Biblio

Authorship attribution, an NLP problem where anonymous text is matched to its author, has important, cross-disciplinary applications, particularly those concerning cyber-defense. Our research examines the degree of sensitivity that attention-based models have to adversarial perturbations. We ask, what is the minimal amount of change necessary to maximally confuse a transformer model? In our investigation we examine a balanced subset of emails from the Enron email dataset, calculating the performance of our model before and after email signatures have been perturbed. Results show that the model's performance changed significantly in the absence of a signature, indicating the importance of email signatures in email authorship detection. Furthermore, we show that these models rely on signatures for shorter emails much more than for longer emails. We also indicate that additional research is necessary to investigate stylometric features and adversarial training to further improve classification model robustness.

Cloud computing has helped in managing big data and providing resources remotely and ubiquitously, but it has some latency and security concerns. Fog has provided tremendous advantages over cloud computing which include low latency rate, improved real-time interactions, reduced network traffic overcrowding, and improved reliability, however, security concerns need to be addressed separately. Another major issue in the cloud is Cloud Lock-in/Vendor Lock-in. Through this research, an effort has been made to extend fog computing and Searchable Encryption technologies. The proposed system can reduce the issue of cloud lock-in faced in traditional cloud computing. The SE schemes used in this paper are Symmetric Searchable Encryption (SSE) and Multi-keyword Ranked Searchable Encryption (MRSE) to achieve confidentiality, privacy, fine-grained access control, and efficient keyword search. This can help to achieve better access control and keyword search simultaneously. An important use of this technique is it helps to prevent the issue of cloud/vendor lock-in. This can shift some computation and storage of index tables over fog nodes that will reduce the dependency on Cloud Service Providers (CSPs).

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.

Many embedded systems have evolved from simple bare-metal control systems to highly complex network-connected systems. These systems increasingly demand rich and feature-full operating-systems (OS) functionalities. Furthermore, the network connectedness offers attack vectors that require stronger security designs. To that end, this paper defines a prototypical RTOS API called Patina that provides services common in featurerich OSes (e.g., Linux) but absent in more trustworthy μ -kernel based systems. Examples of such services include communication channels, timers, event management, and synchronization. Two Patina implementations are presented, one on Composite and the other on seL4, each of which is designed based on the Principle of Least Privilege (PoLP) to increase system security. This paper describes how each of these μ -kernels affect the PoLP based design, as well as discusses security and performance tradeoffs in the two implementations. Results of comprehensive evaluations demonstrate that the performance of the PoLP based implementation of Patina offers comparable or superior performance to Linux, while offering heightened isolation.

Multi-access edge computing (MEC) equipped with artificial intelligence is a promising technology in B5G wireless systems. Due to outsourcing and other transactions, some primitive security modules need to be introduced. In this paper, we design a primitive cipher based on double discrete exponentiation and double discrete logarithm. The machine learning methodology is incorporated in the development. Several interesting results are obtained. It reveals that the number of key-rounds is critically important.

Sharding can significantly improve the blockchain scalability, by dividing nodes into small groups called shards that can handle transactions in parallel. However, all existing sharding systems adopt complete sharding, i.e., shards are isolated. It raises additional overhead to guarantee the atomicity and consistency of cross-shard transactions and seriously degrades the sharding performance. In this paper, we present Pyramid, the first layered sharding blockchain system, in which some shards can store the full records of multiple shards thus the cross-shard transactions can be processed and validated in these shards internally. When committing cross-shard transactions, to achieve consistency among the related shards, a layered sharding consensus based on the collaboration among several shards is presented. Compared with complete sharding in which each cross-shard transaction is split into multiple sub-transactions and cost multiple consensus rounds to commit, the layered sharding consensus can commit cross-shard transactions in one round. Furthermore, the security, scalability, and performance of layered sharding with different sharding structures are theoretically analyzed. Finally, we implement a prototype for Pyramid and its evaluation results illustrate that compared with the state-of-the-art complete sharding systems, Pyramid can improve the transaction throughput by 2.95 times in a system with 17 shards and 3500 nodes.

Quantum computers have the potential to outshine classical alternatives in solving specific problems, under the assumption of mature enough hardware. A specific subset of these problems relate to military applications. In this paper we consider the state-of-the-art of quantum technologies and different applications of this technology. Additionally, four use-cases of quantum computing specific for military applications are presented. These use-cases are directly in line with the 2021 AI strategic agenda of the Netherlands Ministry of Defense.

Cyber-Physical Systems (CPS) tightly couple information technology with physical processes, which rises new vulnerabilities such as physical attacks that are beyond conventional cyber attacks. Attackers may non-invasively compromise sensors and spoof the controller to perform unsafe actions. This issue is even emphasized with the increasing autonomy in CPS. While this fact has motivated many defense mechanisms against sensor attacks, a clear vision on the timing and usability (or the false alarm rate) of attack detection still remains elusive. Existing works tend to pursue an unachievable goal of minimizing the detection delay and false alarm rate at the same time, while there is a clear trade-off between the two metrics. Instead, we argue that attack detection should bias different metrics when a system sits in different states. For example, if the system is close to unsafe states, reducing the detection delay is preferable to lowering the false alarm rate, and vice versa. To achieve this, we make the following contributions. In this paper, we propose a real-time adaptive sensor attack detection framework. The framework can dynamically adapt the detection delay and false alarm rate so as to meet a detection deadline and improve the usability according to different system status. The core component of this framework is an attack detector that identifies anomalies based on a CUSUM algorithm through monitoring the cumulative sum of difference (or residuals) between the nominal (predicted) and observed sensor values. We augment this algorithm with a drift parameter that can govern the detection delay and false alarm. The second component is a behavior predictor that estimates nominal sensor values fed to the core component for calculating the residuals. The predictor uses a deep learning model that is offline extracted from sensor data through leveraging convolutional neural network (CNN) and recurrent neural network (RNN). The model relies on little knowledge of the system (e.g., dynamics), but uncovers and exploits both the local and complex long-term dependencies in multivariate sequential sensor measurements. The third component is a drift adaptor that estimates a detection deadline and then determines the drift parameter fed to the detector component for adjusting the detection delay and false alarms. Finally, we implement the proposed framework and validate it using realistic sensor data of automotive CPS to demonstrate its efficiency and efficacy.

The data revolution will continue in the near future and move from centralized big data to "small" datasets. This trend stimulates the emergence not only new machine learning methods but algorithms for processing data at the point of their origin. So the Compressed Sensing Problem must be investigated in some technology fields that produce the data flow for decision making in real time. In the paper, we compare the random and constant frequency deviation and highlight some circumstances where advantages of the random deviation become more obvious. Also, we propose to use the differential transformations aimed to restore a signal form by discrets of the differential spectrum of the received signal. In some cases for the investigated model, this approach has an advantage in the compress of information.

Knowledge Graph can describe the concepts in the objective world and the relationships between these concepts in a structured way, and identify, discover and infer the relationships between things and concepts. It has been developed in the field of medical and health care. In this paper, the method of natural language processing has been used to build chronic disease knowledge graph, such as named entity recognition, relationship extraction. This method is beneficial to forecast analysis of chronic disease, network monitoring, basic education, etc. The research of this paper can greatly help medical experts in the treatment of chronic disease treatment, and assist primary clinicians with making more scientific decision, and can help Patients with chronic diseases to improve medical efficiency. In the end, it also has practical significance for clinical scientific research of chronic disease.

Smart energy management system in smart grid carries a large number of sensitive data, which needs encryption algorithm to ensure the security of system communication. At present, most of the terminal devices of smart grid are embedded devices with limited computing resources, and their communication encryption mostly relies on AES encryption algorithm. It is difficult in key management and key distribution. Therefore, this paper proposes an improved ECC-AES hybrid encryption algorithm. Firstly, ECC algorithm is improved to improve the speed of encryption and decryption, and then the improved ECC algorithm is used as a supplement to AES algorithm. ECC is used to encrypt the AES key, which improves the security of the algorithm. At the same time, the experimental simulation also proves that the improved ECC algorithm has obvious performance improvement in computing time, CPU occupancy and memory usage.

Cyber-physical systems, which are ubiquitous in modern critical infrastructure, oftentimes rely on sending actuation commands and sensor measurements over a network, subjecting this information to potential man-in-the-middle attacks. These attacks can take the form of denial of service attacks or integrity attacks. Previous approaches at ensuring the resiliency of the overall control system against these types of attacks have leveraged functional redundancy in the system, including resilient estimation and reconfigurable control. However, these approaches are only able to ensure resiliency up to a particular subset of the actuator commands and sensor measurements being compromised. In contrast, we introduce a resiliency mechanism in this paper that can ensure safety for the overall system when all the actuator commands and sensor measurements are compromised. In addition, this approach does not require the implementation of any detection algorithm. We leverage communication redundancy in the number of pathways across the network to guarantee safety when up to a certain percentage of those pathways are compromised. The conditions under which safety is guaranteed are presented along with the resiliency mechanism itself, and our results are illustrated via simulation.

Regular expression denial of service (ReDoS)— which exploits the super-linear running time of matching regular expressions against carefully crafted inputs—is an emerging class of DoS attacks to web services. One challenging question for a victim web service under ReDoS attacks is how to quickly recover its normal operation after ReDoS attacks, especially these zero-day ones exploiting previously unknown vulnerabilities.In this paper, we present RegexNet, the first payload-based, automated, reactive ReDoS recovery system for web services. RegexNet adopts a learning model, which is updated constantly in a feedback loop during runtime, to classify payloads of upcoming requests including the request contents and database query responses. If detected as a cause leading to ReDoS, RegexNet migrates those requests to a sandbox and isolates their execution for a fast, first-measure recovery.We have implemented a RegexNet prototype and integrated it with HAProxy and Node.js. Evaluation results show that RegexNet is effective in recovering the performance of web services against zero-day ReDoS attacks, responsive on reacting to attacks in sub-minute, and resilient to different ReDoS attack types including adaptive ones that are designed to evade RegexNet on purpose.

As a rich source of data, Call Graphs are used for various applications including security vulnerability detection. Despite multiple studies showing that Call Graphs can drastically improve the accuracy of analysis, existing ecosystem-scale tools like Dependabot do not use Call Graphs and work at the package-level. Using Call Graphs in ecosystem use cases is not practical because of the scalability problems that Call Graph generators have. Call Graph generation is usually considered to be a "full program analysis" resulting in large Call Graphs and expensive computation. To make an analysis applicable to ecosystem scale, this pragmatic approach does not work, because the number of possible combinations of how a particular artifact can be combined in a full program explodes. Therefore, it is necessary to make the analysis incremental. There are existing studies on different types of incremental program analysis. However, none of them focuses on Call Graph generation for an entire ecosystem. In this paper, we propose an incremental implementation of the CHA algorithm that can generate Call Graphs on-demand, by stitching together partial Call Graphs that have been extracted for libraries before. Our preliminary evaluation results show that the proposed approach scales well and outperforms the most scalable existing framework called OPAL.

Advanced persistent threats targeting sensitive corporations, are becoming today stealthier and more complex, coordinating different attacks steps and lateral movements, and trying to stay undetected for long time. Classical security solutions that rely on signature-based detection can be easily thwarted by malware using obfuscation and encryption techniques. More recent solutions are using machine learning approaches for detecting outliers. Nevertheless, the majority of them reason on tabular unstructured data which can lead to missing obvious conclusions. We propose in this paper a novel approach that leverages a combination of both knowledge graphs and machine learning techniques to detect and predict attacks. Using Cyber Threat Intelligence (CTI), we built a knowledge graph that processes event logs in order to not only detect attack techniques, but also learn how to predict them.

Commodity hypervisors are widely deployed to support virtual machines (VMs) on multiprocessor hardware. Their growing complexity poses a security risk. To enable formal verification over such a large codebase, we introduce microverification, a new approach that decomposes a commodity hypervisor into a small core and a set of untrusted services so that we can prove security properties of the entire hypervisor by verifying the core alone. To verify the multiprocessor hypervisor core, we introduce security-preserving layers to modularize the proof without hiding information leakage so we can prove each layer of the implementation refines its specification, and the top layer specification is refined by all layers of the core implementation. To verify commodity hypervisor features that require dynamically changing information flow, we introduce data oracles to mask intentional information flow. We can then prove noninterference at the top layer specification and guarantee the resulting security properties hold for the entire hypervisor implementation. Using microverification, we retrofitted the Linux KVM hypervisor with only modest modifications to its codebase. Using Coq, we proved that the hypervisor protects the confidentiality and integrity of VM data, while retaining KVM’s functionality and performance. Our work is the first machine-checked security proof for a commodity multiprocessor hypervisor.

In a centralized Networked Control System (NCS), all agents share local data with a central processing unit that generates control commands for agents. The use of a communication network between the agents gives NCSs a distinct advantage in efficiency, design cost, and simplicity. However, this benefit comes at the expense of vulnerability to a range of cyber-physical attacks. Recently, novel defense mechanisms to counteract false data injection (FDI) attacks on NCSs have been developed for agents with linear dynamics but have not been thoroughly investigated for NCSs with nonlinear dynamics. This paper proposes an FDI attack mitigation strategy for NCSs composed of agents with nonlinear dynamics under disturbances and measurement noises. The proposed algorithm uses both learning and model-based approaches to estimate agents'states for FDI attack mitigation. A neural network is used to model uncertain dynamics and estimate the effect of FDI attacks. The controller and estimator are designed based on Lyapunov stability analysis. A simulation of robots with Euler-Lagrange dynamics is considered to demonstrate the developed controller's performance to respond to FDI attacks in real-time.

Combinatorial designs are powerful structures for key management in wireless sensor networks to address good connectivity and also security against external attacks in large scale networks. Symmetric key foundation is the most appropriate model for secure exchanges in WSNs among the ideal models. The core objective is to enhance and evaluate certain issues like attack on the nodes, to provide better key strength, better connectivity, security in interaction among the nodes. The keys distributed by the base station to cluster head are generated using Symmetric Balanced Incomplete Block Design (SBIBD). The keys distributed by cluster head to its member nodes are generated using Symmetric Balanced Incomplete Block Design (SBIBD) and Keys are refreshed periodically to avoid stale entries. Compromised sensor nodes can be used to insert false reports (spurious reports) in wireless sensor networks. The idea of interaction between the sensor nodes utilizing keys and building up a protected association helps in making sure the network is secure. Compared with similar existing schemes, our approach can provide better security.

With their role as an integral part of its infrastructure, Industrial Control Systems (ICS) are a vital part of every nation's industrial development drive. Despite several significant advancements - such as controlled-environment agriculture, automated train systems, and smart homes, achieved in critical infrastructure sectors through the integration of Information Systems (IS) and remote capabilities with ICS, the fact remains that these advancements have introduced vulnerabilities that were previously either nonexistent or negligible, one being Remote Access Trojans (RATs). Present RAT detection methods either focus on monitoring network traffic or studying event logs on host systems. This research's objective is the detection of RATs by comparing actual utilized system capacity to reported utilized system capacity. To achieve the research objective, open-source RAT detection methods were identified and analyzed, a GAP-analysis approach was used to identify the deficiencies of each method, after which control algorithms were developed into source code for the solution.

Containers technology radically changed the ways for packaging applications and deploying them as services in cloud environments. According to the recent report on security predictions of 2020 by Trend Micro, the vulnerabilities in container components deployed with cloud architecture have been one of the top security concerns for development and operations teams in enterprises. Docker is one of the leading container technologies that automate the deployment of applications into containers. Docker Hub is a public repository by Docker for storing and sharing the Docker images. These Docker images are pulled from the Docker Hub repository and the security of images being used from the repositories in any cloud environment could be at risk. Vulnerabilities in Docker images could have a detrimental effect on enterprise applications. In this paper, the focus is on securing the Docker images using vulnerability centric approach (VCA) to detect the vulnerabilities. A set of use cases compliant with the NIST SP 800-190 Application Container Security Guide is developed for audit compliance of Docker container images with the OWASP Container Security Verification Standards (CSVS). In this paper, firs vulnerabilities of Docker container images are identified and assessed using the VCA. Then, a set of use cases to identify presence of the vulnerabilities is developed to facilitate the security audit of the container images. Finally, it is illustrated how the proposed use cases can be mapped with the requirements of the OWASP Container Security Verification Standards. The use cases can serve as a security auditing tool during the development, deployment, and maintenance of cloud microservices applications.

This paper presents an observer-based security control scheme for a Cyber-Physical System (CPS). In the considered system, the feedback channel of the CPS may suffer from Denial-of-Service (DoS). To begin with, a time-delayed switching CPS model is constructed according to two different attack situations. And then, based on the switching model, an observer-based controller is designed in the cyber-layer, Meanwhile, the stability of the closed-loop system is analyzed based on H$ınfty$ stability of switching systems in view of Average Dwell Time (ADT). At last, the performance of the proposed security control scheme is illustrated by an numerical example in Simulation.

The blockchain technology evolution in recent times has a hopefulness regarding the impression of self-sovereign identity that has a significant effect on the method of interacting with each other with security over the network. The existing system is not complete and procedural. There arises a different idea of self-sovereign identity methodology. To develop to the possibility, it is necessary to guarantee a better understanding in a proper way. This paper has an in-depth analysis of the attributes of the self-sovereign identity and it affects over the laws of identity that are being explored. The Identity management system(IMS) with no centralized authority is proposed in maintaining the secrecy of records, where as traditional systems are replaced by blockchains and identities are generated cryptographically. This study enables sharing of user data on permissioned blockchain which uses identity-based encryption to maintain access control and data security.

Modern enterprises increasingly take advantage of cloud infrastructures. Yet, outsourcing code and data into the cloud requires enterprises to trust cloud providers not to meddle with their data. To reduce the level of trust towards cloud providers, AMD has introduced Secure Encrypted Virtualization (SEV). By encrypting Virtual Machines (VMs), SEV aims to ensure data confidentiality, despite a compromised or curious Hypervisor. The SEV Encrypted State (SEV-ES) extension additionally protects the VM’s register state from unauthorized access. Yet, both extensions do not provide integrity of the VM’s memory, which has already been abused to leak the protected data or to alter the VM’s control-flow. In this paper, we introduce the SEVerity attack; a missing puzzle piece in the series of attacks against the AMD SEV family. Specifically, we abuse the system’s lack of memory integrity protection to inject and execute arbitrary code within SEV-ES-protected VMs. Contrary to previous code execution attacks against the AMD SEV family, SEVerity neither relies on a specific CPU version nor on any code gadgets inside the VM. Instead, SEVerity abuses the fact that SEV-ES prohibits direct memory access into the encrypted memory. Specifically, SEVerity injects arbitrary code into the encrypted VM through I/O channels and uses the Hypervisor to locate and trigger the execution of the encrypted payload. This allows us to sidestep the protection mechanisms of SEV-ES. Overall, our results demonstrate a success rate of 100% and hence highlight that memory integrity protection is an obligation when encrypting VMs. Consequently, our work presents the final stroke in a series of attacks against AMD SEV and SEV-ES and renders the present implementation as incapable of protecting against a curious, vulnerable, or malicious Hypervisor.

Fully Homomorphic Encryption (FHE) allows a third party to perform arbitrary computations on encrypted data, learning neither the inputs nor the computation results. Hence, it provides resilience in situations where computations are carried out by an untrusted or potentially compromised party. This powerful concept was first conceived by Rivest et al. in the 1970s. However, it remained unrealized until Craig Gentry presented the first feasible FHE scheme in 2009.The advent of the massive collection of sensitive data in cloud services, coupled with a plague of data breaches, moved highly regulated businesses to increasingly demand confidential and secure computing solutions. This demand, in turn, has led to a recent surge in the development of FHE tools. To understand the landscape of recent FHE tool developments, we conduct an extensive survey and experimental evaluation to explore the current state of the art and identify areas for future development.In this paper, we survey, evaluate, and systematize FHE tools and compilers. We perform experiments to evaluate these tools’ performance and usability aspects on a variety of applications. We conclude with recommendations for developers intending to develop FHE-based applications and a discussion on future directions for FHE tools development.

With the explosive growth of IoT applications, billions of things are now connected via edge devices and a colossal volume of data is sent over the internet. Providing security to the user data becomes crucial. The rise in zero-day attacks are a challenge in IoT scenarios. With the large scale of IoT application detection and mitigation of such attacks by the network administrators is cumbersome. The edge device Raspberry pi is remotely logged using Secure Shell (SSH) protocol in 90% of the IoT applications. The case study of SSH brute force attack on the edge device Raspberry pi is demonstrated with experimentation in the IoT networking scenario using Intrusion Detection System (IDS). The IP crawlers available on the internet are used by the attacker to obtain the IP address of the edge device. The proposed system continuously monitors traffic, analysis the log of attack patterns, detects and mitigates SSH brute attack. An attack hijacks and wastes the system resources depriving the authorized users of the resources. With the proposed IDS, we observe 25% CPU conservation, 40% power conservation and 10% memory conservation in resource utilization, as the IDS, mitigates the attack and releases the resources blocked by the attacker.