Biblio

Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation. Recently, we also observe a rapidly growing interest in applying machine learning (ML) to support different adaptation mechanisms. While MAPE and CT have particular characteristics and strengths to be applied independently, in this paper, we are concerned with the question of how these approaches are related with one another and whether combining them and supporting them with ML can produce better adaptive systems. We motivate the combined use of different adaptation approaches using a scenario of a cloud-based enterprise system and illustrate the analysis when combining the different approaches. To conclude, we offer a set of open questions for further research in this interesting area.

Cyber-Physical Systems (CPS) integrate computational and physical components. With the digitisation of society and industry and the progressing integration of systems, CPS need to become “smarter” in the sense that they can adapt and learn to handle new and unexpected conditions, and improve over time. Smarter CPS present a combination of challenges that existing engineering methods have difficulties addressing: intertwined digital, physical and social spaces, need for heterogeneous modelling formalisms, demand for context-tied cooperation to achieve system goals, widespread uncertainty and disruptions in changing contexts, inherent human constituents, and continuous encounter with new situations. While approaches have been put forward to deal with some of these challenges, a coherent perspective on engineering smarter CPS is lacking. In this paper, we present six engineering principles for addressing the challenges of smarter CPS. As smarter CPS are software-intensive systems, we approach them from a software engineering perspective with the angle of self-adaptation that offers an effective approach to deal with run-time change. The six principles create an integrated landscape for the engineering and operation of smarter CPS.

Utilities and local power providers throughout the world have recognized the advantages of the "smart grid" to encourage consumers to engage in greater energy efficiency. The digitalization of electricity and the consumer interface enables utilities to develop pricing arrangements that can smooth peak load. Time-varying price signals can enable devices associated with heating, air conditioning, and ventilation (HVAC) systems to communicate with market prices in order to more efficiently configure energy demand. Moreover, the shorter time intervals and greater collection of data can facilitate the integration of distributed renewable energy into the power grid. This study contributes to the understanding of time-varying pricing using a model that examines the extent to which transactive energy can reduce economic costs of an aggregated group of households with varying levels of distributed solar energy. It also considers the potential for transactive energy to smooth the demand curve.

Executive Order (EO) 14028, Improving the Nation's Cybersecurity, 12 May 2021, directs the National Institute of Standards and Technology (NIST) to recommend minimum standards for software testing within 60 days. This document describes eleven recommendations for software verification techniques as well as providing supplemental information about the techniques and references for further information. It recommends the following techniques: • Threat modeling to look for design-level security issues • Automated testing for consistency and to minimize human effort • Static code scanning to look for top bugs • Heuristic tools to look for possible hardcoded secrets • Use of built-in checks and protections • "Black box" test cases • Code-based structural test cases • Historical test cases • Fuzzing • Web app scanners, if applicable • Address included code (libraries, packages, services) The document does not address the totality of software verification, but instead recommends techniques that are broadly applicable and form the minimum standards. The document was developed by NIST in consultation with the National Security Agency. Additionally, we received input from numerous outside organizations through papers submitted to a NIST workshop on the Executive Order held in early June, 2021 and discussion at the workshop as well as follow up with several of the submitters.

The international Supply Chains (SC) have expanded rapidly over the decades and also consist of many entities and business partners. The increasing complexity of supply chain makes it more vulnerable to a security threat. Therefore, it is necessary to evaluate security management systems to ensure the flow of goods in SC. In this paper we used international standards to assess the security of the company's supply chain compliance with ISO 28001. Supply chain security that needs to be assessed includes all inbound logistics activities to outbound logistics. The aim of this research is to analyse the security management system by identifying security threat, consequences, and likelihood to develop adequate countermeasures for the security of the company's supply chain. Security risk assessment was done using methodology compliance with ISO 28001 which are identify scope of security assessment, conduct security assessment, list applicable threat scenario, determine consequences, determine likelihood, determine risk score, risk evaluation using risk matrix, determine counter measures, and estimation of risk matrix after countermeasures. This research conducted in one of the leather factory in Indonesia. In this research we divided security threat into five category: asset security, personnel security, information security, goods and conveyance security, and closed cargo transport units. The security assessment was conducted by considering the performance review according to ISO 28001: 2007 and the results show that there are 22 security threat scenarios in the company's supply chain. Based upon a system of priorities by risk score, countermeasures are designed to reduce the threat into acceptable level.

Recent technological advancements have proliferated the use of small embedded devices for collecting, processing, and transferring the security-critical information. The Internet of Things (IoT) has enabled remote access and control of these network-connected devices. Consequently, an attacker can exploit security vulnerabilities and compromise these devices. In this context, the secure boot becomes a useful security mechanism to verify the integrity and authenticity of the software state of the devices. However, the current secure boot schemes focus on detecting the presence of potential malware on the device but not on disinfecting and restoring the software to a benign state. This manuscript presents CARE - the first secure boot framework that provides malicious code modification attack detection, resilience, and onboard recovery mechanism for the compromised devices. The framework uses a prototype hybrid CARE: Code Authentication and Resilience Engine to verify the integrity and authenticity of the software and restore it to a benign state. It uses Physical Memory Protection (PMP) and other security enchaining techniques of RISC-V processor to provide resilience from modern attacks. The state-of-the-art comparison and performance analysis results indicate that the proposed secure boot framework provides promising resilience and recovery mechanism with very little (8%) performance and resource overhead.

The number of devices in the Internet of Things (IoT) immensely grew in recent years. A frequent challenge in the assurance of the dependability of IoT systems is that components of the system appear as a black box. This paper presents a semi-automatic testing methodology for black-box systems that combines automata learning and fuzz testing. Our testing technique uses stateful fuzzing based on a model that is automatically inferred by automata learning. Applying this technique, we can simultaneously test multiple implementations for unexpected behavior and possible security vulnerabilities.We show the effectiveness of our learning-based fuzzing technique in a case study on the MQTT protocol. MQTT is a widely used publish/subscribe protocol in the IoT. Our case study reveals several inconsistencies between five different MQTT brokers. The found inconsistencies expose possible security vulnerabilities and violations of the MQTT specification.

This work examines volatile memory modules as ephemeral key storage for security applications in the context of low temperatures. In particular, we note that such memories exhibit a rising level of data remanence as the temperature decreases, especially for temperatures below 280 Kelvin. Therefore, these memories cannot be used to protect the superconducting modules found in high-speed Magnetic Levitation (MagLev) trains, as such modules most often require extremely low temperatures in order to provide superconducting applications. Thus, a novel secure storage solution is required in this case, especially within the oncoming framework concept of the internet of railway things, which is partially based on the increasing utilisation of commercial off-the-shelf components and potential economies of scale, in order to achieve cost efficiency and, thus, widespread adoption. Nevertheless, we do note that volatile memory modules can be utilised as intrinsic temperature sensors, especially at low temperatures, as the data remanence they exhibit at low temperatures is highly dependent on the ambient temperature, and can, therefore, be used to distinguish between different temperature levels.

In Magnetic Josephson Junctions (MJJs) based on Superconductor-Insulator-Superconductor-Ferromagnet-Superconductor (SIS’FS), we provide evidence of an unconventional magnetic field behavior of the critical current characterized by an inverted magnetic hysteresis, i.e., an inverted shift of the whole magnetic field pattern when sweeping the external field. By thermoremanence measurements of S/F/S trilayers, we have ruled out that this uncommon behavior could be related to the F-stray fields. In principle, this finding could have a crucial role in the design and proper functioning of scalable cryogenic memories.

The power cyber-physical system combines the cyber network with the traditional electrical power network, which can monitor and control the operation of the power grid stably and efficiently. Since the system's structure and function is complicated and large, it becomes fragile as a result. Therefore, establishing a reasonable and effective CPS model and discussing its vulnerability performance under external attacks is essential and vital for power grid operation. This paper uses the theory of complex networks to establish a independent system model by IEEE-118-node power network and 200-node scale-free information network, introducing information index to identify and sort important nodes in the network, and then cascade model of the power cyber-physical system based on the node load capacity is constructed and the vulnerability assessment analysis is carried out. The simulation shows that the disintegration speed of the system structure under deliberate attacks is faster than random attacks; And increasing the node threshold can effectively inhibit the propagation of failure.

Higher education is increasingly called upon to enhance cyber education, including hands-on "experiential" training. The good news is that additional tools and techniques are becoming more available, both in-house and through third parties, to provide cyber training environments and simulations at various features and price points. However, the training thus far has only focused on "traditional" Cybersecurity that lightly touches on wireless in undergraduate and master's degree programs, and certifications. The purpose of this research is to identify and recognize nascent cyber training emphasizing a broader spectrum of wireless security and encourage curricular development that includes critical experiential training. Experiential wireless security training is important to keep pace with the growth in wireless communication mediums and associated Internet of Things (IoT) and Cyber Physical System (CPS) applications. Cyber faculty at a university offering undergraduate and master's Cybersecurity degrees authored this paper; both degrees are offered to resident as well as online students.

In human-robot collaboration (HRC), human trust in the robot is the human expectation that a robot executes tasks with desired performance. A higher-level trust increases the willingness of a human operator to assign tasks, share plans, and reduce the interruption during robot executions, thereby facilitating human-robot integration both physically and mentally. However, due to real-world disturbances, robots inevitably make mistakes, decreasing human trust and further influencing collaboration. Trust is fragile and trust loss is triggered easily when robots show incapability of task executions, making the trust maintenance challenging. To maintain human trust, in this research, a trust repair framework is developed based on a human-to-robot attention transfer (H2R-AT) model and a user trust study. The rationale of this framework is that a prompt mistake correction restores human trust. With H2R-AT, a robot localizes human verbal concerns and makes prompt mistake corrections to avoid task failures in an early stage and to finally improve human trust. User trust study measures trust status before and after the behavior corrections to quantify the trust loss. Robot experiments were designed to cover four typical mistakes, wrong action, wrong region, wrong pose, and wrong spatial relation, validated the accuracy of H2R-AT in robot behavior corrections; a user trust study with 252 participants was conducted, and the changes in trust levels before and after corrections were evaluated. The effectiveness of the human trust repairing was evaluated by the mistake correction accuracy and the trust improvement.

Human multi-robot system (MRS) collaboration is demonstrating potentials in wide application scenarios due to the integration of human cognitive skills and a robot team’s powerful capability introduced by its multi-member structure. However, due to limited human cognitive capability, a human cannot simultaneously monitor multiple robots and identify the abnormal ones, largely limiting the efficiency of the human-MRS collaboration. There is an urgent need to proactively reduce unnecessary human engagements and further reduce human cognitive loads. Human trust in human MRS collaboration reveals human expectations on robot performance. Based on trust estimation, the work between a human and MRS will be reallocated that an MRS will self-monitor and only request human guidance in critical situations. Inspired by that, a novel Synthesized Trust Learning (STL) method was developed to model human trust in the collaboration. STL explores two aspects of human trust (trust level and trust preference), meanwhile accelerates the convergence speed by integrating active learning to reduce human workload. To validate the effectiveness of the method, tasks "searching victims in the context of city rescue" were designed in an open-world simulation environment, and a user study with 10 volunteers was conducted to generate real human trust feedback. The results showed that by maximally utilizing human feedback, the STL achieved higher accuracy in trust modeling with a few human feedback, effectively reducing human interventions needed for modeling an accurate trust, therefore reducing human cognitive load in the collaboration.

Over the last few years, the deployment of Internet of Things (IoT) is attaining much more concern on smart computing devices. With the exponential growth of small devices and at the same time cheap prices of these sensing devices, there raises an important question for the security of the stored information as these devices generate a large amount of private data for observing and controlling purposes. Distributed Denial of Service (DDoS) attacks are current examples of major security threats to IoT devices. As yet, no standard protocol can fully ensure the security of IoT devices. But adaptive decision making along with elasticity and incessant monitoring is required. These difficulties can be resolved with the assistance of Software Defined Networking (SDN) which can viably deal with the security dangers to the IoT devices in a powerful and versatile way without hampering the lightweightness of the IoT devices. Although SDN performs quite well for managing and controlling IoT devices, security is still an open concern. Nonetheless, there are a few challenges relating to the mitigation of DDoS attacks in IoT systems implemented with SDN architecture. In this paper, a brief overview of some of the popular DDoS attack mitigation techniques and their limitations are described. Also, the challenges of implementing these techniques in SDN-based architecture to IoT devices have been presented.

The most representative method of deep learning is data-driven learning. These methods are often data-dependent, and lack of data leads to poor learning. There is a GAN method that creates a likely image as a way to solve a problem that lacks data. The GAN determines that the discriminator is fake/real with respect to the image created so that the generator learns. However, overfitting problems when the discriminator becomes overly dependent on the learning data. In this paper, we explain overfitting problem when the discriminator decides to fake/real using xAI. Depending on the area of the described image, it is possible to limit the learning of the discriminator to avoid overfitting. By doing so, the generator can produce similar but more diverse images.

Existing compression coding technologies are investigated using a statistical approach. The fundamental strategies used in the process of statistical coding of video information data are analyzed. Factors that have a significant impact on the reliability and efficiency of video delivery in the process of statistical coding are analyzed. A model for estimating the impact of errors in the process of reconstruction of uneven code structures on the quality of recoverable video images is being developed.The influence of errors that occur in data transmission channels on the reliability of the reconstructed video image is investigated.

Hardware Trojans are modifications made by malicious insiders or third party providers during the design or fabrication phase of the IC (Integrated Circuits) design cycle in a covert manner. These cause catastrophic consequences ranging from manipulating the functionality of individual blocks to disabling the entire chip. Thus, a need for detecting trojans becomes necessary. In this work, we propose a deep learning based approach for detecting trojans in IC chips. In particular, we insert trojans at the circuit-level and generate data by measuring power during normal operation and under attack. Further, we develop deep learning models using Neural networks and Auto-encoders to analyze datasets for outlier detection by profiling the normal behavior and leveraging them to detect anomalies in power consumption. Our approach is generic and non-invasive in that it can be applied to any block without any modifications to the design. Evaluation of the proposed approach shows an accuracy ranging from 92.23% to 99.33% in detecting trojans.

Recently, Mobile Edge Cloud computing (MEC) has attracted attention both from academia and industry. The idea of moving a part of cloud resources closer to users and data sources can bring many advantages in terms of speed, data traffic, security and context-aware services. The MEC infrastructure does not only host and serves applications next to the end-users, but services can be dynamically migrated and reallocated as mobile users move in order to guarantee latency and performance constraints. This specific requirement calls for the involvement and collaboration of multiple MEC providers, which raises a major issue related to trustworthiness. Two main challenges need to be addressed: i) trustworthiness needs to be handled in a manner that does not affect latency or performance, ii) trustworthiness is considered in different dimensions - not only security metrics but also performance and quality metrics in general. In this paper, we propose a trust layer for public MEC infrastructure that handles establishing and updating trust relations among all MEC entities, making the interaction withing a MEC network transparent. First, we define trust attributes affecting the trusted quality of the entire infrastructure and then a methodology with a computation model that combines these trust attribute values. Our experiments showed that the trust model allows us to reduce latency by removing the burden from a single MEC node, while at the same time increase the network trustworthiness.

The widespread use of smartphones has made the gadget a prime source of evidence for crime investigators. The cloud-based applications on mobile devices store a rich set of evidence in the cloud servers. The physical acquisition of Android devices reveals only minimal data of cloud-based apps. However, the artifacts collected from mobile devices can be used for data acquisition from cloud servers. This paper focuses on the forensic acquisition and analysis of cloud data of Google apps on Android devices. The proposed methodology uses the tokens extracted from the Android devices to get authenticated to the Google server bypassing the two-factor authentication scheme and access the cloud data for further analysis. Based on the investigation, we have also developed a tool to acquire, preserve and analyze cloud data in a forensically sound manner.

Network resilience is crucial to ensure reliable and secure operation of critical infrastructures. Although graph theoretic methods have been developed to quantify the topological resilience of networks, i.e., measuring resilience with respect to connectivity, in this study we propose to use the tools from Topological Data Analysis (TDA), Algebraic Topology, and Optimal Transport (OT). In our prior work, we used these tools to create a resilience metric that bypassed the need to embed a network onto a space. We also hypothesized that embeddings could encode different information about a network and that different embeddings could result in different outcomes when computing resilience. In this paper we attempt to test this hypothesis. We will utilize the WEGL framework to compute the embedding for the considered network and compare the results against our prior work, which did not use an embedding process. To our knowledge, this is the first attempt to study the ramifications of choosing an embedding, thus providing a novel understanding into how to choose an embedding and whether such a choice matters when quantifying resilience.

This work investigates the problem of analyzing privacy of abrupt changes for general Markov processes. These processes may be affected by changes, or exogenous signals, that need to remain private. Privacy refers to the disclosure of information of these changes through observations of the underlying Markov chain. In contrast to previous work on privacy, we study the problem for an online sequence of data. We use theoretical tools from optimal detection theory to motivate a definition of online privacy based on the average amount of information per observation of the stochastic system in consideration. Two cases are considered: the full-information case, where the eavesdropper measures all but the signals that indicate a change, and the limited-information case, where the eavesdropper only measures the state of the Markov process. For both cases, we provide ways to derive privacy upper-bounds and compute policies that attain a higher privacy level. It turns out that the problem of computing privacy-aware policies is concave, and we conclude with some examples and numerical simulations for both cases.

Over the last few years, the world has been moving towards digital healthcare, where harnessing medical data distributed across multiple healthcare providers is essential to achieving personalized treatments. Though the efficiency and speed of the diagnosis process have increased due to the digitalization of healthcare data, it is at constant risk of cyberattacks. Medical images, in particular, seem to have become a regular victim of hackers, due to which there is a need to find a feasible solution for storing them securely. This work proposes a blockchain-based framework that leverages the InterPlanetary File system (IPFS) to provide decentralized storage for medical images. Our proposed blockchain storage model is implemented in the IPFS distributed file-sharing system, where each image is stored on IPFS, and its corresponding unique content-addressed hash is stored in the blockchain. The proposed model ensures the security of the medical images without any third-party dependency and eliminates the obstacles that arise due to centralized storage.

SQL Injection and Cross-Site Scripting are the two most common attacks in database-based web applications. In this paper we propose a system to detect different types of SQL injection and XSS attacks associated with a web application, without the existence of any firewall, while significantly reducing the network overhead. We use properly modifications of the Nginx Reverse Proxy protocols and Suricata NIDS/ IPS rules. Pure work has been done from other researchers based on the capabilities of Nginx and Suricata and our approach with the experimental results provided in the paper demonstrate the efficiency of our system.

Large open-source systems generate and operate on a plethora of sensitive enterprise data. Thus, security threats or vulnerabilities must not be present in open-source systems and must be resolved as early as possible in the development phases to avoid catastrophic consequences. One way to recognize security vulnerabilities is to predict them while developers write code to minimize costs and resources. This study examines the effectiveness of machine learning algorithms to predict potential security vulnerabilities by analyzing the source code of a system. We obtained the security vulnerabilities dataset from Apache Tomcat security reports for version 4.x to 10.x. We also collected the source code of Apache Tomcat 4.x to 10.x to compute 43 object-oriented metrics. We assessed four traditional supervised learning algorithms, i.e., Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN), and Logistic Regression (LR), to understand their efficacy in predicting security vulnerabilities. We obtained the highest accuracy of 80.6% using the KNN. Thus, the KNN classifier was demonstrated to be the most effective of all the models we built. The DT classifier also performed well but under-performed when it came to multi-class classification.