Biblio

The Open Data Cube (ODC), created and facilitated by the Committee on Earth Observation Satellites (CEOS), is an open source software architecture that continues to gain global popularity through the integration of analysis-ready data (ARD) on cloud computing frameworks. In 2021, CEOS released a new ODC sandbox that provides global users with a free and open programming interface connected to Google Earth Engine datasets. The open source toolset allows users to run application algorithms using a Google Colab Python notebook environment. This tool demonstrates rapid creation of science products anywhere in the world without the need to download and process the satellite data. Basic operation of the tool will support many users but can also be scaled in size and scope to support enhanced user needs. The creation of the ODC sandbox was prompted by the migration of many CEOS ARD satellite datasets to the cloud. The combination of these datasets in an interoperable data cube framework will inspire the creation of many new application products and advance open science.

In today's world, there is rapid progress in the field of artificial intelligence and image captioning. It becomes a fascinating task that has saw widespread interest. The task of image captioning comprises image description engendered based on the hybrid combination of deep learning, natural language processing, and various approaches of machine learning and computer vision. In this work authors emphasize on how the model generates a short description as an output of the input image using the functionalities of Deep Learning and Natural Language Processing, for helping visually impaired people, and can also be cast-off in various web sites to automate the generation of captions reducing the task of recitation with great ease.

In this fast-running world, machine communication plays a vital role. To compete with this world, human-machine interaction is a necessary thing. To enhance this, Natural Language Processing technique is used widely. Using this technique, we can reduce the interaction gap between the machine and human. Till now, many such applications are developed which are using this technique.This tool deals with the various methods which are used for development of grammar error correction. These methods include rule-based method, classifier-based method and machine translation-based method. Also, models regarding the Natural Language Processing (NLP) pipeline are trained and implemented in this project accordingly. Additionally, the tool can also perform speech to text operation.

This paper contains analysis of methods of increasing the information protection from unauthorized access using a multifactor authentication algorithm; figuring out the best, most efficient and secure method of scanning biometric data; development of a method to store and compare a candidate’s and existisng system user’s information in steganographic space. The urgency of the work is confirmed by the need to increase information security of special infocommunication systems with the help of biometric information and protection of this information from intruders by means of steganographic transformation.

Multi-Factor Authentication is an electronic authentication method in which a computer user is granted access to an application or a website only after successfully presenting two or more factors, or pieces of evidence. It is the first step to protect systems against intruders since the traditional log-in methods (username and password) are not completely protected from hackers, since they can guess them easily using tools. Current Systems use additional methods to increase security, such as using two-factor authentication based on a one-time password via mobile or email, or authentication based on biometrics (fingerprint, eye iris or retina, and face recognition) or via token devices. However, these methods require additional hardware equipment with high cost at the level of small and medium companies. This paper proposes a multi-factor authentication system that combines ease of use and low-cost factors. The system does not need any special settings or infrastructure. It relies on graphical passwords, so the user, in registration phase, chooses three images and memorizes them. In the login phase, the user needs only to choose the correct images that he considered during the registration process in a specific order. The proposed system overcomes many different security threats, such as key-loggers, screen capture attack or shoulder surfing. The proposed method was applied to 170 participants, 75% of them are males and 25% are females, classified according to their age, education level, web experience. One-third of them did not have sufficient knowledge about various security threats.

The use of static code analysis tools for security audits can be time consuming, as the many existing tools focus on different aspects and therefore development teams often use several of these tools to keep code quality high and prevent security issues. Displaying the results of multiple tools, such as code smells and security warnings, in a unified interface can help developers get a better overview and prioritize upcoming work. We present visualizations and a dashboard that interactively display results from static code analysis for “interesting” commits during development. With this, we aim to provide an effective visual analytics tool for code security analysis results.

Security analysts conduct continuous evaluations of cyber-defense tools to keep pace with advanced and persistent threats. Cyber agility has become a critical proactive security resource that makes it possible to measure defense adjustments and reactions to rising threats. Subsequently, machine learning has been applied to support cyber agility prediction as an essential effort to anticipate future security performance. Nevertheless, apt and treacherous actors motivated by economic incentives continue to prevail in circumventing machine learning-based protection tools. Adversarial learning, widely applied to computer security, especially intrusion detection, has emerged as a new area of concern for the recently recognized critical cyber agility prediction. The rationale is, if a sophisticated malicious actor obtains the cyber agility parameters, correct prediction cannot be guaranteed. Unless with a demonstration of white-box attack failures. The challenge lies in recognizing that unconstrained adversaries hold vast potential capabilities. In practice, they could have perfect-knowledge, i.e., a full understanding of the defense tool in use. We address this challenge by proposing an adversarial machine learning approach that achieves accurate cyber agility forecast through mapped nefarious influence on static defense tools metrics. Considering an adversary would aim at influencing perilous confidence in a defense tool, we demonstrate resilient cyber agility prediction through verified attack signatures in dynamic learning windows. After that, we compare cyber agility prediction under negative influence with and without our proposed dynamic learning windows. Our numerical results show the model's execution degrades without adversarial machine learning. Such a feigned measure of performance could lead to incorrect software security patching.

Steady-state response of the grid under a predefined set of credible contingencies is an important component of power system security assessment. With the growing complexity of electrical networks, fast and reliable methods and tools are required to effectively assist transmission grid operators in making decisions concerning system security procurement. In this regard, a Convolutional Neural Network (CNN) based approach to develop prediction models for static security assessment under N-1 contingency is investigated in this paper. The CNN model is trained and applied to classify the security status of a sample system according to given node voltage magnitudes, and active and reactive power injections at network buses. Considering a set of performance metrics, the superior performance of the CNN alternative is demonstrated by comparing the obtained results with a support vector machine classifier algorithm.

Campus Companies and network operators perform risk assessment to inform policy-making, guide infrastructure investments or to comply with security standards such as ISO 27001. Due to the size and complexity of these networks, risk assessment techniques such as attack graphs or trees describe the attacker with a finite set of rules. This characterization of the attacker can easily miss attack vectors or overstate them, potentially leading to incorrect risk estimation. In this work, we propose the first methodology to justify a rule-based attacker model. Conceptually, we add another layer of abstraction on top of the symbolic model of cryptography, which reasons about protocols and abstracts cryptographic primitives. This new layer reasons about Internet-scale networks and abstracts protocols.We show, in general, how the soundness and completeness of a rule-based model can be ensured by verifying trace properties, linking soundness to safety properties and completeness to liveness properties. We then demonstrate the approach for a recently proposed threat model that quantifies the confidentiality of email communication on the Internet, including DNS, DNSSEC, and SMTP. Using off-the-shelf protocol verification tools, we discover two flaws in their threat model. After fixing them, we show that it provides symbolic soundness.

Mobile payment protocols have attracted widespread attention over the past decade, due to advancements in digital technology. The use of these protocols in online industries can dramatically improve the quality of online services. However, the central issue of concern when utilizing these types of systems is their accountability, which ensures trust between the parties involved in payment transactions. It is, therefore, vital for researchers to investigate how to handle the accountability of mobile payment protocols. In this research, we introduce a secure mobile payment protocol to overcome this problem. Our payment protocol combines all the necessary security features, such as confidentiality, integrity, authentication, and authorization that are required to build trust among parties. In other words, is the properties of mutual authentication and non-repudiation are ensured, thus providing accountability. Our approach can resolve any conflicts that may arise in payment transactions between parties. To prove that the proposed protocol is correct and complete, we use the Scyther and AVISPA tools to verify our approach formally.

UPI (Unified Payments Interface) is a framework in India wherein customers can send payments to merchants from their smartphones. The framework consists of UPI servers that are connected to the banks at the sender and receiver ends. To send and receive payments, customers and merchants would have to first register themselves with UPI servers by executing a registration protocol using payment apps such as BHIM, PayTm, Google Pay, and PhonePe. Weaknesses were recently reported on these protocols that allow attackers to make money transfers on behalf of innocent customers and even empty their bank accounts. But the reported weaknesses were found after informal and manual analysis. However, as history has shown, formal analysis of cryptographic protocols often reveals flaws that could not be discovered with manual inspection. In this paper, we model UPI protocols in the pattern of traditional cryptographic protocols such that they can be rigorously studied and analyzed using formal methods. The modeling simplifies many of the complexities in the protocols, making it suitable to analyze and verify UPI protocols with popular analysis and verification tools such as the Constraint Solver, ProVerif and Tamarin. Our modeling could also be used as a general framework to analyze and verify many other financial payment protocols than just UPI protocols, giving it a broader applicability.

This paper deals with a problem that arises in vertical composition of protocols, i.e., when a channel protocol is used to encrypt and transport arbitrary data from an application protocol that uses the channel. Our work proves that we can verify that the channel protocol ensures its security goals independent of a particular application. More in detail, we build a general paradigm to express vertical composition of an application protocol and a channel protocol, and we give a transformation of the channel protocol where the application payload messages are replaced by abstract constants in a particular way that is feasible for standard automated verification tools. We prove that this transformation is sound for a large class of channel and application protocols. The requirements that channel and application have to satisfy for the vertical composition are all of an easy-to-check syntactic nature.

Given the central importance of designing secure protocols, providing solid mathematical foundations and computer-assisted methods to attest for their correctness is becoming crucial. Here, we elaborate on the formal approach introduced by Bana and Comon in [10], [11], which was originally designed to analyze protocols for a fixed number of sessions, and lacks support for proof mechanization.In this paper, we present a framework and an interactive prover allowing to mechanize proofs of security protocols for an arbitrary number of sessions in the computational model. More specifically, we develop a meta-logic as well as a proof system for deriving security properties. Proofs in our system only deal with high-level, symbolic representations of protocol executions, similar to proofs in the symbolic model, but providing security guarantees at the computational level. We have implemented our approach within a new interactive prover, the Squirrel prover, taking as input protocols specified in the applied pi-calculus, and we have performed a number of case studies covering a variety of primitives (hashes, encryption, signatures, Diffie-Hellman exponentiation) and security properties (authentication, strong secrecy, unlinkability).

In protocol verification we observe a wide spectrum from fully automated methods to interactive theorem proving with proof assistants like Isabelle/HOL. The latter provide overwhelmingly high assurance of the correctness, which automated methods often cannot: due to their complexity, bugs in such automated verification tools are likely and thus the risk of erroneously verifying a flawed protocol is non-negligible. There are a few works that try to combine advantages from both ends of the spectrum: a high degree of automation and assurance. We present here a first step towards achieving this for a more challenging class of protocols, namely those that work with a mutable long-term state. To our knowledge this is the first approach that achieves fully automated verification of stateful protocols in an LCF-style theorem prover. The approach also includes a simple user-friendly transaction-based protocol specification language embedded into Isabelle, and can also leverage a number of existing results such as soundness of a typed model

Memory Denial of Service (DoS) attacks are easy-to-launch, hard to detect, and significantly impact their targets. In memory DoS, the attacker targets the memory of his Virtual Machine (VM) and, due to hardware isolation issues, the attack affects the co-resident VMs. Theoretically, we can deploy VM migration as Moving Target Defense (MTD) against memory DoS. However, the current literature lacks empirical evidence supporting this hypothesis. Moreover, there is a need to evaluate how the VM migration timing impacts the potential MTD protection. This practical experience report presents an experiment on VM migration-based MTD against memory DoS. We evaluate the impact of memory DoS attacks in the context of two applications running in co-hosted VMs: machine learning and OLTP. The results highlight that the memory DoS attacks lead to more than 70% reduction in the applications' performance. Nevertheless, timely VM migrations can significantly mitigate the attack effects in both considered applications.

While the need for content distribution proliferates - becoming more mammoth and complex on the Internet - the P2P network perseveres as one of the best avenues to service the demand for content distribution. It enjoys a wide range of clients that transport data in bits securely, making it susceptible to moving dubious contents, hence becoming exposed to varying security threats that require credible digital investigation to address. The tools and techniques used in performing digital investigations are still mostly lagging, successfully slowing down law enforcement agencies in general. The acquisition of digital evidence over the Internet is still elusive in the battle against cybercrime. This paper considers a new technique for detecting passive peers that participate in a P2P network. As part of our study, we crawled the µTorrent P2P client over 10 days while logging all participating peers. We then employed digital forensic techniques to analyze the popular users and generate evidence within them with high accuracy. Finally, we evaluated our proposed approach against the standard Analysis, Design, Development, Implementation, and Evaluation, or ADDIE model for digital investigation to arrive at the credible digital evidence presented in this paper.

The collection of digital information by governments, companies and individuals creates tremendous opportunities for knowledge and information-based decision-making. Driven by mutual benefit and laws and regulations, there is a need for data exchange and publication between all parties. However, data in its original form usually contains sensitive information about individuals and publishing such data would violate personal privacy. Privacy Protection Data Distribution (PPDP) provides methods and tools to release useful information while protecting data privacy. In recent years, PPDP has received extensive attention from the research community, and many solutions have been proposed for different data release scenarios. How to ensure the availability of data under the premise of protecting user privacy is the core problem to be solved in this field. This paper studies the existing achievements of privacy protection data release anonymity technology, focusing on the existing anonymity technology in three aspects of high-dimensional, high-deficiency, and complex relational data, and analyzes and summarizes them.

The task of malware labeling on real datasets faces huge challenges—ever-changing datasets and lack of ground-truth labels—owing to the rapid growth of malware. Clustering malware on their respective families is a well known tool used for improving the efficiency of the malware labeling process. In this paper, we addressed the challenge of clustering email malware, and carried out a cluster analysis on a real dataset collected from email campaigns over a 13-month period. Our main original contribution is to analyze the usefulness of email’s header information for malware clustering (a novel approach proposed by Burton [1]), and compare it with features collected from the malware directly. We compare clustering based on email header’s information with traditional features extracted from varied resources provided by VirusTotal [2], including static and dynamic analysis. We show that email header information has an excellent performance.

Intrusion Detection System (IDS) is one of the most important security tool for many security issues that are prevailing in today's cyber world. Intrusion Detection System is designed to scan the system applications and network traffic to detect suspicious activities and issue an alert if it is discovered. So many techniques are available in machine learning for intrusion detection. The main objective of this project is to apply machine learning algorithms to the data set and to compare and evaluate their performances. The proposed application has used the SVM (Support Vector Machine) and ANN (Artificial Neural Networks) Algorithms to detect the intrusion rates. Each algorithm is used to detect whether the requested data is authorized or contains any anomalies. While IDS scans the requested data if it finds any malicious information it drops that request. These algorithms have used Correlation-Based and Chi-Squared Based feature selection algorithms to reduce the dataset by eliminating the useless data. The preprocessed dataset is trained and tested with the models to obtain the prominent results, which leads to increasing the prediction accuracy. The NSL KDD dataset has been used for the experimentation. Finally, an accuracy of about 48% has been achieved by the SVM algorithm and 97% has been achieved by ANN algorithm. Henceforth, ANN model is working better than the SVM on this dataset.

Supply chain management systems (SCM) are the most intensive and statistical RFID application for object tracking. A lot of research has been carried out to overcome security issues in the field of online/offline object tracking as well as authentication protocols involving RFID technology. Due to advancements with the Internet of Things (IoT) and embedded systems in object tracking schemes the latest research manages to deliver information about the object’s location as well as provide particulars about the state of an object. Recent research presented a proposal for an authentication and online object tracking protocol focusing on solutions for privacy issues for device identification, end-to-end authentication, and secure online object tracking. However, recent schemes have been found to be vulnerable to traceability attacks. This paper presents an enhanced end-to-end authentication scheme where the identity of the user is kept anonymous so that its actions can not be tracked, eliminating attacks related to traceability. The security of the proposed protocol is formally analyzed using the attack model of the automated security testing tool, ProVerif. The proposed scheme outperforms competing schemes based on security.

Code-reuse techniques have emerged as a way to defeat the control-flow defenses that prevent the injection and execution of new code, as they allow an adversary to hijack the control flow of a victim program without injected code. A well-known code-reuse attack technique is Return-OrientedProgramming (ROP), which considers and links together (relatively short) code snippets, named ROP gadgets, already present in the victim’s memory address space through a controlled use of the stack values of the victim program. Although ROP attacks are known to be Turing-complete, there are still open question such as the quantification of the executional power of an adversary, which is determined by whatever code exists in the memory of a victim program, and whether an adversary can build a ROP chain, made up of ROP gadgets, for any kind of algorithm. To fill these gaps, in this paper we first define a virtual language, dubbed ROPLANG, that defines a set of operations (specifically, arithmetic, assignment, dereference, logical, and branching operations) which are mapped to ROP gadgets. We then use it to evaluate the executional power of an adversary in Windows 7 and Windows 10, in both 32- and 64-bit versions. In addition, we have developed ROP3, a tool that accepts a set of program files and a ROP chain described with our language and returns the code snippets that make up the ROP chain. Our results show that there are enough ROP gadgets to simulate any virtual operation and that branching operations are the less frequent ones. As expected, our results also indicate that the larger a program file is, the more likely to find ROP gadgets within it for every virtual operation.

This work investigates the impact of mechanical strain on wake-up behavior of planar HfO2 ferroelectric capacitor-based memory. External in-plane strain was applied using a four-point bending tool and strain impact on remanent polarization and coercive voltage of the ferroelectric was monitored. It was established that compressive strain is beneficial for 2Pr improvement, while tensile strain leads to its degradation, with a sensitivity of -8.4 ± 0.5 % per 0.1 % of strain. Strain-induced polarization rotation is considered to be the most likely mechanism affecting 2Pr At the same time, no strain impact on Vcwas observed in the investigated strain range. The results seen here can be utilized to undertake stress engineering of ferroelectric memory in order to improve its performance.

Rescue robots are expected to soon become commonplace at disaster sites, where they are increasingly being deployed to provide rescuers with improved access and intervention capabilities while mitigating risks. The presence of robots in operation areas, however, is likely to carry a layer of additional ethical complexity to situations that are already ethically challenging. In addition, limited guidance is available for ethically informed, practical decision-making in real-life disaster settings, and specific ethics training programs are lacking. The contribution of this paper is thus to propose a tool aimed at supporting ethics training for rescuers operating with rescue robots. To this end, we have designed an interactive text-based simulation. The simulation was developed in Python, using Tkinter, Python's de-facto standard GUI. It is designed in accordance with the Case-Based Learning approach, a widely used instructional method that has been found to work well for ethics training. The simulation revolves around a case grounded in ethical themes we identified in previous work on ethical issues in rescue robotics: fairness and discrimination, false or excessive expectations, labor replacement, safety, and trust. Here we present the design of the simulation and the results of usability testing.

Robotic systems are becoming an ever-increasing part of everyday life due to their capacity to carry out physical tasks on behalf of human beings. Found in nearly every facet of our lives, robotic systems are used domestically, in small and large-scale factories, for the production and processing of agriculture, for military operations, to name a few. The Robotic Operating System (ROS) is the standard operating system used today for the development of modular robotic systems. However, in its development, ROS has been notorious for the absence of security mechanisms, placing people in danger both physically and digitally. This dissertation summary presents the development of a suite of ROS tools, leading up to the development of a modular, secure framework for ROS. An integrated approach for the security of ROS-enabled robotic systems is described, to set a baseline for the continual development to increase ROS security. The work culminates in the ROS security tool ROS-Immunity, combining internal system defense, external system verification, and automated vulnerability detection in an integrated tool that, in conjunction with Secure-ROS, provides a suite of defenses for ROS systems against malicious attackers.

These days, almost everyone has been entirely relying on mobile devices and mobile related applications running on Android Operating Systems, the most used Mobile Operating System in the world with the largest market share. These Mobile devices and applications can become an information goldmine for hackers and are considered one of the significant concerns mobile users face who stand a chance of being victimized during data breach from hackers due to lapse in information security and controls. Such challenge can be put to bare through systematic digital forensic analysis through penetration testing for a humanoid robot like Zenbo, which run Android OS and related application, to help identify associated security vulnerabilities and develop controls required to improve security using popular penetration testing tools such as Drozer, Mobile Application Security framework (mobSF), and AndroBugs with the help of Santoku Linux distribution.