Biblio

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.

Data provenance (keeping track of who did what, where, when and how) boasts of various attractive use cases for distributed systems, such as intrusion detection, forensic analysis and secure information dependability. This potential, however, can only be realized if provenance is accessible by its primary stakeholders: the end-users. Existing provenance systems are designed in a `all-or-nothing' fashion, making provenance inaccessible, difficult to extract and crucially, not controlled by its key stakeholders. To mitigate this, we propose that provenance be separated into system, data-specific and file-metadata provenance. Furthermore, we expand data-specific provenance as changes at a fine-grain level, or provenance-per-change, that is recorded alongside its source. We show that with the use of delta-encoding, provenance-per-change is viable, asserting our proposed architecture to be effectively realizable.

The security and privacy concerns along with the amount of data that is required to be processed on regular basis has pushed processing to the edge of the computing systems. Deploying advanced Neural Networks (NN), such as deep neural networks (DNNs) and spiking neural networks (SNNs), that offer state-of-the-art results on resource-constrained edge devices is challenging due to the stringent memory and power/energy constraints. Moreover, these systems are required to maintain correct functionality under diverse security and reliability threats. This paper first discusses existing approaches to address energy efficiency, reliability, and security issues at different system layers, i.e., hardware (HW) and software (SW). Afterward, we discuss how to further improve the performance (latency) and the energy efficiency of Edge AI systems through HW/SW-level optimizations, such as pruning, quantization, and approximation. To address reliability threats (like permanent and transient faults), we highlight cost-effective mitigation techniques, like fault-aware training and mapping. Moreover, we briefly discuss effective detection and protection techniques to address security threats (like model and data corruption). Towards the end, we discuss how these techniques can be combined in an integrated cross-layer framework for realizing robust and energy-efficient Edge AI systems.

This paper proposes a logic-based machine learning approach called Acuity which is designed to facilitate user-guided elucidation of novel phenomena from evidence sparsely distributed across large volumes of linked relational data. The work builds on systems from the field of Inductive Logic Programming (ILP) by introducing a suite of new techniques for interacting with domain experts and data sources in a way that allows complex logical reasoning to be strategically exploited on large real-world databases through intuitive hypothesis-shaping and data-caching functionality. We propose two methods for rebutting or shaping candidate hypotheses and two methods for querying or importing relevant data from multiple sources. The benefits of Acuity are illustrated in a proof-of-principle case study involving a retrospective analysis of the CryptoWall ransomware attack using data from a cyber security testbed comprising a small business network and an infected laptop.

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.

The emerging time-sensitive applications, such as industrial automation, smart grids, and telesurgery, pose strong demands for enabling large-scale IP-based deterministic networks. The IETF DetNet working group recently proposes a Cycle Specified Queuing and Forwarding (CSQF) solution. However, CSQF only specifies an underlying device-level primitive while how to achieve network-wide flow scheduling remains undefined. Previous scheduling mechanisms are mostly oriented to the context of local area networks, making them inapplicable to the cyclic traffic in wide area networks. In this paper, we design the Cycle Tags Planning (CTP) mechanism, a first mathematical model to enable network-wide scheduling for cyclic traffic in large-scale deterministic networks. Then, a novel scheduling algorithm named flow offset and cycle shift (FO-CS) is designed to compute the flows' cycle tags. The FO-CS algorithm is evaluated under long-distance network topologies in remote industrial control scenarios. Compared with the Naive algorithm without using FO-CS, simulation results demonstrate that FO-CS improves the scheduling flow number by 31.2% in few seconds.

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.

Nowadays a huge amount of sensitive information is sending via packet data networks and its security doesn't provided properly. Very often information leakage causes huge damage to organizations. One of the mechanisms to cause information leakage when it transmits through a communication channel is to construct a covert channel. Everywhere used packet networks provide huge opportunities for covert channels creating, which often leads to leakage of critical data. Moreover, covert channels based on packet length modifying can function in a system even if traffic encryption is applied and there are some data transfer schemes that are difficult to detect. The purpose of the paper is to construct and examine a normalization protection tool against covert channels. We analyze full and partial normalization, propose estimation of the residual covert channel capacity in a case of counteracting and determine the best parameters of counteraction tool.

In this paper we tackle the open paradoxical challenge of FPGA-accelerated cloud computing: On one hand, clients aim to secure their Intellectual Property (IP) by encrypting their configuration bitstreams prior to uploading them to the cloud. On the other hand, cloud service providers disallow the use of encrypted bitstreams to mitigate rogue configurations from damaging or disabling the FPGA. Instead, cloud providers require a verifiable check on the hardware design that is intended to run on a cloud FPGA at the netlist-level before generating the bitstream and loading it onto the FPGA, therefore, contradicting the IP protection requirement of clients. Currently, there exist no practical solution that can adequately address this challenge.We present the first practical solution that, under reasonable trust assumptions, satisfies the IP protection requirement of the client and provides a bitstream sanity check to the cloud provider. Our proof-of-concept implementation uses existing tools and commodity hardware. It is based on a trusted FPGA shell that utilizes less than 1% of the FPGA resources on a Xilinx VCU118 evaluation board, and an Intel SGX machine running the design checks on the client bitstream.

As a supplement to the cloud computing model, the edge computing model can use edge servers and edge devices to coordinate information processing on the edge of the network to help Internet of Thing (IoT) data storage, transmission, and computing tasks. In view of the complex and changeable situation of edge computing IoT scenarios, this paper proposes a multi-dimensional trust evaluation factor selection scheme. Improve the traditional trusted modeling method based on direct/indirect trust, introduce multi-dimensional trusted decision attributes and rely on the collaboration of edge servers and edge device nodes to infer and quantify the trusted relationship between nodes, and combine the information entropy theory to smoothly weight the calculation results of multi-dimensional decision attributes. Improving the current situation where the traditional trusted assessment scheme's dynamic adaptability to the environment and the lack of reliability of trusted assessment are relatively lacking. Simulation experiments show that the edge computing IoT multi-dimensional trust evaluation model proposed in this paper has better performance than the trusted model in related literature.

In order to analyze the trustworthiness of complex software systems, we propose a model of evidence-based software trustworthiness called trustworthiness derivation tree (TDT). The basic idea of constructing a TDT is to refine main properties into key ingredients and continue the refinement until basic facts such as evidences are reached. The skeleton of a TDT can be specified by a set of rules, which is convenient for automated reasoning in Prolog. We develop a visualization tool that can construct the skeleton of a TDT by taking the rules as input, and allow a user to edit the TDT in a graphical user interface. In a software development life cycle, TDTs can serve as a communication means for different stakeholders to agree on the properties about a system in the requirement analysis phase, and they can be used for deductive reasoning so as to verify whether the system achieves trustworthiness in the product validation phase. We have piloted the approach of using TDTs in more than a dozen real scenarios of software development. Indeed, using TDTs helped us to discover and then resolve some subtle problems.

Three issues should be addressed in ubiquitous power Internet of things (IoT) terminals, such as lack of terminal standardization, high business coupling and weak local intelligent processing ability. The application of operating system in power IoT terminals provides the possibility to solve the above problems, but needs to address the real-time and security problems. In this paper, TrustZone based virtualization architecture is used to tackle the above real-time and security problems, which adopts the dual system architecture of real-time operating system (FreeRTOS) to run real-time tasks, such as power parameter acquisition and control on the real-time operating system, to solve the real-time problem; And non real-time tasks are run on the general operating system(Linux) to solve the expansibility problem of power terminals with hardware assisted virtualization technology achieving the isolation of resources, ensuring the safety of power related applications. The scheme is verified on the physical platform. The results show that the dual operating system power IoT terminal scheme based on ARM TrustZone meets the security requirements and has better real-time performance, with unifying terminal standards, business decoupling and enhancing local processing capacity.

In recent years, the “whole chain” development level of China's intellectual property creation, protection and application has been greatly improved. At the same time, cloud computing technology is booming, and intellectual property data distributed platforms based on cloud storage are emerging one after another. Firstly, this paper introduces the domestic intellectual property cloud platform services from the perspectives of government, state-owned enterprises and private enterprises; Secondly, four typical distributed platforms provided by commercial resources are selected to summarize the problems faced by the operation mode of domestic intellectual property services; Then, it compares and discusses the functions and service modes of domestic intellectual property distributed platform, and takes TSITE IP as an example, puts forward the design and construction strategies of intellectual property protection, intellectual property operation service distributed platform and operation service mode under the background of information age. Finally, according to the development of contemporary information technology, this paper puts forward challenges and development direction for the future development of intellectual property platform.

In many distributed learning setups such as federated learning, client nodes at the edge use individually collected data to compute the local gradients and send them to a central master server, and the master aggregates the received gradients and broadcasts the aggregation to all clients with which the clients can update the global model. As straggling communication links could severely affect the performance of distributed learning system, Prakash et al. proposed to utilize helper nodes and coding strategy to achieve resiliency against straggling client-to-helpers links. In this paper, we propose two coding schemes: repetition coding (RC) and MDS coding both of which enable the clients to update the global model in the presence of only helpers but without the master. Moreover, we characterize the uplink and downlink communication loads, and prove the tightness of uplink communication load. Theoretical tradeoff between uplink and downlink communication loads is established indicating that larger uplink communication load could reduce downlink communication load. Compared to Prakash's schemes which require a master to connect with helpers though noiseless links, our scheme can even reduce the communication load in the absence of master when the number of clients and helpers is relatively large compared to the number of straggling links.

Kotlin is a relatively new programming language from JetBrains: its development started in 2010 with release 1.0 done in early 2016. The Kotlin compiler, while slowly and steadily becoming more and more mature, still crashes from time to time on the more tricky input programs, not least because of the complexity of its features and their interactions. This makes it a great target for fuzzing, even the basic forms of which can find a significant number of Kotlin compiler crashes. There is a problem with fuzzing, however, closely related to the cause of the crashes: generating a random, non-trivial and semantically valid Kotlin program is hard. In this paper, we talk about type-centriccompilerfuzzing in the form of type-centricenumeration, an approach inspired by skeletal program enumeration [1] and based on a combination of generative and mutation-based fuzzing, which solves this problem by focusing on program types. After creating the skeleton program, we fill the typed holes with fragments of suitable type, created via generation and enhanced by semantic-aware mutation. We implemented this approach in our Kotlin compiler fuzzing framework called Backend Bug Finder (BBF) and did an extensive evaluation, not only testing the real-world feasibility of our approach, but also comparing it to other compiler fuzzing techniques. The results show our approach to be significantly better compared to other fuzzing approaches at generating semantically valid Kotlin programs, while creating more interesting crash-inducing inputs at the same time. We managed to find more than 50 previously unknown compiler crashes, of which 18 were considered important after their triage by the compiler team.

Integrated Gradients as an attribution method for deep neural network models offers simple implementability. However, it suffers from noisiness of explanations which affects the ease of interpretability. The SmoothGrad technique is proposed to solve the noisiness issue and smoothen the attribution maps of any gradient-based attribution method. In this paper, we present SmoothTaylor as a novel theoretical concept bridging Integrated Gradients and SmoothGrad, from the Taylor's theorem perspective. We apply the methods to the image classification problem, using the ILSVRC2012 ImageNet object recognition dataset, and a couple of pretrained image models to generate attribution maps. These attribution maps are empirically evaluated using quantitative measures for sensitivity and noise level. We further propose adaptive noising to optimize for the noise scale hyperparameter value. From our experiments, we find that the SmoothTaylor approach together with adaptive noising is able to generate better quality saliency maps with lesser noise and higher sensitivity to the relevant points in the input space as compared to Integrated Gradients.

Phishing URLs mainly target individuals and/or organizations through social engineering attacks by exploiting the humans' weaknesses in information security awareness. These URLs lure online users to access fake websites, and harvest their confidential information, such as debit/credit card numbers and other sensitive information. In this work, we introduce a phishing detection technique based on URL lexical analysis and machine learning classifiers. The experiments were carried out on a dataset that originally contained 1056937 labeled URLs (phishing and legitimate). This dataset was processed to generate 22 different features that were reduced further to a smaller set using different features reduction techniques. Random Forest, Gradient Boosting, Neural Network and Support Vector Machine (SVM) classifiers were all evaluated, and results show the superiority of SVMs, which achieved the highest accuracy in detecting the analyzed URLs with a rate of 99.89%. Our approach can be incorporated within add-on/middleware features in Internet browsers for alerting online users whenever they try to access a phishing website using only its URL.

Little can be achieved in the design of security protocols without trusting at least some participants. This trust should be justified or, at the very least, subject to examination. One way to strengthen trustworthiness is to hold parties accountable for their actions, as this provides a strong incentive to refrain from malicious behavior. This has led to an increased interest in accountability in the design of security protocols. In this work, we combine the accountability definition of Künnemann, Esiyok, and Backes [21] with the notion of case tests to extend its applicability to protocols with unbounded sets of participants. We propose a general construction of verdict functions and a set of verification conditions that achieve soundness and completeness. Expressing the verification conditions in terms of trace properties allows us to extend TAMARIN - a protocol verification tool - with the ability to analyze and verify accountability properties in a highly automated way. In contrast to prior work, our approach is significantly more flexible and applicable to a wider range of protocols.

In a beyond-5G (B5G) scenario, we consider a virtual private mobile network (VPMN), i.e., a set of user equipments (UEs) directly communicating in a device-to-device (D2D) fashion, and connected to the cellular network by multiple gateways. The purpose of the VPMN is to hide the position of the VPMN UEs to the mobile network operator (MNO). We investigate the design and performance of packet routing inside the VPMN. First, we note that the routing that maximizes the rate between the VPMN and the cellular network leads to an unbalanced use of the gateways by each UE. In turn, this reveals information on the location of the VPMN UEs. Therefore, we derive a routing algorithm that maximizes the VPMN rate, while imposing for each UE the same data rate at each gateway, thus hiding the location of the UE. We compare the performance of the resulting solution, assessing the location privacy achieved by the VPMN, and considering both the case of single hop and multihop in the transmissions from the UEs to the gateways.

Mobile cloud computing has suggested as a viable technology as a result of the fast growth of mobile applications and the emergence of the cloud computing idea. Mobile cloud computing incorporates cloud computing into the mobile environment and addresses challenges in mobile cloud computing applications like (processing capacity, battery storage capacity, privacy, and security). We discuss the enabling technologies and obstacles that we will face when we transition from mobile computing to mobile cloud computing to develop next-generation mobile cloud applications. This paper provides an overview of the processes and open concerns for mobility in mobile cloud computing for augmented reality service provisioning. This paper outlines the concept, system architecture, and taxonomy of virtualization technology, as well as research concerns related to virtualization security, and suggests future study fields. Furthermore, we highlight open challenges to provide light on the future of mobile cloud computing and future development.

The automatic detection of vulnerabilities in Web applications using taint analysis is a hot topic. However, existing taint analysis methods for sanitizers identification are too simple to find available taint transmission chains effectively. These methods generally use pre-constructed dictionaries or simple keywords to identify, which usually suffer from large false positives and false negatives. No doubt, it will have a greater impact on the final result of the taint analysis. To solve that, we summarise and classify the commonly used sanitizers in Web applications and propose an identification method based on semantic analysis. Our method can accurately and completely identify the sanitizers in the target Web applications through static analysis. Specifically, we analyse the natural semantics and program semantics of existing sanitizers, use semantic analysis to find more in Web applications. Besides, we implemented the method prototype in PHP and achieved a vulnerability detection tool called VulChecker. Then, we experimented with some popular open-source CMS frameworks. The results show that Vulchecker can accurately identify more sanitizers. In terms of vulnerability detection, VulChecker also has a lower false positive rate and a higher detection rate than existing methods. Finally, we used VulChecker to analyse the latest PHP applications. We identified several new suspicious taint data propagation chains. Before the paper was completed, we have identified four unreported vulnerabilities. In general, these results show that our approach is highly effective in improving vulnerability detection based on taint analysis.

With the further development in the fields of computer vision, network security, natural language processing and so on so forth, deep learning technology gradually exposed certain security risks. The existing deep learning algorithms cannot effectively describe the essential characteristics of data, making the algorithm unable to give the correct result in the face of malicious input. Based on current security threats faced by deep learning, this paper introduces the problem of adversarial examples in deep learning, sorts out the existing attack and defense methods of black box and white box, and classifies them. It briefly describes the application of some adversarial examples in different scenarios in recent years, compares several defense technologies of adversarial examples, and finally summarizes the problems in this research field and prospects its future development. This paper introduces the common white box attack methods in detail, and further compares the similarities and differences between the attack of black and white boxes. Correspondingly, the author also introduces the defense methods, and analyzes the performance of these methods against the black and white box attack.

Many people are becoming more reliant on internet social media sites like Facebook. Users can utilize these networks to reveal articles to them and engage with your peers. Several of the data transmitted from these connections is intended to be confidential. However, utilizing publicly available data and learning algorithms, it is feasible to forecast concealed informative data. The proposed research work investigates the different ways to initiate deduction attempts on freely released photo sharing data in order to envisage concealed informative data. Next, this research study offers three distinct sanitization procedures that could be used in a range of scenarios. Moreover, the effectualness of all these strategies and endeavor to utilize collective teaching and research to reveal important bits of the data set are analyzed. It shows how, by using the sanitization methods presented here, a user may lower the accuracy by including both global and interpersonal categorization techniques.

Pooling together samples and testing the resulting mixture is gaining considerable interest as a potential method to markedly increase the rate of testing for SARS-CoV-2, given the resource limited conditions. Such pooling can also be employed for carrying out large scale diagnostic testing of other infectious diseases, especially when the available resources are limited. Therefore, it has become important to design a user-friendly tool to assist clinicians and policy makers, to determine optimal testing pool and sub-pool sizes for their specific scenarios. We have developed such a tool; the calculator web application is available at https://riteshsingh.github.io/poolsize/. The algorithms employed are described and analyzed in this paper, and their application to other scientific fields is also discussed. We find that pooling always reduces the expected number of tests in all the conditions, at the cost of test sensitivity. The No sub-pooling optimal pool size calculator will be the most widely applicable one, because limitations of sample quantity will restrict sub-pooling in most conditions.

The heterogeneous massive logs incoming from multiple sources pose major challenges to professionals responsible for IT security and system administrator. One of the challenges is to develop a scalable heterogeneous logs database for storage and further analysis. In fact, it is difficult to decide which database is suitable for the needs, the best of a use case, execution time and storage performances. In this paper, we explore, study, and compare the performance of SQL and NoSQL databases on large heterogeneous event logs. We implement the relational database using MySQL, the column-oriented database using Impala on the top of Hadoop, and the graph database using Neo4j. We experiment the databases on a large heterogeneous logs and provide advice, the pros and cons of each SQL and NoSQL database. Our findings that Impala outperforms MySQL and Neo4j databases in terms of loading logs, execution time of simple queries, and storage of logs. However, Neo4j outperforms Impala and MySQL in the execution time of complex queries.