducted in ciphertext domain, which means only SP has the access to the privacy of the users. The extraction algorithm is used to obtain a multi-dimensional histogram descriptor as image feature for each image. As a result, the histogram descriptor can be extracted correctly in encrypted domain in an acceptable time. Besides, the extracted feature can represent the image effectively because of relatively high accuracy. Additionally, many different applications can be conducted by using the encrypted features because of the support of our encryption scheme.

Conducted emission of motors is a domain of interest for EMC as it may introduce disturbances in the system in which they are integrated. Nevertheless few publications deal with the susceptibility of motors, and especially, servomotors despite this devices are more and more used in automated production lines as well as for robotics. Recent papers have been released devoted to the possibility of compromising such systems by cyber-attacks. One could imagine the use of smart intentional electromagnetic interference to modify their behavior or damage them leading in the modification of the industrial process. This paper aims to identify the disturbances that may affect the behavior of a Commercial Off-The-Shelf servomotor when exposed to an electromagnetic field and the criticality of the effects with regards to its application. Experiments have shown that a train of radio frequency pulses may induce an erroneous reading of the position value of the servomotor and modify in an unpredictable way the movement of the motor's axis.