Biblio

In the northern gas fields, most data are transmitted via wireless networks, which requires special transmission security measures. Herewith, the gas field infrastructure dictates cybersecurity modules to not only meet standard requirements but also ensure reduced energy consumption. The paper discusses the issue of building such a module for a process control system based on the RTP-04M recorder operating in conjunction with an Android-based mobile device. The software options used for the RSA and Diffie-Hellman data encryption and decryption algorithms on both the RTP-04M and the Android-based mobile device sides in the Keil μVision4 and Android Studio software environments, respectively, have shown that the Diffie-Hellman algorithm is preferable. It provides significant savings in RAM and CPU resources and power consumption of the recorder. In terms of energy efficiency, the implemented programs have been analyzed in the Android Studio (Android Profiler) and Simplicity Studio (Advanced Energy Monitor) environments. The integration of this module into the existing software will improve the field's PCS cybersecurity level due to protecting data transmitted from third-party attacks.

The determination of the damper cage health is a matter of great importance in those industries that use large synchronous motors in their processes. In the past, unexpected damages of that element implied economic losses amounting up to several million \$. The problem is that, in the technical literature, there is a lack of non-invasive techniques enabling the reliable condition monitoring of this element. This explains the fact that, in industry, rudimentary methods are still employed to determine its condition. This paper proposes the analysis of the stray flux as a way to determine the condition of the damper cage. The paper shows that the analysis of the stray flux under starting yields characteristic time-frequency signatures of the fault components that can be used to reliably determine the condition of the damper. Moreover, the analysis of the stray flux at steady-state operation under asynchronous mode could give useful information to this end. The paper also analyses the influence of the remanent magnetism in the rotor of some synchronous motors, which can make the damper cage diagnosis more difficult; some solutions to this problem are also suggested in the paper.

Radio-frequency identification (RFID) has become a preferred technology for monitoring in industrial internet of things (IIoT) applications like supply chain, medical industry, vehicle tracking and warehouse monitoring where information is required continually. Typical security threats seen in these applications are denial of service (DOS) attack, transmission attack etc. We propose a novel edge data security schema based on spike modulation along with backscatter communication technique to modulate both sensor and identification (ID) information. It is observed that this data encoding schema works well even in a multi-tag single-reader environment. Further, it uses lower power and offers a low-cost solution for Industrial IoT applications.

This paper investigates the effects of real time visual biofeedback for improving sports performance using a large scale immersive mixed reality system in which users are able to play a simulated game of curling. The users slide custom curling stones across the floor onto a projected target whose size is dictated by the user’s stress-related physiological measure; heart rate (HR). The higher HR the player has, the smaller the target will be, and vice-versa. In the experiment participants were asked to compete in three different conditions: baseline, with and without the proposed biofeedback. The results show that when providing a visual representation of the player’s HR or "choking" in competition, it helped the player understand their condition and improve competition performance (P-value of 0.0391).

Many cloud services perform periodic database backup to keep the data safe from failures such as sudden system crashes. In the database system, two techniques are widely used for data backup and recovery: a physical backup and a logical backup. The physical backup uses raw data by copying the files in the database, whereas the logical backup extracts data from the database and dumps it into separated files as a sequence of query statements. Both techniques support a full backup strategy that contains data of the entire database and incremental backup strategy that contains changed data since a previous backup. However, both strategies require additional I/O operations to perform the backup and need a long time to restore a backup. In this paper, we propose an efficient backup and recovery scheme by exploiting write-ahead logging (WAL) in database systems. In the proposed scheme, for backup, we devise a backup system to use log data generated by the existing WAL to eliminate the additional I/O operations. To restore a backup, we utilize and optimize the existing crash recovery procedure of WAL to reduce recovery time. For example, we divide the recovery range and applying the backup data for each range independently via multiple threads. We implement our scheme in MySQL, a popular database management system. The experimental result demonstrates that the proposed scheme provides instant backup while reducing recovery time compared with the existing schemes.

Practical activities usually require the ability to simultaneously work with internal, distributed information resources and access to the Internet. The need to solve this problem necessitates the use of appropriate administrative and technical methods to protect information. Such methods relate to the idea of domain isolation. This paper considers the principles of implementation and properties of an "Endpoint Cloud Terminal" that is general-purpose software tool with built-in security instruments. This apparatus solves the problem by combining an arbitrary number of isolated and independent workplaces on one hardware unit, a personal computer.

Software} maintainers employ reverse-engineered sequence diagrams to visually understand software behavior, especially when software documentation is absent or outdated. Much research has studied the adoption of reverse-engineered sequence diagrams to visualize program interactions. However, due to the forward-engineering nature of sequence diagrams, visualizing more complex programming scenarios can be challenging. In particular, sequence diagrams represent method invocations as unidirectional arrows. However, in practice, source code may contain compound method invocations that share values/objects implicitly. For example, method invocations can be nested, e.g., fun (foo ()), or chained, e.g., fun (). foo (). The standard notation of sequence diagrams does not have enough expressive power to precisely represent compound scenarios of method invocations. Understanding the flow of information between method invocations simplifies debugging, inspection, and exception handling operations for software maintainers. Despite the research invested to address the limitations of UML sequence diagrams, previous approaches fail to visualize compound scenarios of method invocations. In this paper, we propose sequence diagram extensions to enhance the visualization of (i) three widely used types of compound method invocations in practice (i.e., nested, chained, and recursive) and (ii) lifelines of objects returned from method invocations. We aim through our extensions to increase the level of abstraction and expressiveness of method invocation code. We develop a tool to reverse engineer compound method invocations and generate the corresponding extended sequence diagrams. We evaluate how our proposed extensions can improve the understandability of program interactions using a controlled experiment. We find that program interactions are significantly more comprehensible when visualized using our extensions.

Self-sovereign identities provide user autonomy and immutability to individual identities and full control to their identity owners. The immutability and control are possible by implementing identities in a decentralized manner on blockchains that are specially designed for identity operations such as Hyperledger Indy. As with any type of identity, self-sovereign identities too deal with Personally Identifiable Information (PII) of the identity holders and comes with the usual risks of privacy and security. This study examined certain scenarios of personal data disclosure via credential exchanges between such identities and risks of man-in-the-middle attacks in the blockchain based identity system Hyperledger Indy. On the basis of the findings, the paper proposes the following enhancements: 1) A novel attribute sensitivity score model for self-sovereign identity agents to ascertain the sensitivity of attributes shared in credential exchanges 2) A method of mitigating man-in-the-middle attacks between peer self-sovereign identities and 3) A novel quantitative model for determining a credential issuer's reputation based on the number of issued credentials in a window period, which is then utilized to calculate an overall confidence level score for the issuer.

Despite advances regarding autonomous functionality for robots, teleoperation remains a means for performing delicate tasks in safety critical contexts like explosive ordnance disposal (EOD) and ambiguous environments. Immersive stereoscopic displays have been proposed and developed in this regard, but bring about their own specific problems, e.g., simulator sickness. This work builds upon standardized test environments to yield reproducible comparisons between different robotic platforms. The focus was placed on testing three optronic systems of differing degrees of immersion: (1) A laptop display showing multiple monoscopic camera views, (2) an off-the-shelf virtual reality headset coupled with a pantilt-based stereoscopic camera, and (3) a so-called Telepresence Unit, providing fast pan, tilt, yaw rotation, stereoscopic view, and spatial audio. Stereoscopic systems yielded significant faster task completion only for the maneuvering task. As expected, they also induced Simulator Sickness among other results. However, the amount of Simulator Sickness varied between both stereoscopic systems. Collected data suggests that a higher degree of immersion combined with careful system design can reduce the to-be-expected increase of Simulator Sickness compared to the monoscopic camera baseline while making the interface subjectively more effective for certain tasks.

The current exploratory study examined software programmer trust in binary analysis techniques used to evaluate and understand binary code components. Experienced software developers participated in knowledge elicitations to identify factors affecting trust in tools and methods used for understanding binary code behavior and minimizing potential security vulnerabilities. Developer perceptions of trust in those tools to assess implementation risk in binary components were captured across a variety of application contexts. The software developers reported source security and vulnerability reports provided the best insight and awareness of potential issues or shortcomings in binary code. Further, applications where the potential impact to systems and data loss is high require relying on more than one type of analysis to ensure the binary component is sound. The findings suggest binary analysis is viable for identifying issues and potential vulnerabilities as part of a comprehensive solution for understanding binary code behavior and security vulnerabilities, but relying simply on binary analysis tools and binary release metadata appears insufficient to ensure a secure solution.

In DDoS attack (Distributed Denial of Service), an attacker gains control of many network users by a virus. Then the controlled users send many requests to a victim, leading to lack of its resources. DDoS attacks are hard to defend because of distributed nature, large scale and various attack techniques. One of possible ways of defense is to place sensors in the network that can detect and stop an unwanted request. However, such sensors are expensive so there is a natural question about a minimum number of sensors and their optimal placement to get the required level of safety. We present two mixed integer models for optimal sensor placement against DDoS attacks. Both models lead to a trade-off between the number of deployed sensors and the volume of uncontrolled flow. Since above placement problems are NP-hard, two efficient heuristics are designed, implemented and compared experimentally with exact linear programming solvers.

Recent work has shown the impact of adversarial machine learning on deep neural networks (DNNs) developed for Radio Frequency Machine Learning (RFML) applications. While these attacks have been shown to be successful in disrupting the performance of an eavesdropper, they fail to fully support the primary goal of successful intended communication. To remedy this, a communications-aware attack framework was recently developed that allows for a more effective balance between the opposing goals of evasion and intended communication through the novel use of a DNN to intelligently create the adversarial communication signal. Given the near ubiquitous usage of for-ward error correction (FEC) coding in the majority of deployed systems to correct errors that arise, incorporating FEC in this framework is a natural extension of this prior work and will allow for improved performance in more adverse environments. This work therefore provides contributions to the framework through improved loss functions and design considerations to incorporate inherent knowledge of the usage of FEC codes within the transmitted signal. Performance analysis shows that FEC coding improves the communications aware adversarial attack even if no explicit knowledge of the coding scheme is assumed and allows for improved performance over the prior art in balancing the opposing goals of evasion and intended communications.

Database outsourcing is one of the important utilities in cloud computing in which the Information Proprietor (IP) transfers the database administration to the Cloud Service Provider (CSP) in order to minimize the administration cost and preservation expenses of the database. Inspite of its immense profit, it undergoes few security issues such as privacy of deployed database and provability of search results. In the recent past, few of the studies have been carried out on provability of search results of Outsourced Database (ODB) that affords correctness and completeness of search results. But in the existing schemes, since there is flow of data between the Information Proprietor and the clients frequently, huge communication cost prevails at the Information Proprietor side. To address this challenge, in this paper we propose Efficient Auditing for Outsourced Database with Token Enforced Cloud Storage (EAODBT). The proposed scheme reduces the large communication cost prevailing at the Information Proprietor side and achieves correctness and completeness of search results even if the mischievous CSP knowingly sends a null set. Experimental analysis show that the proposed scheme has totally reduced the huge communication cost prevailing between Information Proprietor and clients, and simultaneously achieves the correctness and completeness of search results.

Safety and security of complex critical infrastructures are very important for economic, environmental and social reasons. The complexity of these systems introduces difficulties in the identification of safety and security risks that emerge from interdisciplinary interactions and dependencies. The discovery of safety and security design weaknesses late in the design process and during system operation can lead to increased costs, additional system complexity, delays and possibly undesirable compromises to address safety and security weaknesses.

The edge and fog computing paradigms enable more responsive and smarter systems without relying on cloud servers for data processing and storage. This reduces network load as well as latency. Nonetheless, the addition of new layers in the network architecture increases the number of security vulnerabilities. In privacy-critical systems, the appearance of new vulnerabilities is more significant. To cope with this issue, we propose and implement an Ethereum Blockchain based architecture with edge artificial intelligence to analyze data at the edge of the network and keep track of the parties that access the results of the analysis, which are stored in distributed databases.

Proof-of-Work (PoW) is a popular protocol used in Blockchain systems to resolve double-spending problems. However, if an attacker has access to calculation hash power greater than half of the total hash power, this attacker can create a double-spending attack or 51% attack. The cost of creating a 51% attack is surprisingly low if hash power is abundantly available. That posts a great threat to lots of PoW blockchains. We propose a technique to combine history weighted information of miners with the total calculation difficulty to alleviate the 51% attack problem. Analysis indicates that with the new technique, the cost of a traditional attack is increased by two orders of magnitude.

In this article analysis of the effectiveness of the acoustic algorithm of the fingerprint in the conditions of various acoustic disturbances is presented and described. The described algorithm is stable and should identify music even in the presence of acoustic disturbances. This was checked in a series of tests in four different conditions: silence, street noise, noise from the railway station, noise from inside the moving car during rain. In the case of silence, 10 measurements were taken lasting 7 seconds each. For each of the remaining conditions, 21 attempts were made to identify the work. The capture time for each of the 21 trials was 7 seconds. Every 7 attempts were changed noise volume. Subsequently, they were disruptions at a volume lower than the volume of the intercepted song, another 7 with an altitude similar to the intercepted track, and the last with a much higher volume. The effectiveness of the algorithm was calculated for two different times, and general - for the average of two results. Base of "fingerprints" consisted of 20 previously analyzed music pieces belonging to different musical genres.

With the rapid development of location-aware mobile devices, location-based services have been widely used. When LBS (Location Based Services) bringing great convenience and profits, it also brings great hidden trouble, among which user privacy security is one of them. The paper builds a LBS privacy protection model and develops algorithm depend on the technology of one dimensional coding of Geohash geographic information. The results of experiments and data measurements show that the model the model has reached k-anonymity effect and has good performance in avoiding attacking from the leaked information in a continuous query with the user's background knowledge. It also has a preferable performance in time cost of system process.

Most of the security algorithms proposed for the sensor networks such as secure routing, data encryption and authentication, and intrusion detection target protecting the content of the collected data from being exposed to different types of attacks. However, the context of the collected data, such as event occurrence, event time, and event location, is not addressed by these security mechanisms and can still be leaked to the adversaries. Therefore, we propose in this paper a novel and efficient unobservability scheme for source/sink location privacy for wireless multimedia sensor networks. The proposed privacy scheme is based on a cross-layer design between the application and routing layers in order to exploit the multimedia processing technique with multipath routing to hide the event occurrences and locations of important nodes without degrading the network performance. Simulation analysis shows that our proposed scheme satisfies the privacy requirements and has better performance compared to other existing techniques.

Protection of secured data content in volatile memories (processor caches, embedded RAMs etc) is essential in networking, wireless, automotive and other embedded secure applications. It is utmost important to protect secret data, like authentication credentials, cryptographic keys etc., stored over volatile memories which can be hacked during normal device operations. Several security attacks like cold boot, disclosure attack, data remanence, physical attack, cache attack etc. can extract the cryptographic keys or secure data from volatile memories of the system. The content protection of memory is typically done by assuring data deletion in minimum possible time to minimize data remanence effects. In today's state-of-the-art SoCs, dedicated hardwares are used to functionally erase the private memory contents in case of security violations. This paper, in general, proposes a novel approach of using existing memory built-in-self-test (MBIST) hardware to zeroize (initialize memory to all zeros) on-chip memory contents before it is being hacked either through different side channels or secuirty attacks. Our results show that the proposed MBIST based content zeroization approach is substantially faster than conventional techniques. By adopting the proposed approach, functional hardware requirement for memory zeroization can be waived.

Cyber-event-triggered power grid blackout compels utility operators to intensify cyber-aware and physics-constrained recovery and restoration process. Recently, coordinated cyber attacks on the Ukrainian grid witnessed such a cyber-event-triggered power system blackout. Various cyber-physical system (CPS) testbeds have attempted with multitude designs to analyze such interdependent events and evaluate remedy measures. However, resource constraints and modular integration designs have been significant barriers while modeling large-scale grid models (scalability) and multi-grid isolated models (concurrency) under a single real-time execution environment for the hardware-in-the-loop (HIL) CPS security testbeds. This paper proposes a meticulous design and effective modeling for simulating large-scale grid models and multi-grid isolated models in a HIL realtime digital simulator environment integrated with industry-grade hardware and software systems. We have used our existing HIL CPS security testbed to demonstrate scalability by the realtime performance of a Texas-2000 bus US synthetic grid model and concurrency by the real-time performance of simultaneous ten IEEE-39 bus grid models and an IEEE-118 bus grid model. The experiments demonstrated significant results by 100% realtime performance with zero overruns, low latency while receiving and executing control signals from SEL Relays via IEC-61850 protocol and low latency while computing and transmitting grid data streams including stability measures via IEEE C37.118 synchrophasor data protocol to SEL Phasor Data Concentrators.

In recent years, cloud computing has gained lots of importance and is being used in almost all applications in terms of various services. One of the most widely used service is storage as a service. Even though the stored data can be accessed from anytime and at any place, security of such data remains a prime concern of storage server as well as data owner. It may possible that the stored data can be altered or deleted. Therefore, it is essential to verify the correctness of data (auditing) and an agent termed as Third Party Auditor (TPA) can be utilised to do so. Existing auditing approaches have their own strengths and weakness. Hence, it is essential to propose auditing scheme which eliminates limitations of existing auditing mechanisms. Here we are proposing public auditing scheme which supports data dynamics as well as preserves privacy. Data owner, TPA, and cloud server are integral part of any auditing mechanism. Data in the form of various blocks are encoded, hashed, concatenated and then signature is calculated on it. This scheme also supports data dynamics in terms of addition, modification and deletion of data. TPA reads encoded data from cloud server and perform hashing, merging and signature calculation for checking correctness of data. In this paper, we have proposed efficient privacy preserving and dynamic public auditing by utilizing Merkle Hash Tree (MHT) for indexing of encoded data. It allows updating of data dynamically while preserving data integrity. It supports data dynamics operations like insert, modify and deletion. Several users can request for storage correctness simultaneously and it will be efficiently handled in the proposed scheme. It also minimizes the communication and computing cost. The proposed auditing scheme is experimented and results are evaluated considering various block size and file size parameters.

Mobile Ad hoc Network (MANET) routing is basic and route selection ought to be made faster before the node leaves the system. MANET routing Methods are intended to work in a friendly and satisfying condition which makes them helpless against different attacks. MANET is one of the most encouraging fields for innovative work of remote system. MANET has now turned out to be one of the most lively and dynamic field of communication among systems. A MANET is a self-sufficient gathering of mobile nodes that speak with one another over remote connections and coordinate in an appropriated way so as to give the fundamental system convenience without a fixed framework. MANET has transfer speed limitations yet it permits self-ruling communication of versatile clients over it. Because of regular node mobility, and along these lines change in route topology, the architecture of the system goes unpredicted after some time. In such a decentralized situation, secured route identification is a key task for communication among nodes. Trust calculation among nodes is done for involving trusted nodes in route discovery process. In this manuscript, a novel secure routing method is proposed which identifies route among trusted nodes and update the routing table info frequently because of dynamic topology of the network. The outcomes demonstrate that the proposed method takes better routing technique when compared with existing methods.

Machine Learning as a Service (MLaaS) is a growing paradigm in the Machine Learning (ML) landscape. More and more ML models are being uploaded to the cloud and made accessible from all over the world. Creating good ML models, however, can be expensive and the used data is often sensitive. Recently, Secure Multi-Party Computation (SMPC) protocols for MLaaS have been proposed, which protect sensitive user data and ML models at the expense of substantially higher computation and communication than plaintext evaluation. In this paper, we show that for a subset of ML models used in MLaaS, namely Support Vector Machines (SVMs) and Support Vector Regression Machines (SVRs) which have found many applications to classifying multimedia data such as texts and images, it is possible for adversaries to passively extract the private models even if they are protected by SMPC, using known and newly devised model extraction attacks. We show that our attacks are not only theoretically possible but also practically feasible and cheap, which makes them lucrative to financially motivated attackers such as competitors or customers. We perform model extraction attacks on the homomorphic encryption-based protocol for privacy-preserving SVR-based indoor localization by Zhang et al. (International Workshop on Security 2016). We show that it is possible to extract a highly accurate model using only 854 queries with the estimated cost of \$0.09 on the Amazon ML platform, and our attack would take only 7 minutes over the Internet. Also, we perform our model extraction attacks on SVM and SVR models trained on publicly available state-of-the-art ML datasets.

Tor's anonymity network is one of the most widely used anonymity networks online, it consists of thousands of routers run by volunteers. Tor preserves the anonymity of its users by relaying the traffic through a number of routers (called onion routers) forming a circuit. The current design of Tor's transport layer suffers from a number of problems affecting the performance of the network. Several researches proposed changes in the transport design in order to eliminate the effect of these problems and improve the performance of Tor's network. In this paper. we propose "QuicTor", an improvement to the transport layer of Tor's network by using Google's protocol "QUIC" instead of TCP. QUIC was mainly developed to eliminate TCP's latency introduced from the handshaking delays and the head-of-line blocking problem. We provide an empirical evaluation of our proposed design and compare it to two other proposed designs, IMUX and PCTCP. We show that QuicTor significantly enhances the performance of Tor's network.