Biblio

In view of the demand for the continuous guarantee capability of the information system in the diversified task and the complex cyber threat environment, a dual loop architecture of the resilient cloud environment for mission assurance is proposed. Firstly, general technical architecture of cloud environment is briefly introduced. Drawing on the idea of software definition, a resilient dual loop architecture based on "perception analysis planning adjustment" is constructed. Then, the core mission assurance system deployment mechanism is designed using the idea of distributed control. Finally, the core mission assurance system is designed in detail, which is consisted of six functional modules, including mission and environment awareness network, intelligent anomaly analysis and prediction, mission and resource situation generation, mission and resource planning, adaptive optimization and adjustment. The design of the dual loop architecture of the resilient cloud environment for mission assurance will further enhance the fast adaptability of the information system in the complex cyber physical environment.

Network-on-Chip (NOC) is the heart of data communication between processing cores in Multiprocessor-based Systems on Chip (MPSoC). Packets transferred via the NoC are exposed to snooping, which makes NoC-based systems vulnerable to security attacks. Additionally, Hardware Trojans (HTs) can be deployed in some of the NoC nodes to apply security threats of extracting sensitive information or degrading the system performance. In this paper, an overview of some security attacks in NoC-based systems and the countermeasure techniques giving prominence on malicious nodes are discussed. Work in progress for secure routing algorithms is also presented.

In cloud computing customers upload data and computation to cloud providers. As they upload their data to the cloud provider, they typically give up data confidentiality. We develop SecureMR, a system that analyzes and transforms MapReduce programs to operate over encrypted data. SecureMR makes use of partially homomorphic encryption and a trusted client. We evaluate SecureMR on a set of complex computation-intensive MapReduce benchmarks.

Smart grid systems data has been exposed to several threats and attacks from different perspectives and have resulted in several system failures. Obtaining security of data and key exposure and enhancing system ability in data collection and transmission process are challenging, on the grounds smart grid data is sensitive and enormous sum. In this paper we introduce smart grid data security method along with advanced Cipher text policy attribute based encryption (CP-ABE). Cloud supported IoT is widely used in smart grid systems. Smart IoT devices collect data and perform status management. Data obtained from the IOT devices will be divided into blocks and encrypted data will be stored in different cloud server with different encrypted keys even when one cloud server is assaulted and encrypted key is exposed data cannot be decrypted, thereby the transmission and encryption process are done in correspondingly. We protect access-tree structure information even after the data is shared to user by solving revocation problem in which cloud will inform data owner to revoke and update encryption key after user has downloaded the data, which preserves the data privacy from unauthorized users. The analysis of the system concludes that our proposed system can meet the security requirements in smart grid systems along with cloud-Internet of things.

In modern cyber-physical systems and wireless sensor networks the complexity of crisis management processes is caused by a variety of software/hardware assets and communication protocols, the necessity of their collaborative function, possible inconsistency of data flows between particular devices and increased requirements to cyber-physical security. A crisis management oriented model of a communicational mobile network is constructed. A general architecture of network nodes by the use of XBee circuits, Arduino microcontrollers and connecting equipment are developed. An analysis of possible cyber-physical security events on the base of existing intruder models is performed. A series of experiments on modeling attacks on network nodes is conducted. Possible ways for attack revelations by means of components for security event collection and data correlation is discussed.

Each year, large ground motions from earthquakes cause infrastructure damage and loss of life worldwide. Here we present a novel concept that redirects and attenuates hazardous seismic waves using an engineered seismic-muffler acting as a cloaking device. The device employs vertically-oriented, sloping-opposing boreholes or trenches to form muffler walls and is designed to: 1) reflect and divert large amplitude surface waves as a barrier, while 2) dissipating body and converted waves traveling from depth upward into the muffler duct. Seismic wave propagation models suggest that a seismic-muffler can effectively reduce broadband ground motion directly above the muffler. 3D simulations are also compared for validation with experimental data obtained from bench-scale blocks containing machined borehole arrays and trenches. Computer models are then scaled to an earth-sized model. Results suggest a devastating seismic energy magnitude 7.0-\$\textbackslashtextbackslashmathrm M\_\textbackslashtextbackslashmathrm E\$ earthquake can be reduced to less damaging magnitudes experienced in the muffler vicinity, 4.5- \$\textbackslashtextbackslashmathrm M\_\textbackslashtextbackslashmathrm E\$ (surface wave) and 5.7- \$\textbackslashtextbackslashmathrm M\_\textbackslashtextbackslashmathrm E\$ (upgoing coupling into the muffler). Our findings imply that seismic-muffler structures significantly reduce the impact of the peak ground velocity of dangerous surface waves, while, seismic transmission upward through the muffler base at depth has marginal effects.

Blockchain normalizes applications that run on the internet through the standardization of decentralized data structure, computational requirements and trust in transactions. This new standard has now spawned hundreds of legitimate internet applications in addition to the cryptocurrency revolution. This next frontier that standardizes internet applications will dramatically increase productivity to levels never seen before, especially when applied to Internet of Things (IoT) applications. The blockchain framework relies on cryptographic private keys to sign digital data as its foundational principle. Without the security of private keys to sign data blocks, there can be no trust in blockchain. Central storage of these keys for managing IoT machines and users, while convenient to implement, will be highly detrimental to the assumed safety and security of this next frontier. In this paper, we will introduce decentralized and device agnostic cryptographic signing solutions suitable for securing users and machines in blockchain and IoT applications.

The Internet of Things (IoT) consists of embedded devices that sense and manage our environment in a growing range of applications. Large-scale IoT systems such as smart cities require significant investment in both equipment and personnel. To maximize return on investment, IoT platforms should support multiple third-party applications and adaptation of infrastructure over time. Realizing the vision of shared IoT platforms demands strong security guarantees. That is particularly challenging considering the limited capability and resource constraints of many IoT devices. In this paper, we present SPEED, an approach to secure erasure with verifiability in IoT. Secure erasure is a fundamental property when it comes to share an IoT platform with other users which guarantees the cleanness of a device's memory at the beginning of the application deployment as well as at the time of releasing the underlying IoT device. SPEED relies on two security primitives: memory isolation and distance bounding protocol. We evaluate the performance of SPEED by implementing it on a simple bare-metal IoT device belongs to Class-1. Our evaluation results show a limited overhead in terms of memory footprint, time, and energy consumption.

There is increased interest in smart vehicles acting as both data consumers and producers in smart cities. Vehicles can use smart city data for decision-making, such as dynamic routing based on traffic conditions. Moreover, the multitude of embedded sensors in vehicles can collectively produce a rich data set of the urban landscape that can be used to provide a range of services. Key to the success of this vision is a scalable and private architecture for trusted data sharing. This paper proposes a framework called SpeedyChain, that leverages blockchain technology to allow smart vehicles to share their data while maintaining privacy, integrity, resilience, and non-repudiation in a decentralized and tamper-resistant manner. Differently from traditional blockchain usage (e.g., Bitcoin and Ethereum), the proposed framework uses a blockchain design that decouples the data stored in the transactions from the block header, thus allowing fast addition of data to the blocks. Furthermore, an expiration time for each block is proposed to avoid large sized blocks. This paper also presents an evaluation of the proposed framework in a network emulator to demonstrate its benefits.

Selfish mining is a well-known mining attack strategy discovered by Eyal and Sirer in 2014. After that, the attackers' strategy has been further discussed by many other works, which analyze the strategy and behavior of a single attacker. The extension of the strategy research is greatly restricted by the assumption that there is only one attacker in the blockchain network, since, in many cases, a proof of work blockchain has multiple attackers. The attackers can be independent of others instead of sharing information and attacking the blockchain as a whole. In this paper, we will establish a new model to analyze the miners' behavior in a proof of work blockchain with multiple attackers. Based on our model, we extend the attackers' strategy by proposing a new strategy set publish-n. Meanwhile, we will also review other attacking strategies such as selfish mining and stubborn mining in our model to explore whether these strategies work or not when there are multiple attackers. The performances of different strategies are compared using relative stale block rate of the attackers. In a proof of work blockchain model with two attackers, strategy publish-n can beat selfish mining by up to 26.3%.

Nowadays public-key cryptography is based on number theory problems, such as computing the discrete logarithm on an elliptic curve or factoring big integers. Even though these problems are considered difficult to solve with the help of a classical computer, they can be solved in polynomial time on a quantum computer. Which is why the research community proposed alternative solutions that are quantum-resistant. The process of finding adequate post-quantum cryptographic schemes has moved to the next level, right after NIST's announcement for post-quantum standardization. One of the oldest quantum-resistant proposition goes back to McEliece in 1978, who proposed a public-key cryptosystem based on coding theory. It benefits of really efficient algorithms as well as a strong mathematical background. Nonetheless, its security has been challenged many times and several variants were cryptanalyzed. However, some versions remain unbroken. In this paper, we propose to give some background on coding theory in order to present some of the main flawless in the protocols. We analyze the existing side-channel attacks and give some recommendations on how to securely implement the most suitable variants. We also detail some structural attacks and potential drawbacks for new variants.

This paper proposed an advanced round modification fault analysis (RMFA) at the theoretical level on AEGIS-128, which is one of seven finalists in CAESAR competition. First, we clarify our assumptions and simplifications on the attack model, focusing on the encryption security. Then, we emphasize the difficulty of applying vanilla RMFA to AEGIS-128 in the practical case. Finally we demonstrate our advanced fault analysis on AEGIS-128 using machine-solver based algebraic techniques. Our enhancement can be used to conquer the practical scenario which is difficult for vanilla RMFA. Simulation results show that when the fault is injected to the initialization phase and the number of rounds is reduced to one, two samples of injections can extract the whole 128 key bits within less than two hours. This work can also be extended to other versions such as AEGIS-256.

Technologies such as Industry 4.0 or assisted/autonomous driving are relying on highly customized cyber-physical realtime systems. Those systems are designed to match functional safety regulations and requirements such as EN ISO 13849, EN IEC 62061 or ISO 26262. However, as systems - especially vehicles - are becoming more connected and autonomous, they become more likely to suffer from new attack vectors. New features may meet the corresponding safety requirements but they do not consider adversaries intruding through security holes with the purpose of bringing vehicles into unsafe states. As research goal, we want to bridge the gap between security and safety in cyber-physical real-time systems by investigating real-time-aware intrusion-tolerant architectures for automotive use-cases.

Web security is a big concern in the current Internet; users may visit websites that automatically download malicious codes for leaking user's privacy information, or even mildly their web browser may help for someone's cryptomining. In this paper, we analyze abusive web resources (i.e. malicious resources and cryptomining) crawled from the Alexa Top 150,000 sites. We highlight the abusive web resources on Alexa ranking, TLD usage, website geolocation, and domain lifetime. Our results show that abusive resources are spread in the Alexa ranking, websites particularly generic Top Level Domain (TLD) and their recently registered domains. In addition, websites with malicious resources are mainly located in China while cryptomining is located in USA. We further evaluate possible counter-measures against abusive web resources. We observe that ad or privacy block lists are ineffective to block against malicious resources while coin-blocking lists are powerful enough to mitigate in-browser cryptomining. Our observations shed light on a little studied, yet important, aspect of abusive resources, and can help increase user awareness about the malicious resources and drive-by mining on web browsers.

High-level C++ source code abstractions such as classes and methods greatly assist human analysts and automated algorithms alike when analyzing C++ programs. Unfortunately, these abstractions are lost when compiling C++ source code, which impedes the understanding of C++ executables. In this paper, we propose a system, OOAnalyzer, that uses an innovative new design to statically recover detailed C++ abstractions from executables in a scalable manner. OOAnalyzer's design is motivated by the observation that many human analysts reason about C++ programs by recognizing simple patterns in binary code and then combining these findings using logical inference, domain knowledge, and intuition. We codify this approach by combining a lightweight symbolic analysis with a flexible Prolog-based reasoning system. Unlike most existing work, OOAnalyzer is able to recover both polymorphic and non-polymorphic C++ classes. We show in our evaluation that OOAnalyzer assigns over 78% of methods to the correct class on our test corpus, which includes both malware and real-world software such as Firefox and MySQL. These recovered abstractions can help analysts understand the behavior of C++ malware and cleanware, and can also improve the precision of program analyses on C++ executables.

Ciphertext-policy attribute-based encryption (CP-ABE) has been proposed to enable fine-grained access control on encrypted data for cloud storage service. In the context of CP-ABE, since the decryption privilege is shared by multiple users who have the same attributes, it is difficult to identify the original key owner when given an exposed key. This leaves the malicious cloud users a chance to leak their access credentials to outsourced data in clouds for profits without the risk of being caught, which severely damages data security. To address this problem, we add the property of traceability to the conventional CP-ABE. To catch people leaking their access credentials to outsourced data in clouds for profits effectively, in this paper, we first propose two kinds of non-interactive commitments for traitor tracing. Then we present a fully secure traceable CP-ABE system for cloud storage service from the proposed commitment. Our proposed commitments for traitor tracing may be of independent interest, as they are both pairing-friendly and homomorphic. We also provide extensive experimental results to confirm the feasibility and efficiency of the proposed solution.

The evolution of the enterprise computing landscape towards emerging trends such as fog/edge computing and the Industrial Internet of Things (IIoT) are leading to a change of approach to securing computer networks to deal with challenges such as mobility, virtualized infrastructures, dynamic and heterogeneous user contexts and transaction-based interactions. The uncertainty introduced by such dynamicity introduces greater uncertainty into the access control process and motivates the need for risk-based access control decision making. Thus, the traditional perimeter-based security paradigm is increasingly being abandoned in favour of a so called "zero trust networking" (ZTN). In ZTN networks are partitioned into zones with different levels of trust required to access the zone resources depending on the assets protected by the zone. All accesses to sensitive information is subject to rigorous access control based on user and device profile and context. In this paper we outline a policy enforcement framework to address many of open challenges for risk-based access control for ZTN. We specify the design of required policy languages including a generic firewall policy language to express firewall rules. We design a mechanism to map these rules to specific firewall syntax and to install the rules on the firewall. We show the viability of our design with a small proof-of-concept.

Modern malware applies a rich arsenal of evasion techniques to render dynamic analysis ineffective. In turn, dynamic analysis tools take great pains to hide themselves from malware; typically this entails trying to be as faithful as possible to the behavior of a real run. We present a novel approach to malware analysis that turns this idea on its head, using an extreme abstraction of the operating system that intentionally strays from real behavior. The key insight is that the presence of malicious behavior is sufficient evidence of malicious intent, even if the path taken is not one that could occur during a real run of the sample. By exploring multiple paths in a system that only approximates the behavior of a real system, we can discover behavior that would often be hard to elicit otherwise. We aggregate features from multiple paths and use a funnel-like configuration of machine learning classifiers to achieve high accuracy without incurring too much of a performance penalty. We describe our system, TAMALES (The Abstract Malware Analysis LEarning System), in detail and present machine learning results using a 330K sample set showing an FPR (False Positive Rate) of 0.10% with a TPR (True Positive Rate) of 99.11%, demonstrating that extreme abstraction can be extraordinarily effective in providing data that allows a classifier to accurately detect malware.

Industrial control systems (ICS) are key enabling systems that drive the productivity and efficiency of omnipresent industries such as power, gas, water treatment, transportation, and manufacturing. These systems consist of interconnected components that communicate over industrial networks using industrial protocols such as the Common Industrial Protocol (CIP). CIP is one of the most commonly used network-based process control protocols, and utilizes an object-oriented communication structure for device to device interaction. Due to this object-oriented structure, CIP communication reveals detailed information about the devices, the communication patterns, and the system, providing an in-depth view of the system. The details from this in-depth system perspective can be utilized as part of a system cybersecurity or discovery approach. However, due to the variety of commands, corresponding parameters, and variable layer structure of the CIP network layer, processing this layer is a challenging task. This paper presents a tool, Advanced CIP Evaluator (ACE), which passively processes the CIP communication layer and automatically extracts device, communication, and system information from observed network traffic. ACE was tested and verified using a representative ICS power generation testbed. Since ACE operates passively, without generating any network traffic of its own, system operations are not disturbed. This novel tool provides ICS information, such as networked devices, communication patterns, and system operation, at a depth and breadth that is unique compared with other known tools.

Attackers create new threats and constantly change their behavior to mislead security systems. In this paper, we propose an adaptive threat detection architecture that trains its detection models in real time. The major contributions of the proposed architecture are: i) gather data about zero-day attacks and attacker behavior using honeypots in the network; ii) process data in real time and achieve high processing throughput through detection schemes implemented with stream processing technology; iii) use of two real datasets to evaluate our detection schemes, the first from a major network operator in Brazil and the other created in our lab; iv) design and development of adaptive detection schemes including both online trained supervised classification schemes that update their parameters in real time and learn zero-day threats from the honeypots, and online trained unsupervised anomaly detection schemes that model legitimate user behavior and adapt to changes. The performance evaluation results show that proposed architecture maintains an excellent trade-off between threat detection and false positive rates and achieves high classification accuracy of more than 90%, even with legitimate behavior changes and zero-day threats.

The Agave Platform first appeared in 2011 as a pilot project for the iPlant Collaborative [11]. In its first two years, Foundation saw over 40% growth per month, supporting 1000+ clients, 600+ applications, 4 HPC systems at 3 centers across the US. It also gained users outside of plant biology. To better serve the needs of the general open science community, we rewrote Foundation as a scalable, cloud native application and named it the Agave Platform. In this paper we present the Agave Platform, a Science-as-a-Service (ScaaS) platform for reproducible science. We provide a brief history and technical overview of the project, and highlight three case studies leveraging the platform to create synergistic value for their users.

Network Management is a critical process for an enterprise to configure and monitor the network devices using cost effective methods. It is imperative for it to be robust and free from adversarial or accidental security flaws. With the advent of cloud computing and increasing demands for centralized network control, conventional management protocols like SNMP appear inadequate and newer techniques like NMDA and NETCONF have been invented. However, unlike SNMP which underwent improvements concentrating on security, the new data management and storage techniques have not been scrutinized for the inherent security flaws. In this paper, we identify several vulnerabilities in the widely used critical infrastructures which leverage the Network Management Datastore Architecture design (NMDA). Software Defined Networking (SDN), a proponent of NMDA, heavily relies on its datastores to program and manage the network. We base our research on the security challenges put forth by the existing datastore's design as implemented by the SDN controllers. The vulnerabilities identified in this work have a direct impact on the controllers like OpenDayLight, Open Network Operating System and their proprietary implementations (by CISCO, Ericsson, RedHat, Brocade, Juniper, etc). Using our threat detection methodology, we demonstrate how the NMDA-based implementations are vulnerable to attacks which compromise availability, integrity, and confidentiality of the network. We finally propose defense measures to address the security threats in the existing design and discuss the challenges faced while employing these countermeasures.

The term "Cyber Physical System" (CPS) has been used in the recent years to describe a system type, which makes use of powerful communication networks to functionally combine systems that were previously thought of as independent. The common theme of CPSs is that through communication, CPSs can make decisions together and achieve common goals. Yet, in contrast to traditional system types such as embedded systems, the functional dependence between CPSs can change dynamically at runtime. Hence, their functional dependence may cause unforeseen runtime behavior, e.g., when a CPS becomes unavailable, but others depend on its correct operation. During development of any individual CPS, this runtime behavior must hence be predicted, and the system must be developed with the appropriate level of robustness. Since at present, research is mainly concerned with the impact of functional dependence in CPS on development, the impact on runtime behavior is mere conjecture. In this paper, we present AirborneCPS, a simulation tool for functionally dependent CPSs which simulates runtime behavior and aids in the identification of undesired functional interaction.

Exfiltrating sensitive information from smartphones has become one of the most significant security threats. We have built a system to identify HTTP-based information exfiltration of malicious Android applications. In this paper, we discuss the method to track the propagation of sensitive information in Android applications using static taint analysis. We have studied the leaked information, destinations to which information is exfiltrated, and their correlations with types of sensitive information. The analysis results based on 578 malicious Android applications have revealed that a significant portion of these applications are interested in identity-related sensitive information. The vast majority of malicious applications leak multiple types of sensitive information. We have also identified servers associated with three country codes including CN, US, and SG are most active in collecting sensitive information. The analysis results have also demonstrated that a wide range of non-default ports are used by suspicious URLs.

This work investigates the fundamental constraints of anonymous communication (AC) protocols. We analyze the relationship between bandwidth overhead, latency overhead, and sender anonymity or recipient anonymity against the global passive (network-level) adversary. We confirm the trilemma that an AC protocol can only achieve two out of the following three properties: strong anonymity (i.e., anonymity up to a negligible chance), low bandwidth overhead, and low latency overhead. We further study anonymity against a stronger global passive adversary that can additionally passively compromise some of the AC protocol nodes. For a given number of compromised nodes, we derive necessary constraints between bandwidth and latency overhead whose violation make it impossible for an AC protocol to achieve strong anonymity. We analyze prominent AC protocols from the literature and depict to which extent those satisfy our necessary constraints. Our fundamental necessary constraints offer a guideline not only for improving existing AC systems but also for designing novel AC protocols with non-traditional bandwidth and latency overhead choices.