Biblio

Due to the extensive use of network services and emerging security threats, enterprise networks deploy varieties of security devices for controlling resource access based on organizational security requirements. These requirements need fine-grained access control rules based on heterogeneous isolation patterns like access denial, trusted communication, and payload inspection. Organizations are also seeking for usable and optimal security configurations that can harden the network security within enterprise budget constraints. In order to design a security architecture, i.e., the distribution of security devices along with their security policies, that satisfies the organizational security requirements as well as the business constraints, it is required to analyze various alternative security architectures considering placements of network security devices in the network and the corresponding access controls. In this paper, we present an automated formal framework for synthesizing network security configurations. The main design alternatives include different kinds of isolation patterns for network traffic flows. The framework takes security requirements and business constraints along with the network topology as inputs. Then, it synthesizes cost-effective security configurations satisfying the constraints and provides placements of different security devices, optimally distributed in the network, according to the given network topology. In addition, we provide a hypothesis testing-based security architecture refinement mechanism that explores various security design alternatives using ConfigSynth and improves the security architecture by systematically increasing the security requirements. We demonstrate the execution of ConfigSynth and the refinement mechanism using case studies. Finally, we evaluate their scalability using simulated experiments.
 

Data Analytics is at the core of almost all modern ap-plications ranging from science and finance to healthcare and web applications. The evolution of data analytics over the last decade has been dramatic - new methods, new tools and new platforms - with no slowdown in sight. This rapid evolution has pushed the boundaries of data analytics along several axis including scalability especially with the rise of distributed infrastructures and the Big Data era, and interoperability with diverse data management systems such as relational databases, Hadoop and Spark. However, many analytic application developers struggle with the challenge of production deployment. Recent experience suggests that it is difficult to deliver modern data analytics with the level of reliability, security and manageability that has been a feature of traditional SQL DBMSs. In this tutorial, we discuss the advances and innovations introduced at both the infrastructure and algorithmic levels, directed at making analytic workloads scale, while paying close attention to the kind of quality of service guarantees different technology provide. We start with an overview of the classical centralized analytical techniques, describing the shift towards distributed analytics over non-SQL infrastructures. We contrast such approaches with systems that integrate analytic functionality inside, above or adjacent to SQL engines. We also explore how Cloud platforms' virtualization capabilities make it easier - and cheaper - for end users to apply these new analytic techniques to their data. Finally, we conclude with the learned lessons and a vision for the near future.

In most PUF-based authentication schemes, a central server is usually engaged to verify the response of the device’s PUF to challenge bit-streams. However, the server availability may be intermittent in practice. To tackle such an issue, this paper proposes a new protocol for supporting distributed authentication while avoiding vulnerability to information leakage where CRPs could be retrieved from hacked devices and collectively used to model the PUF. The main idea is to provision for scrambling the challenge bit-stream in a way that is dependent on the verifier. The scrambling pattern varies per authentication round for each device and independently across devices. In essence, the scrambling function becomes node- and packetspecific and the response received by two verifiers of one device for the same challenge bit-stream could vary. Thus, neither the scrambling function can be reverted, nor the PUF can be modeled even by a collusive set of malicious nodes. The validation results using data of an FPGA-based implementation demonstrate the effectiveness of our approach in thwarting PUF modeling attacks by collusive actors. We also discuss the approach resiliency against impersonation, Sybil, and reverse engineering attacks.

Blockchain is a relatively new technology, a distributed database used for sharing between nodes of computer networks. A blockchain stores all information in automated digital format as a database. Blockchain innovation ensures the accuracy and security of the data record and generates trust without the need for a trusted third party. The objectives of this paper are to determine the security risk of the blockchain systems, analyze the vulnerabilities exploited on the blockchain, and identify recent security challenges that the blockchain faces. This review paper presents some of the previous studies of the security threats that blockchain faces and reviews the security enhancement solutions for blockchain vulnerabilities. There are some studies on blockchain security issues, but there is no systematic examination of the problem, despite the blockchain system’s security threats. An observational research methodology was used in this research. Through this methodology, many research related to blockchain threats and vulnerabilities obtained. The outcomes of this research are to Identify the most important security threats faced by the blockchain and consideration of security recently vulnerabilities. Processes and methods for dealing with security concerns are examined. Intelligent corporate security academic challenges and limitations are covered throughout this review. The goal of this review is to serve as a platform as well as a reference point for future work on blockchain-based security.

FPGA bitstream protection schemes are often the first line of defense for secure hardware designs. In general, breaking the bitstream encryption would enable attackers to subvert the confidentiality and infringe on the IP. Or breaking the authenticity enables manipulating the design, e.g., inserting hardware Trojans. Since FPGAs see widespread use in our interconnected world, such attacks can lead to severe damages, including physical harm. Recently we [1] presented a surprising attack — Starbleed — on Xilinx 7-Series FPGAs, tricking an FPGA into acting as a decryption oracle. For their UltraScale(+) series, Xilinx independently upgraded the security features to AES-GCM, RSA signatures, and a periodic GHASH-based checksum to validate the bitstream during decryption. Hence, UltraScale(+) devices were considered not affected by Starbleed-like attacks [2], [1].We identified novel security weaknesses in Xilinx UltraScale(+) FPGAs if configured outside recommended settings. In particular, we present four attacks in this situation: two attacks on the AES encryption and novel GHASH-based checksum and two authentication downgrade attacks. As a major contribution, we show that the Starbleed attack is still possible within the UltraScale(+) series by developing an attack against the GHASH-based checksum. After describing and analyzing the attacks, we list the subtle configuration changes which can lead to security vulnerabilities and secure configurations not affected by our attacks. As Xilinx only recommends configurations not affected by our attacks, users should be largely secure. However, it is not unlikely that users employ settings outside the recommendations, given the rather large number of configuration options and the fact that Security Misconfiguration is among the leading top 10 OWASP security issues. We note that these security weaknesses shown in this paper had been unknown before.

With memory safety and security issues continuing to plague modern systems, security is rapidly becoming a first class priority in new architectures and competes directly with performance and power efficiency. The capability-based architecture model provides a promising solution to many memory vulnerabilities by replacing plain addresses with capabilities, i.e., addresses and related metadata. A key advantage of the capability model is compatibility with existing code bases. Capabilities can be implemented transparently to a programmer, i.e., without source code changes. Capabilities leverage semantics in source code to describe access permissions but require customized compilers to translate the semantics to their binary equivalent.In this work, we introduce a complete capabilityaware compiler toolchain for such secure architectures. We illustrate the compiler construction with a RISC-V capability-based architecture, called Zeno. As a securityfocused, large-scale, global shared memory architecture, Zeno implements a Namespace-based capability model for accesses. Namespace IDs (NSID) are encoded with an extended addressing model to associate them with access permission metadata elsewhere in the system. The NSID extended addressing model requires custom compiler support to fully leverage the protections offered by Namespaces. The Zeno compiler produces code transparently to the programmer that is aware of Namespaces and maintains their integrity. The Zeno assembler enables custom Zeno instructions which support secure memory operations. Our results show that our custom toolchain moderately increases the binary size compared to nonZeno compilation. We find the minimal overhead incurred by the additional NSID management instructions to be an acceptable trade-off for the memory safety and security offered by Zeno Namespaces.

Due to the rapid development of cyber-physical systems, there are more and more security problems. The purpose of this work is to develop the concept of a knowledge base in the field of security of cyber-physical systems based on an ontological approach. To create the concept of a knowledge base, it was necessary to consider the system of a cyber-physical system and highlight its structural parts. As a result, the main concepts of the security of a cyber-physical system were identified and the concept of a knowledge base was drawn up, which in the future will help to analyze potential threats to cyber-physical systems.

The proliferation of autonomous and connected vehicles on our roads is increasingly felt. However, the problems related to the optimization of the energy consumed, to the safety, and to the security of these do not cease to arise on the tables of debates bringing together the various stakeholders. By focusing on the security aspect of such systems, we can realize that there is a family of problems that must be investigated as soon as possible. In particular, those that may manifest as the system expands. Therefore, this work aims to model and simulate the behavior of a system of autonomous and connected vehicles in the face of a malware invasion. In order to achieve the set objective, we propose a model to our system which is inspired by those used in epidimology, such as SI, SIR, SIER, etc. This being adapted to our case study, stochastic processes are defined in order to characterize its dynamics. After having fixed the values of the various parameters, as well as those of the initial conditions, we run 100 simulations of our system. After which we visualize the results got, we analyze them, and we give some interpretations. We end by outlining the lessons and recommendations drawn from the results.

Embedded Systems - the hidden computers in our lives - are deployed in the billionths and are already in the focus of attackers. They pose security risks when not tested and maintained thoroughly. In recent years, fuzzing has become a promising technique for automated security testing of programs, which can generate tons of test inputs for a program. Fuzzing is hardly applied to embedded systems, because of their high diversity and closed character. During my research I want tackle that gap in fuzzing embedded systems - short: “Embedded Fuzzing”. My goal is to obtain insights of the embedded system during execution, by using common debugging interfaces and hardware breakpoints to enable guided fuzzing in a generic and widely applicable way. Debugging interfaces and hardware breakpoints are available for most common microcontrollers, generating a potential industry impact. Preliminary results show that the approach covers basic blocks faster than blackbox fuzzing. Additionally, it is source code agnostic and leaves the embedded firmware unaltered.

Cyberattacks are one of the most pressing issues of our time. The impact of cyberthreats can damage various sectors such as business, health care, and governments, so one of the best solutions to deal with these cyberattacks and reduce cybersecurity threats is using Deep Learning. In this paper, we have created an in-depth study model to detect SQL Injection Attacks and Cross-Site Script attacks. We focused on XSS on the Stored-XSS attack type because SQL and Stored-XSS have similar site management methods. The advantage of combining deep learning with cybersecurity in our system is to detect and prevent short-term attacks without human interaction, so our system can reduce and prevent web attacks. This post-training model achieved a more accurate result more than 99% after maintaining the learning level, and 99% of our test data is determined by this model if this input is normal or dangerous.

Federated learning (FL) has emerged as a promising paradigm for distributed training of machine learning models. In FL, several participants train a global model collaboratively by only sharing model parameter updates while keeping their training data local. However, FL was recently shown to be vulnerable to data poisoning attacks, in which malicious participants send parameter updates derived from poisoned training data. In this paper, we focus on defending against targeted data poisoning attacks, where the attacker’s goal is to make the model misbehave for a small subset of classes while the rest of the model is relatively unaffected. To defend against such attacks, we first propose a method called MAPPS for separating malicious updates from benign ones. Using MAPPS, we propose three methods for attack detection: MAPPS + X-Means, MAPPS + VAT, and their Ensemble. Then, we propose an attack mitigation approach in which a "clean" model (i.e., a model that is not negatively impacted by an attack) can be trained despite the existence of a poisoning attempt. We empirically evaluate all of our methods using popular image classification datasets. Results show that we can achieve \textgreater 95% true positive rates while incurring only \textless 2% false positive rate. Furthermore, the clean models that are trained using our proposed methods have accuracy comparable to models trained in an attack-free scenario.

The demand for increasing flexibility use in power systems is stressed by the changing grid utilization. Making use of largely untapped flexibility potential is possible through novel flexibility markets. Different approaches for these markets are being developed and vary considering their handling of transaction schemes and relation of participating entities. This paper delivers the conceptual development of a holistic system architecture for the realization of an interregional flexibility market, which targets a market based congestion management in the transmission and distribution system through trading between system operators and flexibility providers. The framework combines a market mechanism with the required supplements like appropriate control algorithms for emergency situations, cyber-physical system monitoring and cyber-security assessment. The resulting methods are being implemented and verified in a remote-power-hardware-in-the-loop setup coupling a real world low voltage grid with a geographically distant real time simulation using state of the art control system applications with an integration of the aforementioned architecture components.

The technology advance and convergence of cyber physical systems, smart sensors, short-range wireless communications, cloud computing, and smartphone apps have driven the proliferation of Internet of things (IoT) devices in smart homes and smart industry. In light of the high heterogeneity of IoT system, the prevalence of system vulnerabilities in IoT devices and applications, and the broad attack surface across the entire IoT protocol stack, a fundamental and urgent research problem of IoT security is how to effectively collect, analyze, extract, model, and visualize the massive network traffic of IoT devices for understanding what is happening to IoT devices. Towards this end, this paper develops and demonstrates an end-to-end system with three key components, i.e., the IoT network traffic monitoring system via programmable home routers, the backend IoT traffic behavior analysis system in the cloud, and the frontend IoT visualization system via smartphone apps, for monitoring, analyzing and virtualizing network traffic behavior of heterogeneous IoT devices in smart homes. The main contributions of this demonstration paper is to present a novel system with an end-to-end process of collecting, analyzing and visualizing IoT network traffic in smart homes.

Mesh networks based on the wireless local area network (WLAN) technology, as specified by the standards amendment IEEE 802.11s, provide for a flexible and low-cost interconnection of devices and embedded systems for various use cases. To assess the real-world performance of WLAN mesh networks and potential optimization strategies, suitable testbeds and measurement tools are required. Designed for highly automated transport-layer throughput and latency measurements, the software FLExible Network Tester (Flent) is a promising candidate. However, so far Flent does not integrate information specific to IEEE 802.11s networks, such as peer link status data or mesh routing metrics. Consequently, we propose Flent extensions that allow to additionally capture IEEE 802.11s information as part of the automated performance tests. For the functional validation of our extensions, we conduct Flent measurements in a mesh mobility scenario using the network emulation framework Mininet-WiFi.

Modular exponentiation (ME) is a complex operation for several public-key cryptosystems (PKCs). Moreover, ME is expensive for resource-constrained devices in terms of computation time and energy consumption, especially when the exponent is large. ME is defined as the task of raising an integer x to power k and reducing the result modulo some integer n. Several methods to calculate ME have been proposed. In this paper, we present the efficient ME methods. We then implement the methods using different security levels of RSA keys on a Raspberry Pi. Finally, we give the fast ME method.