Biblio

Traditionally, utility crews have used faulted circuit indicators (FCIs) to locate faulted line sections. FCIs monitor current and provide a local visual indication of recent fault activity. When a fault occurs, the FCIs operate, triggering a visual indication that is either a mechanical target (flag) or LED. There are also enhanced FCIs with communications capability, providing fault status to the outage management system (OMS) or supervisory control and data acquisition (SCADA) system. Such quickly communicated information results in faster service restoration and reduced outage times. For distribution system protection, protection devices (such as recloser controls) must coordinate with downstream devices (such as fuses or other recloser controls) to clear faults. Furthermore, if there are laterals on a feeder that are protected by a recloser control, it is desirable to communicate to the recloser control which lateral had the fault in order to enhance tripping schemes. Because line sensors are typically placed along distribution feeders, they are capable of sensing fault status and characteristics closer to the fault. If such information can be communicated quickly to upstream protection devices, at protection speeds, the protection devices can use this information to securely speed up distribution protection scheme operation. With recent advances in low-power electronics, wireless communications, and small-footprint sensor transducers, wireless line sensors can now provide fault information to the protection devices with low latencies that support protection speeds. This paper describes the components of a wireless protection sensor (WPS) system, its integration with protection devices, and how the fault information can be transmitted to such devices. Additionally, this paper discusses how the protection devices use this received fault information to securely speed up the operation speed of and improve the selectivity of distribution protection schemes, in add- tion to locating faulted line sections.

The growing volume of data and its increasing complexity require even more efficient and faster information retrieval techniques. Approximate nearest neighbor search algorithms based on hashing were proposed to query high-dimensional datasets due to its high retrieval speed and low storage cost. Recent studies promote the use of Convolutional Neural Network (CNN) with hashing techniques to improve the search accuracy. However, there are challenges to solve in order to find a practical and efficient solution to index CNN features, such as the need for a heavy training process to achieve accurate query results and the critical dependency on data-parameters. In this work we execute exhaustive experiments in order to compare recent methods that are able to produces a better representation of the data space with a less computational cost for a better accuracy by computing the best data-parameter values for optimal sub-space projection exploring the correlations among CNN feature attributes using fractal theory. We give an overview of these different techniques and present our comparative experiments for data representation and retrieval performance.

Virtual Routers (VRs) are increasingly common in cloud environments. VRs route traffic between network segments and support network services. Routers, including VRs, have been the target of several recent high-profile attacks, emphasizing the need for more security measures, including security monitoring. However, existing agent-based monitoring systems are incompatible with a VR's temporary nature, stripped-down operating system, and placement in the cloud. As a result, VRs are often not monitored, leading to undetected security incidents. This paper proposes a new security monitoring design that leverages virtualization instead of in-guest agents. Its hypervisor-based system, Arav, scrutinizes VRs by novel application of Virtual Machine Introspection (VMI) breakpoint injection. Arav monitored and addressed security-related events in two common VRs, pfSense and VyOS, and detected four attacks against two popular VR services, Quagga and OpenVPN. Arav's performance overhead is negligible, less than 0.63%, demonstrating VMI's utility in monitoring virtual machines unsuitable for traditional security monitoring.

In the field of smartphones a number of proposals suggest that sensing the ambient environment can act as an effective anti-relay mechanism. However, existing literature is not compliant with industry standards (e.g. EMV and ITSO) that require transactions to complete within a certain time-frame (e.g. 500ms in the case of EMV contactless payments). In previous work the generation of an artificial ambient environment (AAE), and especially the use of infrared light as an AAE actuator was shown to have high success rate in relay attacks detection. In this paper we investigate the application of infrared as a relay attack detection technique in various scenarios, namely, contactless transactions (mobile payments, transportation ticketing, and physical access control), and continuous Two-Factor Authentication. Operating requirements and architectures are proposed for each scenario, while taking into account industry imposed performance requirements, where applicable. Protocols for integrating the solution into the aforementioned scenarios are being proposed, and formally verified. The impact on the performance is assessed through practical implementation. Proposed protocols are verified using Scyther, a formal mechanical verification tool. Finally, additional scenarios, in which this technique can be applied to prevent relay or other types of attacks, are discussed.

The Network Intrusion Detection Systems (NIDS) are either signature based or anomaly based. In this paper presented NIDS system belongs to anomaly based Neural Network Intrusion Detection System (NNIDS). The proposed NNIDS is able to successfully recognize learned malicious activities in a network environment. It was tested for the SYN flood attack, UDP flood attack, nMap scanning attack, and also for non-malicious communication.

Software1 vulnerabilities are closely associated with information systems security, a major and critical field in today's technology. Vulnerabilities constitute a constant and increasing threat for various aspects of everyday life, especially for safety and economy, since the social impact from the problems that they cause is complicated and often unpredictable. Although there is an entire research branch in software engineering that deals with the identification and elimination of vulnerabilities, the growing complexity of software products and the variability of software production procedures are factors contributing to the ongoing occurrence of vulnerabilities, Hence, another area that is being developed in parallel focuses on the study and management of the vulnerabilities that have already been reported and registered in databases. The information contained in such databases includes, a textual description and a number of metrics related to vulnerabilities. The purpose of this paper is to investigate to what extend the assessment of the vulnerability severity can be inferred directly from the corresponding textual description, or in other words, to examine the informative power of the description with respect to the vulnerability severity. For this purpose, text mining techniques, i.e. text analysis and three different classification methods (decision trees, neural networks and support vector machines) were employed. The application of text mining to a sample of 70,678 vulnerabilities from a public data source shows that the description itself is a reliable and highly accurate source of information for vulnerability prioritization.

Vulnerability exploitation is reportedly one of the main attack vectors against computer systems. Yet, most vulnerabilities remain unexploited by attackers. It is therefore of central importance to identify vulnerabilities that carry a high 'potential for attack'. In this paper we rely on Symantec data on real attacks detected in the wild to identify a trade-off in the Impact and Complexity of a vulnerability in terms of attacks that it generates; exploiting this effect, we devise a readily computable estimator of the vulnerability's Attack Potential that reliably estimates the expected volume of attacks against the vulnerability. We evaluate our estimator performance against standard patching policies by measuring foiled attacks and demanded workload expressed as the number of vulnerabilities entailed to patch. We show that our estimator significantly improves over standard patching policies by ruling out low-risk vulnerabilities, while maintaining invariant levels of coverage against attacks in the wild. Our estimator can be used as a first aid for vulnerability prioritisation to focus assessment efforts on high-potential vulnerabilities.

Testing and fixing Web Application Firewalls (WAFs) are two relevant and complementary challenges for security analysts. Automated testing helps to cost-effectively detect vulnerabilities in a WAF by generating effective test cases, i.e., attacks. Once vulnerabilities have been identified, the WAF needs to be fixed by augmenting its rule set to filter attacks without blocking legitimate requests. However, existing research suggests that rule sets are very difficult to understand and too complex to be manually fixed. In this paper, we formalise the problem of fixing vulnerable WAFs as a combinatorial optimisation problem. To solve it, we propose an automated approach that combines machine learning with multi-objective genetic algorithms. Given a set of legitimate requests and bypassing SQL injection attacks, our approach automatically infers regular expressions that, when added to the WAF's rule set, prevent many attacks while letting legitimate requests go through. Our empirical evaluation based on both open-source and proprietary WAFs shows that the generated filter rules are effective at blocking previously identified and successful SQL injection attacks (recall between 54.6% and 98.3%), while triggering in most cases no or few false positives (false positive rate between 0% and 2%).

We present AVAMAT: AntiVirus and Malware Analysis Tool - a tool for analysing the malware detection capabilities of AntiVirus (AV) products running on different operating system (OS) platforms. Even though similar tools are available, such as VirusTotal and MetaDefender, they have several limitations, which motivated the creation of our own tool. With AVAMAT we are able to analyse not only whether an AV detects a malware, but also at what stage of inspection does it detect it and on what OS. AVAMAT enables experimental campaigns to answer various research questions, ranging from the detection capabilities of AVs on OSs, to optimal ways in which AVs could be combined to improve malware detection capabilities.

The panic among medical control, information, and device administrators is due to surmounting number of high-profile attacks on healthcare facilities. This hostile situation is going to lead the health informatics industry to cloud-hoarding of medical data, control flows, and site governance. While different healthcare enterprises opt for cloud-based solutions, it is a matter of time when fog computing environment are formed. Because of major gaps in reported techniques for fog security administration for health data i.e. absence of an overarching certification authority (CA), the security provisioning is one of the the issue that we address in this paper. We propose a security provisioning model (AZSPM) for medical devices in fog environments. We propose that the AZSPM can be build by using atomic security components that are dynamically composed. The verification of authenticity of the atomic components, for trust sake, is performed by calculating the processor clock cycles from service execution at the resident hardware platform. This verification is performed in the fully sand boxed environment. The results of the execution cycles are matched with the service specifications from the manufacturer before forwarding the mobile services to the healthcare cloud-lets. The proposed model is completely novel in the fog computing environments. We aim at building the prototype based on this model in a healthcare information system environment.

Sites for online classified ads selling sex are widely used by human traffickers to support their pernicious business. The sheer quantity of ads makes manual exploration and analysis unscalable. In addition, discerning whether an ad is advertising a trafficked victim or an independent sex worker is a very difficult task. Very little concrete ground truth (i.e., ads definitively known to be posted by a trafficker) exists in this space. In this work, we develop tools and techniques that can be used separately and in conjunction to group sex ads by their true owner (and not the claimed author in the ad). Specifically, we develop a machine learning classifier that uses stylometry to distinguish between ads posted by the same vs. different authors with 90% TPR and 1% FPR. We also design a linking technique that takes advantage of leakages from the Bitcoin mempool, blockchain and sex ad site, to link a subset of sex ads to Bitcoin public wallets and transactions. Finally, we demonstrate via a 4-week proof of concept using Backpage as the sex ad site, how an analyst can use these automated approaches to potentially find human traffickers.

Summary form only given. Strong light-matter coupling has been recently successfully explored in the GHz and THz [1] range with on-chip platforms. New and intriguing quantum optical phenomena have been predicted in the ultrastrong coupling regime [2], when the coupling strength Ω becomes comparable to the unperturbed frequency of the system ω. We recently proposed a new experimental platform where we couple the inter-Landau level transition of an high-mobility 2DEG to the highly subwavelength photonic mode of an LC meta-atom [3] showing very large Ω/ωc = 0.87. Our system benefits from the collective enhancement of the light-matter coupling which comes from the scaling of the coupling Ω ∝ √n, were n is the number of optically active electrons. In our previous experiments [3] and in literature [4] this number varies from 104-103 electrons per meta-atom. We now engineer a new cavity, resonant at 290 GHz, with an extremely reduced effective mode surface Seff = 4 × 10-14 m2 (FE simulations, CST), yielding large field enhancements above 1500 and allowing to enter the few ({\textbackslash}textless;100) electron regime. It consist of a complementary metasurface with two very sharp metallic tips separated by a 60 nm gap (Fig.1(a, b)) on top of a single triangular quantum well. THz-TDS transmission experiments as a function of the applied magnetic field reveal strong anticrossing of the cavity mode with linear cyclotron dispersion. Measurements for arrays of only 12 cavities are reported in Fig.1(c). On the top horizontal axis we report the number of electrons occupying the topmost Landau level as a function of the magnetic field. At the anticrossing field of B=0.73 T we measure approximately 60 electrons ultra strongly coupled (Ω/ω- {\textbackslash}textbar{\textbackslash}textbar

Runtime hardware Trojan detection techniques are required in third party IP based SoCs as a last line of defense. Traditional techniques rely on golden data model or exotic signal processing techniques such as utilizing Choas theory or machine learning. Due to cumbersome implementation of such techniques, it is highly impractical to embed them on the hardware, which is a requirement in some mission critical applications. In this paper, we propose a methodology that generates a digital power profile during the manufacturing test phase of the circuit under test. A simple processing mechanism, which requires minimal computation of measured power signals, is proposed. For the proof of concept, we have applied the proposed methodology on a classical Advanced Encryption Standard circuit with 21 available Trojans. The experimental results show that the proposed methodology is able to detect 75% of the intrusions with the potential of implementing the detection mechanism on-chip with minimal overhead compared to the state-of-the-art techniques.

Trust Management (TM) systems for authentication are vital to the security of online interactions, which are ubiquitous in our everyday lives. Various systems, like the Web PKI (X.509) and PGP's Web of Trust are used to manage trust in this setting. In recent years, blockchain technology has been introduced as a panacea to our security problems, including that of authentication, without sufficient reasoning, as to its merits.In this work, we investigate the merits of using open distributed ledgers (ODLs), such as the one implemented by blockchain technology, for securing TM systems for authentication. We formally model such systems, and explore how blockchain can help mitigate attacks against them. After formal argumentation, we conclude that in the context of Trust Management for authentication, blockchain technology, and ODLs in general, can offer considerable advantages compared to previous approaches. Our analysis is, to the best of our knowledge, the first to formally model and argue about the security of TM systems for authentication, based on blockchain technology. To achieve this result, we first provide an abstract model for TM systems for authentication. Then, we show how this model can be conceptually encoded in a blockchain, by expressing it as a series of state transitions. As a next step, we examine five prevalent attacks on TM systems, and provide evidence that blockchain-based solutions can be beneficial to the security of such systems, by mitigating, or completely negating such attacks.

In the recent years, we have observed the development of several connected and mobile devices intended for daily use. This development has come with many risks that might not be perceived by the users. These threats are compromising when an unauthorized entity has access to private big data generated through the user objects in the Internet of Things. In the literature, many solutions have been proposed in order to protect the big data, but the security remains a challenging issue. This work is carried out with the aim to provide a solution to the access control to the big data and securing the localization of their generator objects. The proposed models are based on Attribute Based Encryption, CHORD protocol and $μ$TESLA. Through simulations, we compare our solutions to concurrent protocols and we show its efficiency in terms of relevant criteria.

Driver's uncertainty during decision-making in overtaking results in long reaction times and potentially dangerous lane change maneuvers. Current lane change assistance systems focus on safety assessments providing either too conservative or excessive warnings, which influence driver's acceptance and trust in these systems. Inspired by the emancipation theory of trust, we expect systems providing information adapted to driver's uncertainty states to simultaneously help to reduce long reaction times and build the overall trust in automation. In previous work, we presented an adaptive lane change assistance system based on this concept utilizing a probabilistic model of driver's uncertainty. In this paper, we investigate whether the proposed system is able to improve reaction times and build trust in the automation as expected. A simulator study was conducted to compare the proposed system with an unassisted baseline and three reference systems not adaptive to driver's uncertainty. The results show while all systems reduce reaction times compared to the baseline, the proposed adaptive system is the most trusted and accepted.

This paper considers the security problem of outsourcing storage from user devices to the cloud. A secure searchable encryption scheme is presented to enable searching of encrypted user data in the cloud. The scheme simultaneously supports fuzzy keyword searching and matched results ranking, which are two important factors in facilitating practical searchable encryption. A chaotic fuzzy transformation method is proposed to support secure fuzzy keyword indexing, storage and query. A secure posting list is also created to rank the matched results while maintaining the privacy and confidentiality of the user data, and saving the resources of the user mobile devices. Comprehensive tests have been performed and the experimental results show that the proposed scheme is efficient and suitable for a secure searchable cloud storage system.

The MgO-based magnetic tunnel junction (MTJ) is the basis of modern hard disk drives' magnetic read sensors. Within its operating bandwidth, the sensor's performance is significantly affected by nonlinear and oscillating behavior arising from the MTJ's magnetization dynamics at microwave frequencies. Static I-V curve measurements are commonly used to characterize sensor's nonlinear effects. Unfortunately, these do not sufficiently capture the MTJ's magnetization dynamics. In this paper, we demonstrate the use of the two-tone measurement technique for full treatment of the sensor's nonlinear effects in conjunction with dynamic ones. This approach is new in the field of magnetism and magnetic materials, and it has its challenges due to the nature of the device. Nevertheless, the experimental results demonstrate how the two-tone measurement technique can be used to characterize magnetic sensor nonlinear properties.

In the past couple of years Cloud Computing has become an eminent part of the IT industry. As a result of its economic benefits more and more people are heading towards Cloud adoption. In present times there are numerous Cloud Service providers (CSP) allowing customers to host their applications and data onto Cloud. However Cloud Security continues to be the biggest obstacle in Cloud adoption and thereby prevents customers from accessing its services. Various techniques have been implemented by provides in order to mitigate risks pertaining to Cloud security. In this paper, we present a Hybrid Cryptographic System (HCS) that combines the benefits of both symmetric and asymmetric encryption thus resulting in a secure Cloud environment. The paper focuses on creating a secure Cloud ecosystem wherein we make use of multi-factor authentication along with multiple levels of hashing and encryption. The proposed system along with the algorithm are simulated using the CloudSim simulator. To this end, we illustrate the working of our proposed system along with the simulated results.