Biblio

Shared register emulations on top of message-passing systems provide an illusion of a simpler shared memory system which can make the task of a system designer easier. Numerous shared register applications have a considerably high read-to-write ratio. Thus, having algorithms that make reads more efficient than writes is a fair trade-off.Typically, such algorithms for reads and writes are asymmetric and sacrifice the stringent consistency condition atomicity, as it is impossible to have fast reads for multi-writer atomicity. Safety is a consistency condition that has has gathered interest from both the systems and theory community as it is weaker than atomicity yet provides strong enough guarantees like "strong consistency" or read-my-write consistency. One requirement that is assumed by many researchers is that of the reliable broadcast (RB) primitive, which ensures the "all or none" property during a broadcast. One drawback is that such a primitive takes 1.5 rounds to complete and requires server-to-server communication.This paper implements an efficient multi-writer multi-reader safe register without using a reliable broadcast primitive. Moreover, we provide fast reads or one-shot reads – our read operations can be completed in one round of client-to-server communication. Of course, this comes with the price of requiring more servers when compared to prior solutions assuming reliable broadcast. However, we show that this increased number of servers is indeed necessary as we prove a tight bound on the number of servers required to implement Byzantine-fault tolerant safe registers in a system without reliable broadcast.We extend our results to data stored using erasure coding as well. We present an emulation of single-writer multi-reader safe register based on MDS codes. The usage of MDS codes reduces storage and communication costs. On the negative side, we also show that to use MDS codes and at the same time achieve one-shot reads, we need even more servers.

Open-source development is a well-accepted model by software development communities from both academia and industry. Many companies and corporations adopt and use open source systems daily as a core component in their business activities. One of the most important factors that will determine the success of this model is security. The security of software systems is a combination of source code quality, stability, and vulnerabilities. Software vulnerabilities can be introduced by many factors, some of which are the way that programmers write their programs, their background on security standards, and safe programming practices. This paper describes a cloud-based software tool developed by the authors that can help our computing communities in both academia and research to evaluate their software systems on the source code level to help them identify and detect some of the well-known source code vulnerability patterns that can cause security issues if maliciously exploited. The paper also presents an empirical study on the prevalence of vulnerable C/C++ coding patterns inside three large-scale open-source systems comprising more than 42 million lines of source code. The historical data for the studied systems is presented over five years to uncover some historical trends to highlight the changes in the system analyzed over time concerning the presence of some of the source code vulnerabilities patterns. The majority of results show the continued usage of known unsafe functions.

Antisparse coding aims at spreading the information uniformly over representation coefficients and can be expressed as the solution of an ℓ∞-norm regularized problem. In this paper, we propose a new methodology, coined "safe squeezing", accelerating the computation of antisparse representations. The idea consists in identifying saturated entries of the solution via simple tests and compacting their contribution to achieve some form of dimensionality reduction. Numerical experiments show that the proposed approach leads to significant computational gain.

The increasing using of IoT technologies in the industrial sector creates new challenges for the information security of such systems. Using IoT-devices for building SCADA systems cause standard protocols and public networks for data transmitting. Commercial off-the-shelf devices and systems are a new base for industrial control systems, which have high-security risks. There are some useful models are exist for security analysis of information systems, but they do not take into account IoT architecture. The nested attributed metagraph model for the security of IoT-based solutions is proposed and discussed.

With the increase of the information level of SCADA system in recent years, the attacks against SCADA system are also increasing. Therefore, more and more scholars are beginning to study the safety of SCADA systems. Game theory is a balanced decision involving the main body of all parties. In recent years, domestic and foreign scholars have applied game theory to SCADA systems to achieve active defense. However, their research often focuses on the entire SCADA system, and the game theory is solved for the entire SCADA system, which is not flexible enough, and the calculation cost is also high. In this paper, a dynamic local game model (DLGM) for power SCADA system is proposed. This model first obtains normal data to form a whitelist, then dynamically detects each attack of the attacker's SCADA system, and through white list to determine the node location of the SCADA system attacked by the attacker, then obtains the smallest system attacked by SCADA system, and finally performs a local dynamic game algorithm to find the best defense path. Experiments show that DLGM model can find the best defense path more effectively than other game strategies.

The advantages of measuring multiple wireless links simultaneously has been gaining attention due to the growing complexity of wireless communication systems. Analyzing vehicular communication systems presents a particular challenge due to their rapid time-varying nature. Therefore multi-node channel sounding is crucial for such endeavors. In this paper, we present the architecture and practical implementation of a scalable mobile multi-node channel sounder, optimized for use in vehicular scenarios. We perform a measurement campaign with three moving nodes, which includes a line of sight (LoS) connection on two links and non LoS(NLoS) conditions on the third link. We present the results on the obtained channel delay and Doppler characteristics, followed by the assessment of the degree of correlation of the analyzed channels and time-variant channel rates, hence investigating the suitability of the channel's physical attributes for relaying. The results show low cross-correlation between the transfer functions of the direct and the relaying link, while a higher rate is calculated for the relaying link.

Mining of data is used to analyze facts to discover formerly unknown patterns, classifying and grouping the records. There are several crucial scalable statistics mining platforms that have been developed in latest years. RapidMiner is a famous open source software which can be used for advanced analytics, Weka and Orange are important tools of machine learning for classifying patterns with techniques of clustering and regression, whilst Knime is often used for facts preprocessing like information extraction, transformation and loading. This article encapsulates the most important and robust platforms.

The ability to advance the state of the art in automated cybersecurity protections for industrial control systems (ICS) has as a prerequisite of understanding the trade-off space. That is, to enable a cyber feedback loop in a control system environment you must first consider both the security mitigation available, the benefits and the impacts to the control system functionality when the mitigation is used. More damaging impacts could be precipitated that the mitigation was intended to rectify. This paper details networked ICS that controls a simulation of the frequency response represented with the swing equation. The microgrid loads and base generation can be balanced through the control of an emulated battery and power inverter. The simulated plant, which is implemented in Raspberry Pi computers, provides an inexpensive platform to realize the physical effects of cyber attacks to show the trade-offs of available mitigating actions. This network design can include a commercial ICS controller and simple plant or emulated plant to introduce real world implementation of feedback controls, and provides a scalable, physical effects measurable microgrid for cyber resilience analysis (SPEMMCRA).

Binary code search has received much attention recently due to its impactful applications, e.g., plagiarism detection, malware detection and software vulnerability auditing. However, developing an effective binary code search tool is challenging due to the gigantic syntax and structural differences in binaries resulted from different compilers, compiler options and malware family. In this paper, we propose a scalable and accurate binary search engine which performs syntactic matching by combining a set of key techniques to address the challenges above. The key contribution is binary code searching technique which combined function filtering and partial trace method to match the function code relatively quick and accurate. In addition, a simhash and basic information based function filtering is proposed to dramatically reduce the irrelevant target functions. Besides, we introduce a partial trace method for matching the shortlisted function accurately. The experimental results show that our method can find similar functions, even with the presence of program structure distortion, in a scalable manner.

Automated techniques for rigorous floating-point round-off error analysis are a prerequisite to placing important activities in HPC such as precision allocation, verification, and code optimization on a formal footing. Yet existing techniques cannot provide tight bounds for expressions beyond a few dozen operators-barely enough for HPC. In this work, we offer an approach embedded in a new tool called SATIHE that scales error analysis by four orders of magnitude compared to today's best-of-class tools. We explain how three key ideas underlying SATIHE helps it attain such scale: path strength reduction, bound optimization, and abstraction. SATIHE provides tight bounds and rigorous guarantees on significantly larger expressions with well over a hundred thousand operators, covering important examples including FFT, matrix multiplication, and PDE stencils.

Fault effect simulation is a well-established technique for the qualification of robust embedded software and hardware as required by different safety standards. Our article introduces a Virtual Prototype based approach for the fault analysis and fast simulation of a set of automatically generated and target compiled software programs. The approach scales to different RISC-V ISA standard subset configurations and is based on an instruction and hardware register coverage for automatic fault injections of permanent and transient bitflips. The analysis of each software binary evaluates its opcode type and register access coverage including the addressed memory space. Based on this information dedicated sets of fault injected hardware models, i.e., mutants, are generated. The simulation of all mutants conducted with the different binaries finally identifies the cases with a normal termination though executed on a faulty hardware model. They are identified as a subject for further investigations and improvements by the implementation of additional hardware or software safety countermeasures. Our final evaluation results with automatic C code generation, compilation, analysis, and simulation show that QEMU provides an adequate efficient platform, which also scales to more complex scenarios.

This paper presents a novel method, combining new formulations and sampling, to improve the scalability of sum-of-squares (SOS) programming-based system verification. Region-of-attraction approximation problems are considered for polynomial, polynomial with generalized Lur'e uncertainty, and rational trigonometric multi-rigid-body systems. Our method starts by identifying that Lagrange multipliers, traditionally heavily used for S-procedures, are a major culprit of creating bloated SOS programs. In light of this, we exploit inherent system properties-continuity, convexity, and implicit algebraic structure-and reformulate the problems as quotient-ring SOS programs, thereby eliminating all the multipliers. These new programs are smaller, sparser, less constrained, yet less conservative. Their computation is further improved by leveraging a recent result on sampling algebraic varieties. Remarkably, solution correctness is guaranteed with just a finite (in practice, very small) number of samples. Altogether, the proposed method can verify systems well beyond the reach of existing SOS-based approaches (32 states); on smaller problems where a baseline is available, it computes tighter solution 2-3 orders of magnitude faster.

Technological progress in cloud networking, 5G networks, and the IoT (Internet of Things) are remarkable. In addition, demands for flexible construction of SoEs (Systems on Engagement) for various type of businesses are increasing. In such environments, dynamic changes of network rules, such as access control (AC) or packet forwarding, are required to ensure function and security in networks. On the other hand, it is becoming increasingly difficult to grasp the exact situation in such networks by utilizing current well-known network verification technologies since a huge number of network rules are complexly intertwined. To mitigate these issues, we have proposed a scalable network verification approach utilizing the concept of "Packet Equivalence Class (PEC)," which enable precise network function verification by strictly recognizing the impact range of each network rule. However, this approach is still not scalable for very large-scale networks which consist of tens of thousands of routers. In this paper, we enhanced our impact range detection algorithm for practical large-scale networks. Through evaluation in the network with more than 80,000 AC rules, we confirmed that our enhanced algorithm can achieve precise impact range detection in under 600 seconds.

The security of wireless network devices has received widespread attention, but most existing schemes cannot achieve fine-grained device identification. In practice, the security vulnerabilities of a device are heavily depending on its model and firmware version. Motivated by this issue, we propose a universal, extensible and device-independent framework called SCAFFISD, which can provide fine-grained identification of wireless routers. It can generate access rules to extract effective information from the router admin page automatically and perform quick scans for known device vulnerabilities. Meanwhile, SCAFFISD can identify rogue access points (APs) in combination with existing detection methods, with the purpose of performing a comprehensive security assessment of wireless networks. We implement the prototype of SCAFFISD and verify its effectiveness through security scans of actual products.

The sophistication and complexity of recent exploitation techniques, which rely on memory disclosure and whole-function reuse to bypass address space layout randomization and control flow integrity, is indicative of the effect that the combination of exploit mitigations has in challenging the construction of reliable exploits. In addition to software diversification and control flow enforcement, recent efforts have focused on the complementary approach of code and API specialization to restrict further the critical operations that an attacker can perform as part of a code reuse exploit. In this paper we propose Saffire, a compiler-level defense against code reuse attacks. For each calling context of a critical function, Saffire creates a specialized and hardened replica of the function with a restricted interface that can accommodate only that particular invocation. This is achieved by applying staticargumentbinding, to eliminate arguments with static values and concretize them within the function body, and dynamicargumentbinding, which applies a narrow-scope form of data flow integrity to restrict the acceptable values of arguments that cannot be statically derived. We have implemented Saffire on top of LLVM, and applied it to a set of 11 applications, including Nginx, Firefox, and Chrome. The results of our experimental evaluation with a set of 17 real-world ROP exploits and three whole-function reuse exploits demonstrate the effectiveness of Saffire in preventing these attacks while incurring a negligible runtime overhead.

The problem of optimizing the security policy for the composite information system is formulated. Subject-object model for information system is used. Combining different types of security policies is formalized. The target function for the optimization task is recorded. The optimization problem for combining two discretionary security policies is solved. The case of combining two mandatory security policies is studied. The main problems of optimization the composite security policy are formulated. +50 CHMBOJIOB‼!

Software Quality Testing has always been a crucial part of the software development process and lately, there has been a rise in the usage of testing applications. While a well-planned and performed test, regardless of its nature - automated or manual, is a key factor when deciding on the results of the test, it is often not enough to give a more deep and thorough view of the whole process. That can be achieved with properly selected software metrics that can be used for proper risk assessment and evaluation of the development.This paper considers the most commonly used metrics when measuring a performed test and examines metrics that can be applied in the development process.

A metric and structure of computing 2020 is proposed in the form of Top 12 Technology Trends, which will influence on investment in science, education and industry in developing countries. The primary social and technological problem of the protection of society and critical facilities through the creation of Global Intelligent Cyber Security is formulated. The axioms of the constructive formation of developing countries on the basis of the adoption of moral relations are formulated. Models, methods and algorithms of cyber-social computing are proposed that are focused on processing big data, searching for keywords and test fragments. New characteristic equations of similarity - differences between the processes and phenomena are synthesized for the exact information retrieval by keywords in cyber-physical space. A computing model of the development of the Universe is formulated, where the binary interactions of entities and forms are harmonic functions of the phase state. A structure of interactive computing of the creative process based on a metric assessment of the development status with world achievements is proposed.

The blockchain technology revolution and the use of blockchains in various applications have resulted in many companies and programmers developing and customizing specific fit-for-purpose consensus algorithms. Security and performance are determined by the chosen consensus algorithm; hence, the reliability and security of these algorithms must be assured and tested, which requires an understanding of all the security assumptions that make such algorithms correct and byzantine fault-tolerant.This paper studies the "security ingredients" that enable a given consensus algorithm to achieve safety, liveness, and byzantine fault tolerance (BFT) in both permissioned and permissionless blockchain systems. The key contributions of this paper are the organization of these requirements and a new taxonomy that describes the requirements for security. The CAP Theorem is utilized to explain important tradeoffs between consistency and availability in consensus algorithm design, which are crucial depending on the specific application of a given algorithm. This topic has also been explored previously by De Angelis. However, this paper expands that prior explanation and dilemma of consistency vs. availability and then combines this with Buterin's Trilemma to complete the overall exposition of tradeoffs.

Initially, legitimate users were working under a normal web browser to do all activities over the internet [1]. To get more secure service and to get protection against Bot activity, the legitimate users switched their activity from Normal web browser to low latency anonymous communication such as Tor Browser. The Traffic monitoring in Tor Network is difficult as the packets are traveling from source to destination in an encrypted fashion and the Tor network hides its identity from destination. But lately, even the illegitimate users such as attackers/criminals started their activity on the Tor browser. The secured Tor network makes the detection of Botnet more difficult. The existing tools for botnet detection became inefficient against Tor-based bots because of the features of the Tor browser. As the Tor Browser is highly secure and because of the ethical issues, doing practical experiments on it is not advisable which could affect the performance and functionality of the Tor browser. It may also affect the endanger users in situations where the failure of Tor's anonymity has severe consequences. So, in the proposed research work, Private Tor Networks (PTN) on physical or virtual machines with dedicated resources have been created along with Trusted Middle Node. The motivation behind the trusted middle node is to make the Private Tor network more efficient and to increase its performance.

A significant challenge in modern computer security is the growing skill gap as intruder capabilities increase, making it necessary to begin automating elements of penetration testing so analysts can contend with the growing number of cyber threats. In this paper, we attempt to assist human analysts by automating a single host penetration attack. To do so, a smart agent performs different attack sequences to find vulnerabilities in a target system. As it does so, it accumulates knowledge, learns new attack sequences and improves its own internal penetration testing logic. As a result, this agent (AgentPen for simplicity) is able to successfully penetrate hosts it has never interacted with before. A computer security administrator using this tool would receive a comprehensive, automated sequence of actions leading to a security breach, highlighting potential vulnerabilities, and reducing the amount of menial tasks a typical penetration tester would need to execute. To achieve autonomy, we apply an unsupervised machine learning algorithm, Q-learning, with an approximator that incorporates a deep neural network architecture. The security audit itself is modelled as a Markov Decision Process in order to test a number of decision-making strategies and compare their convergence to optimality. A series of experimental results is presented to show how this approach can be effectively used to automate penetration testing using a scalable, i.e. not exhaustive, and adaptive approach.

Ciphertext policy attribute based encryption (CP-ABE) can provide high finegrained access control for cloud storage. However, it needs to solve problems such as property privacy protection, ciphertext search and data update in the application process. Therefore, based on CP-ABE scheme, this paper proposes a dynamically updatable searchable encryption cloud storage (DUSECS) scheme. Using the characteristics of homomorphic encryption, the encrypted data is compared to achieve efficient hiding policy. Meanwhile, adopting linked list structure, the DUSECS scheme realizes the dynamic data update and integrity detection, and the search encryption against keyword guessing attacks is achieved by combining homomorphic encryption with aggregation algorithm. The analysis of security and performance shows that the scheme is secure and efficient.

Cloud computing provides an appearing application for compelling vision in managing big-data files and responding queries over a distributed cloud platform. To overcome privacy revealing risks, sensitive documents and private data are usually stored in the clouds in a cipher-based manner. However, it is inefficient to search the data in traditional encryption systems. Searchable encryption is a useful cryptographic primitive to enable users to retrieve data in ciphertexts. However, the traditional searchable encryptions provide lower search efficiency and cannot carry out fuzzy multikeyword queries. To solve this issue, in this article, we propose a searchable encryption that supports privacy-preserving fuzzy multikeyword search (SE-PPFM) in cloud systems, which is built by asymmetric scalar-product-preserving encryptions and Hadamard product operations. In order to realize the functionality of efficient fuzzy searches, we employ Word2vec as the primitive of machine learning to obtain a fuzzy correlation score between encrypted data and queries predicates. We analyze and evaluate the performance in terms of token of multikeyword, retrieval and match time, file retrieval time and matching accuracy, etc. The experimental results show that our scheme can achieve a higher efficiency in fuzzy multikeyword ciphertext search and provide a higher accuracy in retrieving and matching procedure.

Sybils are users created for carrying out nefarious actions in online social networks (OSNs) and threaten the security of OSNs. Therefore, Sybil detection is an urgent security task, and various detection methods have been proposed. Existing Sybil detection methods are based on the relationship (i.e., graph structure) of users in OSNs. Structure-based methods can be classified into two categories: Random Walk (RW)-based and Belief Propagation (BP)-based. However, although almost all methods have been experimentally evaluated in terms of their performance and robustness to noise, the theoretical understanding of them is insufficient. In this paper, we interpret the Sybil detection problem from the viewpoint of graph signal processing and provide a framework to formulate RW- and BPbased methods as low-pass filtering. This framework enables us to theoretically compare RW- and BP-based methods and explain why BP-based methods perform well for scale-free graphs, unlike RW-based methods. Furthermore, by this framework, we relate RW- and BP-based methods and Graph Neural Networks (GNNs) and discuss the difference among these methods. Finally, we evaluate the validity of this framework through numerical experiments.

Spiking Neural Networks (SNNs) claim to present many advantages in terms of biological plausibility and energy efficiency compared to standard Deep Neural Networks (DNNs). Recent works have shown that DNNs are vulnerable to adversarial attacks, i.e., small perturbations added to the input data can lead to targeted or random misclassifications. In this paper, we aim at investigating the key research question: "Are SNNs secure?" Towards this, we perform a comparative study of the security vulnerabilities in SNNs and DNNs w.r.t. the adversarial noise. Afterwards, we propose a novel black-box attack methodology, i.e., without the knowledge of the internal structure of the SNN, which employs a greedy heuristic to automatically generate imperceptible and robust adversarial examples (i.e., attack images) for the given SNN. We perform an in-depth evaluation for a Spiking Deep Belief Network (SDBN) and a DNN having the same number of layers and neurons (to obtain a fair comparison), in order to study the efficiency of our methodology and to understand the differences between SNNs and DNNs w.r.t. the adversarial examples. Our work opens new avenues of research towards the robustness of the SNNs, considering their similarities to the human brain's functionality.