Biblio

This paper proposes a novel concept for an electric generator in which both ac windings and permanent magnets (PMs) are placed in the stator. Concentrated windings with a special pattern and phase coils placed in separate slots are employed. The PMs are positioned in a spoke-type field concentrating arrangement, which provides high flux intensification and enables the use of lower remanence and energy non-rare earth magnets. The rotor is exterior to the stator and has a simple and robust reluctance-type configuration without any active electromagnetic excitation components. The principle of operation is introduced based on the concept of virtual work with closed-form analytical airgap flux density distributions. Initial and parametric design studies were performed using electromagnetic FEA for a 3MW direct-drive wind turbine generator employing PMs of different magnetic remanence and specific energy. Results include indices for the goodness of excitation and the goodness of the electric machine designs; loss; and efficiency estimations, indicating that performance comparable to PM synchronous designs employing expensive and critical supply rare-earth PMs may be achieved with non-rare earth PMs using the proposed configuration.

Today billions of people are accessing the internet around the world. There is a need for new technology to provide security against malicious activities that can take preventive/ defensive actions against constantly evolving attacks. A new generation of technology that keeps an eye on such activities and responds intelligently to them is the intrusion detection system employing machine learning. It is difficult for traditional techniques to analyze network generated data due to nature, amount, and speed with which the data is generated. The evolution of advanced cyber threats makes it difficult for existing IDS to perform up to the mark. In addition, managing large volumes of data is beyond the capabilities of computer hardware and software. This data is not only vast in scope, but it is also moving quickly. The system architecture suggested in this study uses SVM to train the model and feature selection based on the information gain ratio measure ranking approach to boost the overall system's efficiency and increase the attack detection rate. This work also addresses the issue of false alarms and trying to reduce them. In the proposed framework, the UNSW-NB15 dataset is used. For analysis, the UNSW-NB15 and NSL-KDD datasets are used. Along with SVM, we have also trained various models using Naive Bayes, ANN, RF, etc. We have compared the result of various models. Also, we can extend these trained models to create an ensemble approach to improve the performance of IDS.

The intrusion detection systems are vital for the sustainability of Cooperative Intelligent Transportation Systems (C-ITS) and the detection of sybil attacks are particularly challenging. In this work, we propose a novel approach for the detection of sybil attacks in C-ITS environments. We provide an evaluation of our approach using extensive simulations that rely on real traces, showing our detection approach's effectiveness.

This paper investigates the robustness of the received signal strength (RSS)-based physical layer authentication (PLA) for wireless mesh networks, through experimental results. Specifically, we develop a secure wireless mesh networking framework and apply the RSS-based PLA scheme, with the aim to perform continuous authentication. The mesh setup comprises three Raspberry-PI4 computing nodes (acting as Alice, Bob, and Eve) and a server. The server role is to perform the initial authentication when a new node joins the mesh network. After that, the legitimate nodes in the mesh network perform continuous authentication, by leveraging the RSS feature of wireless signals. In particular, Bob tries to authenticate Alice in the presence of Eve. The performance of the presented framework is quantified through extensive experimental results in an outdoor environment, where various nodes' positions, relative distances, and pedestrian speeds scenarios are considered. The obtained results demonstrate the robustness of the underlying model, where an authentication rate of 99% for the static case can be achieved. Meanwhile, at the pedestrian speed, the authentication rate can drop to 85%. On the other hand, the detection rate improves when the distance between the legitimate and wiretap links is large (exceeds 20 meters) or when Alice and Eve are moving in different mobility patterns.

Unit Commitment (UC) problem is one of the most fundamental constrained optimization problems in the planning and operation of electric power systems and electricity markets. Solving a large-scale UC problem requires a lot of computational effort which can be improved using data driven approaches. In practice, a UC problem is solved multiple times a day with only minor changes in the input data. Hence, this aspect can be exploited by using the historical data to solve the problem. In this paper, an Artificial Intelligence (AI) based approach is proposed to solve a Security Constrained UC problem. The proposed algorithm was tested through simulations on a 4-bus power system and satisfactory results were obtained. The results were compared with those obtained using IBM CPLEX MIQP solver.

This article is devoted to the existing standards for assessing the state of information security, which provides a classification and comparative analysis of standards for assessing the state of information.

The article describes an analytical model of the actions of an information security violator for the secret extraction of confidential information processed on the protected object in terms of the theory of Markov random processes. The characteristics of the existing models are given, as well as the requirements that are imposed on the model for simulating the process. All model states are described in detail, as well as the data flow that is used in the process simulation. The model is represented as a directed state graph. It also describes the option for evaluating the data obtained during modeling. In the modern world, with the developing methods and means of covert extraction of information, the problem of assessing the damage that can be caused by the theft of the organization's data is acute. This model can be used to build a model of information security threats.

Automatic Static Analysis Tools (ASATs) detect coding rule violations, including mistakes and bad practices that frequently occur during programming. While ASATs are widely used in both OSS and industry, the developers do not resolve more than 80% of the detected violations. As one of the reasons, most ASATs users do not customize their ASATs to their projects after installation; the ASATs with the default configuration report many rule violations that confuse developers. To reduce the ratio of such uninteresting warning messages, we propose a method to customize ASATs according to the product source code automatically. Our fundamental hypothesis is: A software project has interesting ASAT rules that are consistent over time. Our method takes source code as input and generates an ASAT configuration. In particular, the method enables optional (i.e., disabled by default) rules that detected no violations on the version because developers are likely to follow the rules in future development. Our method also disables violated rules because developers were unlikely to follow them. To evaluate the method, we applied our method to 643 versions of four JavaScript projects. The generated configurations for all four projects increased the ASAT precision. They also increased recall for two projects. The result shows that our method helps developers to focus on their attractive rule violations. Our implementation of the proposed method is available at https://github.com/devreplay/linter-maintainer

This study focuses on the behavior of magnetic properties and electromagnetic absorption of MgFe2O4 prepared by powder metallurgy. Magnesium ferrite was synthesized using oxide precursors (MgO and Fe2 O3). The samples were calcined at 700 °C for 3 hours and sintered at 1100 °C for 24 hours with varying compaction pressure (80 kg/cm2, 90 kg/cm2, 100 kg/cm2). Magnesium ferrites were characterized using an X-Ray Diffraction (XRD) for their crystal structure analysis, a Scanning Electron Microscope equipped with an Energy Dispersive Spectroscopy (SEM-EDS) for their microstructure and elemental composition studies, a Permagraph for their magnetic properties, and a Vector Network Analysis (VNA) for their microwave absorption characteristics. XRD patterns shows primary phase of MgFe2O4 and secondary phase of Fe2 O3 present in all three samples. The SEM characterization reveal the microstructure of magnesium ferrite and the EDS spectra confirm the presence of Fe, Mg, and O. The hysteresis curves show that the values of remanence magnetic induction (Br) are 17.5 emu/g, 16.5 emu/g, and 14.5 emu/g, respective to the increasing compaction pressure. Saturation magnetization values are increasing whereas the coercivity values are found to have inconsistent change with increasing compaction pressure. According to VNA results, the values of reflection loss are -16.15 dB, -22.45 dB, and -27.55 dB, respectively.

As Edge deployments move closer towards the end devices, low latency communication among Edge aware applications is one of the key tenants of Edge service offerings. In order to simplify application development, service mesh architectures have emerged as the evolutionary architectural paradigms for taking care of bulk of application communication logic such as health checks, circuit breaking, secure communication, resiliency (among others), thereby decoupling application logic with communication infrastructure. The latency to throughput ratio needs to be measurable for high performant deployments at the Edge. Providing benchmark data for various edge deployments with Bare Metal and virtual machine-based scenarios, this paper digs into architectural complexities of deploying service mesh at edge environment, performance impact across north-south and east-west communications in and out of a service mesh leveraging popular open-source service mesh Istio/Envoy using a simple on-prem Kubernetes cluster. The performance results shared indicate performance impact of Kubernetes network stack with Envoy data plane. Microarchitecture analyses indicate bottlenecks in Linux based stacks from a CPU micro-architecture perspective and quantify the high impact of Linux's Iptables rule matching at scale. We conclude with the challenges in multiple areas of profiling and benchmarking requirement and a call to action for deploying a service mesh, in latency sensitive environments at Edge.

With the rapid growth of Internet of Things (IoT) network intrusion attacks, there is a critical need for sophisticated and comprehensive intrusion detection systems (IDSs). Classifying infrequent intrusion types such as root-to-local (R2L) and user-to-root (U2R) attacks is a reoccurring problem for IDSs. In this study, various data sampling and class balancing techniques-Generative Adversarial Network (GAN)-based oversampling, k-nearest-neighbor (kNN) oversampling, NearMiss-1 undersampling, and class weights-were used to resolve the severe class imbalance affecting U2R and R2L attacks in the NSL-KDD intrusion detection dataset. Artificial Neural Networks (ANNs) were trained on the adjusted datasets, and their performances were evaluated with a multitude of classification metrics. Here, we show that using no data sampling technique (baseline), GAN-based oversampling, and NearMiss-l undersampling, all with class weights, displayed high performances in identifying R2L and U2R attacks. Of these, the baseline with class weights had the highest overall performance with an F1-score of 0.11 and 0.22 for the identification of U2R and R2L attacks, respectively.

Dew Computing (DC) is a comparatively modern field with a wide range of applications. By examining how technological advances such as fog, edge and Dew computing, and distributed intelligence force us to reconsider traditional Cloud Computing (CC) to serve the Internet of Things. A new dew estimation theory is presented in this article. The revised definition is as follows: DC is a software and hardware cloud-based company. On-premises servers provide autonomy and collaborate with cloud networks. Dew Calculation aims to enhance the capabilities of on-premises and cloud-based applications. These categories can result in the development of new applications. In the world, there has been rapid growth in Information and Communication Technology (ICT), starting with Grid Computing (GC), CC, Fog Computing (FC), and the latest Edge Computing (EC) technology. DC technologies, infrastructure, and applications are described. We’ll go through the newest developments in fog networking, QoE, cloud at the edge, platforms, security, and privacy. The dew-cloud architecture is an option concerning the current client-server architecture, where two servers are located at opposite ends. In the absence of an Internet connection, a dew server helps users browse and track their details. Data are primarily stored as a local copy on the dew server that starts the Internet and is synchronized with the cloud master copy. The local dew pages, a local online version of the current website, can be browsed, read, written, or added to the users. Mapping between different Local Dew sites has been made possible using the dew domain name scheme and dew domain redirection.

As the major factors affecting the safety of deep learning models, corner cases and related detection are crucial in AI quality assurance for constructing safety- and security-critical systems. The generic corner case researches involve two interesting topics. One is to enhance DL models' robustness to corner case data via the adjustment on parameters/structure. The other is to generate new corner cases for model retraining and improvement. However, the complex architecture and the huge amount of parameters make the robust adjustment of DL models not easy, meanwhile it is not possible to generate all real-world corner cases for DL training. Therefore, this paper proposes a simple and novel approach aiming at corner case data detection via a specific metric. This metric is developed on surprise adequacy (SA) which has advantages on capture data behaviors. Furthermore, targeting at characteristics of corner case data, three modifications on distanced-based SA are developed for classification applications in this paper. Consequently, through the experiment analysis on MNIST data and industrial data, the feasibility and usefulness of the proposed method on corner case data detection are verified.

Cyberattacks on cyber supply chain (CSC) systems and the cascading impacts have brought many challenges and different threat levels with unpredictable consequences. The embedded networks nodes have various loopholes that could be exploited by the threat actors leading to various attacks, risks, and the threat of cascading attacks on the various systems. Key factors such as lack of common ontology vocabulary and semantic interoperability of cyberattack information, inadequate conceptualized ontology learning and hierarchical approach to representing the relationships in the CSC security domain has led to explicit knowledge representation. This paper explores cyberattack ontology learning to describe security concepts, properties and the relationships required to model security goal. Cyberattack ontology provides a semantic mapping between different organizational and vendor security goals has been inherently challenging. The contributions of this paper are threefold. First, we consider CSC security modelling such as goal, actor, attack, TTP, and requirements using semantic rules for logical representation. Secondly, we model a cyberattack ontology for semantic mapping and knowledge representation. Finally, we discuss concepts for threat intelligence and knowledge reuse. The results show that the cyberattack ontology concepts could be used to improve CSC security.

The rapid progress experienced in the Internet of Things (IoT) space is one that has introduced new and unique challenges for cybersecurity and IoT-Forensics. One of these problems is how digital forensics and incident response (DFIR) are handled in IoT. Since enormous users use IoT platforms to accomplish their day to day task, massive amounts of data streams are transferred with limited hardware resources; conducting DFIR needs a new approach to mitigate digital evidence and incident response challenges owing to the facts that there are no unified standard or classified principles for IoT forensics. Today's IoT DFIR relies on self-defined best practices and experiences. Given these challenges, IoT-related incidents need a more structured approach in identifying problems of DFIR. In this paper, we examined the major DFIR challenges in IoT by exploring the different phases involved in a DFIR when responding to IoT-related incidents. This study aims to provide researchers and practitioners a road-map that will help improve the standards of IoT security and DFIR.

In recent years we can observe that digital forensics is being applied to a variety of domains as nearly any data can become valuable forensic evidence. The sheer scope of web-based investigations provides a vast amount of information. Due to a rapid increase in the number of cybercrimes the importance of application-specific forensics is greater than ever. Criminals use the application not only to communicate but also to facilitate crimes. It came to our attention that the gaming chat application Discord is one of them. Discord allows its users to send text messages as well as exchange image, video, and audio files. While Discord's community is not as large as that of the most popular messaging apps the stable growth of its userbase and recent incidents indicate that it is used by criminals. This paper presents our research into the digital forensic analysis of Discord client-side artefacts and presents experimental development of a tool for extraction, analysis, and presentation of the data from Discord application. The work then proposes a solution in form of a tool, `DiscFor', that can retrieve information from the application's local files and cache storage.

When location-based services (LBS) are delivered, location data should be protected against honest-but-curious LBS providers, them being quasi-identifiers. One of the existing approaches to achieving this goal is location k-anonymity, which leverages the presence of a trusted party, called location trusted service (LTS), playing the role of anonymizer. A drawback of this approach is that the location trusted service is a single point of failure and traces all the users. Moreover, the protection is completely nullified if a global passive adversary is allowed, able to monitor the flow of messages, as the source of the query can be identified despite location k-anonymity. In this paper, we propose a distributed and hierarchical LTS model, overcoming both the above drawbacks. Moreover, position notification is used as cover traffic to hide queries and multicast is minimally adopted to hide responses, to keep k-anonymity also against the global adversary, thus enabling the possibility that LBS are delivered within social networks.

Federated Identity Management (FIM) is a model of identity management in which different trusted organizations can provide secure online services to their uses. Security Assertion Markup Language (SAML) is one of the widely-used technologies for FIM. However, a SAML-based FIM has two significant issues: the metadata (a crucial component in SAML) has security issues, and federation management is hard to scale. The concept of dynamic identity federation has been introduced, enabling previously unknown entities to join in a new federation facilitating inter-organization service provisioning to address federation management's scalability issue. However, the existing dynamic federation approaches have security issues concerning confidentiality, integrity, authenticity, and transparency. In this paper, we present the idea of facilitating dynamic identity federations utilizing blockchain technology to improve the existing approaches' security issues. We demonstrate its architecture based on a rigorous threat model and requirement analysis. We also discuss its implementation details, current protocol flows and analyze its performance to underline its applicability.

This work presents a system which allows image data and extracted features from a real-world location to be captured and modelled in a Virtual Reality (VR) environment combined with Augmented Situated Visualizations (ASV) overlaid and registered in a virtual environment. Combining these technologies with techniques from Data Science and Artificial Intelligence (AI)(such as image analysis and 3D reconstruction) allows the creation of a setting where remote locations can be modelled and interacted with from anywhere in the world. This Enhanced Photorealistic Immersive (EPI) system is highly adaptable to a wide range of use cases and users as it can be utilized to model and interact with any environment which can be captured as image data (such as training for operation in hazardous environments, accessibility solutions for exploration of historical/tourism locations and collaborative learning environments). A use case example focused on a structural examination of railway tunnels along with a pilot study is presented, which can demonstrate the usefulness of the EPI system.

In the recent time due to advancement of technology, Malware and its clan have continued to advance and become more diverse. Malware otherwise Malicious Software consists of Virus, Trojan horse, Adware, Spyware etc. This said software leads to extrusion of data (Spyware), continuously flow of Ads (Adware), modifying or damaging the system files (Virus), or access of personal information (Trojan horse). Some of the major factors driving the growth of these attacks are due to poorly secured devices and the ease of availability of tools in the Internet with which anyone can attack any system. The attackers or the developers of Malware usually lean towards blending of malware into the executable file, which makes it hard to detect the presence of malware in executable files. In this paper we have done experimental study on various algorithms of Machine Learning for detecting the presence of Malware in executable files. After testing Naïve Bayes, KNN and SVM, we found out that SVM was the most suited algorithm and had the accuracy of 94%. We then created a web application where the user could upload executable file and test the authenticity of the said executable file if it is a Malware file or a benign file.

Authorship Attribution (AA) is currently applied in several applications, among which fraud detection and anti-plagiarism checks: this task can leverage stylometry and Natural Language Processing techniques. In this work, we explored some strategies to enhance the performance of an AA task for the automatic detection of false and misleading information (e.g., fake news). We set up a text classification model for AA based on stylometry exploiting recurrent deep neural networks and implemented two learning tasks trained on the same collection of fake and real news, comparing their performances: one is based on Federated Learning architecture, the other on a centralized architecture. The goal was to discriminate potential fake information from true ones when the fake news comes from heterogeneous sources, with different styles. Preliminary experiments show that a distributed approach significantly improves recall with respect to the centralized model. As expected, precision was lower in the distributed model. This aspect, coupled with the statistical heterogeneity of data, represents some open issues that will be further investigated in future work.

Tor is a famous routing overlay network based on the Onion multi-layered encryption to support communication anonymity in a threat model in which some network nodes are malicious. However, Tor does not provide any protection against the global passive adversary. In this threat model, an idea to obtain recipient anonymity, which is enough to have relationship anonymity, is to hide the recipient among a sufficiently large anonymity set. However, this would lead to high latency both in the set-up phase (which has a quadratic cost in the number of involved nodes) and in the successive communication. In this paper, we propose a way to arrange a Tor circuit with a tree-like topology, in which the anonymity set consists of all its nodes, whereas set-up and communication latency depends on the number of the sole branch nodes (which is a small fraction of all the nodes). Basically, the cost goes down from quadratic to linear. Anonymity is obtained by applying a broadcast-based technique for the forward message, and cover traffic (generated by the terminal-chain nodes) plus mixing over branch nodes, for the response.

Fitbit Versa is the most popular of its predecessors and successors in the Fitbit faction. Increasingly data stored on these smart fitness devices, their linked applications and cloud datacenters are being used for criminal convictions. There is limited research for investigators on wearable devices and specifically exploring evidence identification and methods of extraction. In this paper we present our analysis of Fitbit Versa using Cellebrite UFED and MSAB XRY. We present a clear scope for investigation and data significance based on the findings from our experiments. The data recovery will include logical and physical extractions using devices running Android 9 and iOS 12, comparing between Cellebrite and XRY capabilities. This paper discusses databases and datatypes that can be recovered using different extraction and analysis techniques, providing a robust outlook of data availability. We also discuss the accuracy of recorded data compared to planned test instances, verifying the accuracy of individual data types. The verifiable accuracy of some datatypes could prove useful if such data was required during the evidentiary processes of a forensic investigation.

Intrusion Detection System (IDS) is a system that could detect suspicious activity in a network. Two approaches are known for IDS, namely signature-based and anomaly-based. The anomaly-based detection method was chosen to detect suspicious and abnormal activity for the system that cannot be performed by the signature-based method. In this study, attack testing was carried out using three DoS tools, namely the LOIC, Torshammer, and Xerxes tools, with a test scenario using IDS and without IDS. From the test results that have been carried out, IDS has successfully detected the attacks that were sent, for the delivery of the most consecutive attack packages, namely Torshammer, Xerxes, and LOIC. In the detection of Torshammer attack tools on the target FTP Server, 9421 packages were obtained, for Xerxes tools as many as 10618 packages and LOIC tools as many as 6115 packages. Meanwhile, attacks on the target Web Server for Torshammer tools were 299 packages, for Xerxes tools as many as 530 packages, and for LOIC tools as many as 103 packages. The accuracy of the IDS performance results is 88.66%, the precision is 88.58% and the false positive rate is 63.17%.

The industry of telecommunications is being transformed towards 5G technology, because it has to deal with the emerging and existing use cases. Because, 5G wireless networks need rather large data rates and much higher coverage of the dense base station deployment with the bigger capacity, much better Quality of Service - QoS, and the need very low latency [1–3]. The provision of the needed services which are envisioned by 5G technologies need the new service models of deployment, networking architectures, processing technologies and storage to be defined. These technologies will cause the new problems for the cybersecurity of 5G systems and the security of their functionality. The developers and researchers working in this field make their best to secure 5G systems. The researchers showed that 5G systems have the security challenges. The researchers found the vulnerabilities in 5G systems which allow attackers to integrate malicious code into the system and make the different types of the illegitimate actions. MNmap, Battery drain attacks and MiTM can be successfully implemented on 5G. The paper makes the analysis of the existing cyber security problems in 5G technology. Based on the analysis, we suggest the novel Intrusion Detection System - IDS by means of the machine-learning algorithms. In the related papers the scientists offer to use NSL-KDD in order to train IDS. In our paper we offer to train IDS using the big datasets of DOS/DDOS attacks, besides of training using NSL-KDD. The research also offers the methodology of integration of the offered intrusion detection systems into an standard architecture of 5G. The paper also offers the pseudo code of the designed system.