Biblio

The rapid advancement of IoT technologies has led to its flexible adoption in battle field networks, known as Internet of Battlefield Things (IoBT) networks. One important application of IoBT networks is the weather sensory network characterized with a variety of weather, land and environmental sensors. This data contains hidden trends and correlations, needed to provide situational awareness to soldiers and commanders. To interpret the incoming data in real-time, machine learning algorithms are required to automate strategic decision-making. Existing solutions are not well-equipped to provide the fine-grained feedback to military personnel and cannot facilitate a scalable, end-to-end platform for fast unlabeled data collection, cleaning, querying, analysis and threats identification. In this work, we present a scalable end-to-end IoBT data driven platform for SVAD (Storage, Visualization, Anomaly Detection) analysis of heterogeneous weather sensor data. Our SVAD platform includes extensive data cleaning techniques to denoise efficiently data to differentiate data from anomalies and noise data instances. We perform comparative analysis of unsupervised machine learning algorithms for multi-variant data analysis and experimental evaluation of different data ingestion pipelines to show the ability of the SVAD platform for (near) real-time processing. Our results indicate impending turbulent weather conditions that can be detected by early anomaly identification and detection techniques.

A tamper-resistant logical operation method based on integrated nanophotonics is proposed focusing on electromagnetic side-channel attacks. In the proposed method, only the phase of each optical signal is modulated depending on its logical state, which keeps the power of optical signals in optical logic circuits constant. This provides logic-gate-level tamper resistance which is difficult to achieve with CMOS circuits. An optical implementation method based on electronically-controlled phase shifters is then proposed. The electrical part of proposed circuits achieves 300 times less instantaneous current change, which is proportional to intensity of the leaked electromagnetic wave, than a CMOS logic gate.

The rapid rise of the Dark Web and supportive technologies has served as the backbone facilitating online illegal activity worldwide. These illegal activities supported by anonymisation technologies such as Tor has made it increasingly elusive to law enforcement agencies. Despite several successful law enforcement operations, illegal activity on the Dark Web is still growing. There are approaches to monitor, mine, and research the Dark Web, all with varying degrees of success. Given the complexity and dynamics of the services offered, we recognize the need for in depth analysis of the Dark Web with regard to its infrastructures, actors, types of abuse and their relationships. This involves the challenging task of information extraction from the very heterogeneous collection of web pages that make up the Dark Web. Most providers develop their services on top of standard frameworks such as WordPress, Simple Machine Forum, phpBB and several other frameworks to deploy their services. As a result, these service providers publish significant number of pages based on similar structural and stylistic templates. We propose an efficient, scalable, repeatable and accurate approach to cluster Dark Web pages based on those structural and stylistic features. Extracting relevant information from those clusters should make it feasible to conduct in depth Dark Web analysis. This paper presents our clustering algorithm to accelerate information extraction, and as a result improve attribution of digital traces to infrastructures or individuals in the fight against cyber crime.

The aim of the study is to present an advanced overview and potential application of the innovative tools/software's/methods used for data visualization, text mining, scientific mapping, and bibliometric analysis. Text mining and data visualization has been a topic of research for several years for academic researchers and practitioners. With the advancement in technology and innovation in the data analysis techniques, there are many online and offline software tools available for text mining and visualisation. The purpose of this study is to present an advanced overview of latest, sophisticated, and innovative tools available for this purpose. The unique characteristic about this study is that it provides an overview with examples of the five most adopted software tools such as VOSviewer, Biblioshiny, Gephi, HistCite and CiteSpace in social science research. This study will contribute to the academic literature and will help the researchers and practitioners to apply these tools in future research to present their findings in a more scientific manner.

This paper describes the architecture and the fundamental methodology of an anomaly detector, which by continuously monitoring Simple Network Management Protocol data and by processing it as complex-events, is able to timely recognize patterns of faults and relevant cyber-attacks. This solution has been applied in the context of smart grids, and in particular as part of a security and resilience component of the Information and Communication Technologies (ICT) Gateway, a middleware-based architecture that correlates and fuses measurement data from different sources (e.g., Inverters, Smart Meters) to provide control coordination and to enable grid observability applications. The detector has been evaluated through experiments, where we selected some representative anomalies that can occur on the ICT side of the energy distribution infrastructure: non-malicious faults (indicated by patterns in the system resources usage), as well as effects of typical cyber-attacks directed to the smart grid infrastructure. The results show that the detection is promisingly fast and efficient.

The fields of transportation and fleet management have been evolving at a rapid pace and most of these changes are due to numerous incremental developments in the area. However, a comprehensive study that critically compares and contrasts all the existing techniques and methodologies in the area is still missing. This paper presents a comparative analysis of the vulnerabilities and security threats for IoT and their mitigation strategies in the context of transportation and fleet management. Moreover, we attempt to classify the existing strategies based on their underlying principles.

The secure functioning of automotive systems is vital to the safety of their passengers and other roadway users. One of the critical functions for safety is the controller area network (CAN), which interconnects the safety-critical electronic control units (ECUs) in the majority of ground vehicles. Unfortunately CAN is known to be vulnerable to several attacks. One such attack is the bus-off attack, which can be used to cause a victim ECU to disconnect itself from the CAN bus and, subsequently, for an attacker to masquerade as that ECU. A limitation of the bus-off attack is that it requires the attacker to achieve tight synchronization between the transmission of the victim and the attacker's injected message. In this paper, we introduce a schedule-based attack framework for the CAN bus-off attack that uses the real-time schedule of the CAN bus to predict more attack opportunities than previously known. We describe a ranking method for an attacker to select and optimize its attack injections with respect to criteria such as attack success rate, bus perturbation, or attack latency. The results show that vulnerabilities of the CAN bus can be enhanced by schedule-based attacks.

With the ever-increasing energy demand and enormous development of generation capacity, modern bulk power systems are mostly pushed to operate with narrower security boundaries. Therefore, timely and reliable assessment of power system security is an inevitable necessity to prevent widespread blackouts and cascading outages. In this paper, a new white-box decision tree-based preventive strategy is presented to evaluate and enhance the power system dynamic security versus the credible N-K contingencies originating from transient instabilities. As well, a competent operating measure is expertly defined to detect and identify the islanding and non-islanding conditions with the aid of a wide-area phasor measurement system. The newly developed strategy is outlined by a three-level simulation with the aim of guaranteeing the power system dynamic security. In the first-level, six hundred islanding and non-islanding scenarios are generated using an enhanced version of the ID3 algorithm, referred to as the C4.5 algorithms. In the second-level, optimal C4.5 decision trees are offline trained based on operating parameters achieved by the reduction error pruning method. In the third level, however, all trained decision trees are rigorously investigated offline and online; and then, the most accurate and reliable decision tree is selected. The newly developed strategy is examined on the IEEE New England 39-bus test system, and its effectiveness is assured by simulation studies.

The total number of photovoltaic power producing facilities whose FIT-based ten-year contract expires by 2023 is expected to reach approximately 1.65 million in Japan. If the number of renewable electricity-producing/consuming facilities reached two million, an enormous number of transactions would be invoked beyond blockchain's scalability.We propose mutually cooperative two novel methods to simultaneously solve scalability, data size, and privacy problems in blockchain-based trading platforms for renewable energy environmental value. One is a management scheme of electricity production resources (EPRs) using an extended UTXO token. The other is a data aggregation scheme that aggregates a significant number of smart meter records with evidentiality using zero-knowledge proof (ZKP).

One of the most innovative directions in the Internet is Information Centric Networks, in particular the Named Data Network. This approach should make it easier to find and retrieve the desired information on the network through name-based addressing, intranet caching and other schemes. This article presents Named Data Network modeling, results and performance evaluation of proposed caching policies for Named Data Network research, taking into account the influence of external factors on base of Zipf's law and uniform distribution.

This demo paper presents the technical details and usage scenarios of μSE: a mutation-based tool for evaluating security-focused static analysis tools for Android. Mutation testing is generally used by software practitioners to assess the robustness of a given test-suite. However, we leverage this technique to systematically evaluate static analysis tools and uncover and document soundness issues.μSE's analysis has found 25 previously undocumented flaws in static data leak detection tools for Android.μSE offers four mutation schemes, namely Reachability, Complex-reachability, TaintSink, and ScopeSink, which determine the locations of seeded mutants. Furthermore, the user can extend μSE by customizing the API calls targeted by the mutation analysis.μSE is also practical, as it makes use of filtering techniques based on compilation and execution criteria that reduces the number of ineffective mutations.

We present a novel approach for the collaborative training of neural network models in decentralized federated environments. In the iterative process a group of autonomous peers run multiple training rounds to train a common model. Thereby, participants perform all model training steps locally, such as stochastic gradient descent optimization, using their private, e.g. mission-critical, training datasets. Based on locally updated models, participants can jointly determine a common model by averaging all associated model weights without sharing the actual weight values. For this purpose we introduce a simple n-out-of-n secret sharing schema and an algorithm to calculate average values in a peer-to-peer manner. Our experimental results with deep neural networks on well-known sample datasets prove the generic applicability of the approach, with regard to model quality parameters. Since there is no need to involve a central service provider in model training, the approach can help establish trustworthy collaboration platforms for businesses with high security and data protection requirements.

Quality assurance and food safety are the most problem that the consumers are special care. To solve this problem, the enterprises must improve their food supply chain management system. In addition to tracking and storing orders and deliveries, it also ensures transparency and traceability of food production and transportation. This is a big challenge that the food supply chain system using the client-server model cannot meet with the requirements. Blockchain was first introduced to provide distributed records of digital currency exchanges without reliance on centralized management agencies or financial institutions. Blockchain is a disruptive technology that can improve supply chain related transactions, enable to access data permanently, data security, and provide a distributed database. In this paper, we propose a method to design a food supply chain management system base on Blockchain technology that is capable of bringing consumers’ trust in food traceability as well as providing a favorable supply and transaction environment. Specifically, we design a system architecture that is capable of controlling and tracking the entire food supply chain, including production, processing, transportation, storage, distribution, and retail. We propose the KDTrace system model and the Channel of KDTrace network model. The Smart contract between the organizations participating in the transaction is implemented in the Channel of KDTrace network model. Therefore, our supply chain system can decrease the problem of data explosion, prevent data tampering and disclosure of sensitive information. We have built a prototype based on Hyperledger Fabric Blockchain. Through the prototype, we demonstrated the effectiveness of our method and the suitability of the use cases in a supply chain. Our method that uses Blockchain technology can improve efficiency and security of the food supply chain management system compared with traditional systems, which use a clientserver model.

Due to the computing capability limitation of the Internet of things devices in the perception layer, the traditional security solutions are difficult to be used directly. How to design a new lightweight, secure and reliable protocol suitable for the Internet of Things application environment, and realize the secure transmission of information among many sensing checkpoints is an urgent problem to be solved. In this paper, we propose a decentralized lightweight authentication key protocol based on the combination of public key and trusted computing technology, which is used to establish secure communication between nodes in the perception layer. The various attacks that the protocol may suffer are analyzed, and the formal analysis method is used to verify the security of the protocol. To verify the validity of the protocol, the computation and communication cost of the protocol are compared with the existing key protocols. And the results show that the protocol achieved the promised performance.

Advances in information and communication technologies – global Internet, social media, Internet of Things, and a range of related science-driven innovations and generative and emergent technologies – continue to shape a dynamic communication and information ecosystem for which there is no precedent. These advances are powerful in many ways. Foremost among these in terms of salience, ubiquity, pervasiveness, and expansion in scale and scope is the broad area of artificial intelligence. They have created a new global ecology; yet they remain opaque and must be better understood—an ecology of “knowns” that is evolving in ways that remain largely “unknown.” Especially compelling is the acceleration of Artificial Intelligence – in all its forms – with far-ranging applications shaping a new global ecosystem for which there is no precedent. This chapter presents a brief view of the most pressing challenges, articulates the logic for worldwide agreement to retain the rule of law in the international system, and presents salient features of an emergent International Accord on Artificial Intelligence. The Framework for Artificial Intelligence International Accord (AIIA) is an initial response to this critical gap in the system of international rules and regulations.

Software-defined network (SDN) is a network architecture that used to build, design the hardware components virtually. We can dynamically change the settings of network connections. In the traditional network, it's not possible to change dynamically, because it's a fixed connection. SDN is a good approach but still is vulnerable to DDoS attacks. The DDoS attack is menacing to the internet. To prevent the DDoS attack, the machine learning algorithm can be used. The DDoS attack is the multiple collaborated systems that are used to target the particular server at the same time. In SDN control layer is in the center that link with the application and infrastructure layer, where the devices in the infrastructure layer controlled by the software. In this paper, we propose a machine learning technique namely Decision Tree and Support Vector Machine (SVM) to detect malicious traffic. Our test outcome shows that the Decision Tree and Support Vector Machine (SVM) algorithm provides better accuracy and detection rate.

Discovering vulnerabilities is an information-intensive task that requires a developer to locate the defects in the code that have security implications. The task is difficult due to the growing code complexity and some developer's lack of security expertise. Although tools have been created to ease the difficulty, no single one is sufficient. In practice, developers often use a combination of tools to uncover vulnerabilities. Yet, the basis on which different tools are composed is under explored. In this paper, we examine the composition base by taking advantage of the tool design patterns informed by foraging theory. We follow a design science methodology and carry out a three-step empirical study: mapping 34 foraging-theoretic patterns in a specific vulnerability discovery tool, formulating hypotheses about the value and cost of foraging when considering two composition scenarios, and performing a human-subject study to test the hypotheses. Our work offers insights into guiding developers' tool usage in detecting software vulnerabilities.

GSSD is an evolving knowledge networking system dedicated to sustainable development. Designed to help identify and extend innovative approaches toward sustainability—including enabling technologies, policies, and strategies—it tracks diverse aspects of challenges, problems, and emergent solutions to date. Specifically, it is a computer-assisted, organized system linking discrete actors with a knowledge producing capacity that is, (b) combined via common organizing principles, and (c) based on individual autonomy; such that (d) the value of networked knowledge is enhanced, and (e) the stock of knowledge is expanded further.

An electronic medical record (EMR) is the digital medical data of a patient, and they are healthcare system's most valuable asset. In this paper, we introduce a decentralized network using blockchain technology and smart contracts as a solution to manage and secure medical records storing, and transactions between medical healthcare providers. Ethereum blockchain is employed to build the blockchain. Solidity object-oriented language was utilized to implement smart contracts to digitally facilitate and verify transactions across the network (creating records, access requests, permitting access, revoking access, rejecting access). This will mitigate prevailing issues of current systems and enhance their performance, since current EMRs are stored on a centralized database, which cannot guarantee data integrity and security, consequently making them susceptible to malicious attacks. Our proposed system approach is of vital importance considering that healthcare providers depend on various tests in making a decision about a patient's diagnosis, and the respective plan of treatment they will go through. These tests are not shared with other providers, while data is scattered on various systems, as a consequence of these ensuing scenarios, patients suffer of the resulting care provided. Moreover, blockchain can meliorate the motley serious challenges caused by future use of IoT devices that provide real-time data from patients. Therefore, integrating the two technologies will produce decentralized IoT based healthcare systems.

The paper considers the issue of assessing threats to information security in industrial automation and telecommunication systems in order to improve the efficiency of their security systems. A method for determining a quantitative indicator of threats is proposed, taking into account the probabilistic nature of the process of implementing negative impacts on objects of both industrial and telecommunications systems. The factors that contribute and (or) initiate them are also determined, the dependences of the formal definition of the quantitative indicator of threats are obtained. Methods for a quantitative threat assessment as well as the degree of this threat are presented in the form of a mathematical model in order to substantiate and describe the method for determining a threat to industrial automation systems. Recommendations necessary for obtaining expert assessments of negative impacts on the informatisation objects and information security systems counteracting are formulated to facilitate making decisions on the protection of industrial and telecommunication systems.

Protecting privacy of the user location in Internet of Things (IoT) applications is a complex problem. Peer-to-peer (P2P) approach is one of the most popular techniques used to protect privacy in IoT applications, especially that use the location service. The P2P approach requires trust among peers in addition to serious cooperation. These requirements are still an open problem for this approach and its methods. In this paper, we propose an effective solution to this issue by creating a manager for the peers' reputation called R-TTP. Each peer has a new query. He has to evaluate the cooperated peer. Depending on the received result of that evaluation, the main peer will send multiple copies of the same query to multiple peers and then compare results. Moreover, we proposed another scenario to the manager of reputation by depending on Fog computing to enhance both performance and privacy. Relying on this work, a user can determine the most suitable of many available cooperating peers, while avoiding the problems of putting up with an inappropriate cooperating or uncommitted peer. The proposed method would significantly contribute to developing most of the privacy techniques in the location-based services. We implemented the main functions of the proposed method to confirm its effectiveness, applicability, and ease of application.

Malware poses a threat to computing systems worldwide, and security experts work tirelessly to detect and classify malware as accurately and quickly as possible. Since malware can use evasion techniques to bypass static analysis and security mechanisms, dynamic analysis methods are more useful for accurately analyzing the behavioral patterns of malware. Previous studies showed that malware behavior can be represented by sequences of executed system calls and that machine learning algorithms can leverage such sequences for the task of malware classification (a.k.a. malware categorization). Accurate malware classification is helpful for malware signature generation and is thus beneficial to antivirus vendors; this capability is also valuable to organizational security experts, enabling them to mitigate malware attacks and respond to security incidents. In this paper, we propose an improved methodology for malware classification, based on analyzing sequences of system calls invoked by malware in a dynamic analysis environment. We show that adding an attention mechanism to a LSTM model improves accuracy for the task of malware classification, thus outperforming the state-of-the-art algorithm by up to 6%. We also show that the transformer architecture can be used to analyze very long sequences with significantly lower time complexity for training and prediction. Our proposed method can serve as the basis for a decision support system for security experts, for the task of malware categorization.

The globalized supply chain in the semiconductor industry raises several security concerns such as IC overproduction, intellectual property piracy and design tampering. Logic locking has emerged as a Design-for-Trust countermeasure to address these issues. Original logic locking proposals provide a high degree of output corruption – i.e., errors on circuit outputs – unless it is unlocked with the correct key. This is a prerequisite for making a manufactured circuit unusable without the designer’s intervention. Since the introduction of SAT-based attacks – highly efficient attacks for retrieving the correct key from an oracle and the corresponding locked design – resulting design-based countermeasures have compromised output corruption for the benefit of better resilience against such attacks. Our proposed logic locking scheme, referred to as SKG-Lock, aims to thwart SAT-based attacks while maintaining significant output corruption. The proposed provable SAT-resilience scheme is based on the novel concept of decoy key-inputs. Compared with recent related works, SKG-Lock provides higher output corruption, while having high resistance to evaluated attacks.

We propose and demonstrate a set of microservice-based security components able to perform physical layer security assessment and mitigation in optical networks. Results illustrate the scalability of the attack detection mechanism and the agility in mitigating attacks.

We also sought to shed light on a yet-unexamined attack vector as it translates to healthcare networks: supply chain attacks. Several high-profile breaches in recent years involved lapses in the supply chain. Furthermore, according to a health and human services public breach reporting tool, 30 percent of healthcare breaches in 2016 were due to business associates and third-party vendor breaches. To learn from these cases, we studied the different ways threat actors can take advantage of weaknesses in the supply chain to infiltrate healthcare networks.