Biblio

In this paper, we describe the formatting guidelines for ACM SIG Proceedings. In order to assure safety of Chinese Train Control System (CTCS), it is necessary to ensure the operational risk is acceptable throughout its life-cycle, which requires a pragmatic risk assessment required for effective risk control. Many risk assessment techniques currently used in railway domain are qualitative, and rely on the experience of experts, which unavoidably brings in subjective judgements. This paper presents a method that combines fuzzy reasoning and analytic hierarchy process approach to quantify the experiences of experts to get the scores of risk parameters. Fuzzy reasoning is used to obtain the risk of system hazard, analytic hierarchy process approach is used to determine the risk level (RL) and its membership of the system. This method helps safety analyst to calculate overall collective risk level of system. A case study of risk assessment of CTCS system is used to demonstrate this method can give quantitative result of collective risks without much information from experts, but can support the risk assessment with risk level and its membership, which are more valuable to guide the further risk management.

Resiliency is a relatively new topic in the context of access control. Informally, it refers to the extent to which a multi-user computer system, subject to an authorization policy, is able to continue functioning if a number of authorized users are unavailable. Several interesting problems connected to resiliency were introduced by Li, Wang and Tripunitara [13], many of which were found to be intractable. In this paper, we show that these resiliency problems have unexpected connections with the workflow satisfiability problem (WSP). In particular, we show that an instance of the resiliency checking problem (RCP) may be reduced to an instance of WSP. We then demonstrate that recent advances in our understanding of WSP enable us to develop fixed-parameter tractable algorithms for RCP. Moreover, these algorithms are likely to be useful in practice, given recent experimental work demonstrating the advantages of bespoke algorithms to solve WSP. We also generalize RCP in several different ways, showing in each case how to adapt the reduction to WSP. Li et al also showed that the coexistence of resiliency policies and static separation-of-duty policies gives rise to further interesting questions. We show how our reduction of RCP to WSP may be extended to solve these problems as well and establish that they are also fixed-parameter tractable.

Many emerging applications, from domains such as healthcare and oil & gas, require several data processing systems for complex analytics. This demo paper showcases system, a framework that provides multi-platform task execution for such applications. It features a three-layer data processing abstraction and a new query optimization approach for multi-platform settings. We will demonstrate the strengths of system by using real-world scenarios from three different applications, namely, machine learning, data cleaning, and data fusion.

Large scale sensor networks are ubiquitous nowadays. An important objective of deploying sensors is to detect anomalies in the monitored system or infrastructure, which allows remedial measures to be taken to prevent failures, inefficiencies, and security breaches. Most existing sensor anomaly detection methods are local, i.e., they do not capture the global dependency structure of the sensors, nor do they perform well in the presence of missing or erroneous data. In this paper, we propose an anomaly detection technique for large scale sensor data that leverages relationships between sensors to improve robustness even when data is missing or erroneous. We develop a probabilistic graphical model-based global outlier detection technique that represents a sensor network as a pairwise Markov Random Field and uses graphical model inference to detect anomalies. We show our model is more robust than local models, and detects anomalies with 90% accuracy even when 50% of sensors are erroneous. We also build a synthetic graphical model generator that preserves statistical properties of a real data set to test our outlier detection technique at scale.

As web applications is becoming more prominent due to the ubiquity of web services, web applications have become main targets for attackers. In order to steal or leak sensitive user data managed by web applications, attackers exploit a wide range of input validation vulnerabilities such as SQL injection, path traversal (or directory traversal), cross-site scripting (XSS), etc. This paper propose a technique that can verify input values of Java-based web applications using static bytecode instrumentation and runtime input validation. The technique searches for target methods or object constructors in compiled Java class files, and statically inserts bytecode modules. At runtime, the instrumented bytecode modules validate input values of the targets, and take countermeasure against malicious inputs. The proposed technique can mitigate the input validation vulnerabilities in Java-based web applications without source codes. To evaluate the effectiveness of the proposed technique, experiments are carried out with an insecure web application maintained by OWASP WebGoat Project. The experimental results show that the proposed technique successfully mitigates input validation vulnerabilities such as SQL injection and path traversal.

The premise of the SafeConfig'16 Workshop is existing tools and methods for security assessments are necessary but insufficient for scientifically rigorous testing and evaluation of resilient and active cyber systems. The objective for this workshop is the exploration and discussion of scientifically sound testing regimen(s) that will continuously and dynamically probe, attack, and "test" the various resilient and active technologies. This adaptation and change in focus necessitates at the very least modification, and potentially, wholesale new developments to ensure that resilient- and agile-aware security testing is available to the research community. All testing, validation and experimentation must also be repeatable, reproducible, subject to scientific scrutiny, measurable and meaningful to both researchers and practitioners. The workshop will convene a panel of experts to explore this concept. The topic will be discussed from three different perspectives. One perspective is that of the practitioner. We will explore whether active and resilient technologies are or are planned for deployment and whether the verification methodology affects that decision. The second perspective will be that of the research community. We will address the shortcomings of current approaches and the research directions needed to address the practitioner's concerns. The third perspective is that of the policy community. Specifically, we will explore the dynamics between technology, verification, and policy.

Large numbers of smart connected devices, also named as the Internet of Things (IoT), are permeating our environments (homes, factories, cars, and also our body - with wearable devices) to collect data and act on the insight derived. Ensuring software integrity (including OS, apps, and configurations) on such smart devices is then essential to guarantee both privacy and safety. A key mechanism to protect the software integrity of these devices is remote attestation: A process that allows a remote verifier to validate the integrity of the software of a device. This process usually makes use of a signed hash value of the actual device's software, generated by dedicated hardware. While individual device attestation is a well-established technique, to date integrity verification of a very large number of devices remains an open problem, due to scalability issues. In this paper, we present SANA, the first secure and scalable protocol for efficient attestation of large sets of devices that works under realistic assumptions. SANA relies on a novel signature scheme to allow anyone to publicly verify a collective attestation in constant time and space, for virtually an unlimited number of devices. We substantially improve existing swarm attestation schemes by supporting a realistic trust model where: (1) only the targeted devices are required to implement attestation; (2) compromising any device does not harm others; and (3) all aggregators can be untrusted. We implemented SANA and demonstrated its efficiency on tiny sensor devices. Furthermore, we simulated SANA at large scale, to assess its scalability. Our results show that SANA can provide efficient attestation of networks of 1,000,000 devices, in only 2.5 seconds.

Recent literature on iOS security has focused on the malicious potential of third-party applications, demonstrating how developers can bypass application vetting and code-level protections. In addition to these protections, iOS uses a generic sandbox profile called "container" to confine malicious or exploited third-party applications. In this paper, we present the first systematic analysis of the iOS container sandbox profile. We propose the SandScout framework to extract, decompile, formally model, and analyze iOS sandbox profiles as logic-based programs. We use our Prolog-based queries to evaluate file-based security properties of the container sandbox profile for iOS 9.0.2 and discover seven classes of exploitable vulnerabilities. These attacks affect non-jailbroken devices running later versions of iOS. We are working with Apple to resolve these attacks, and we expect that SandScout will play a significant role in the development of sandbox profiles for future versions of iOS.

Conventional overwriting-based and encryption-based secure deletion schemes can only sanitize data. However, the past existence of the deleted data may leave artifacts in the layout at all layers of a computing system. These structural artifacts may be utilized by the adversary to infer sensitive information about the deleted data or even to fully recover them. The conventional secure deletion solutions unfortunately cannot sanitize them. In this work, we introduce truly secure deletion, a novel security notion that is much stronger than the conventional secure deletion. Truly secure deletion requires sanitizing both the obsolete data as well as the corresponding structural artifacts, so that the resulting storage layout after a delete operation is indistinguishable from that the deleted data never appeared. We propose TedFlash, a Truly secure deletion scheme for Flash-based block devices. TedFlash can successfully sanitize both the data and the structural artifacts, while satisfying the design constraints imposed for flash memory. Security analysis and experimental evaluation show that TedFlash can achieve the truly secure deletion guarantee with a small additional overhead compared to conventional secure deletion solutions.

Attack graphs are a powerful modeling technique with which to explore the attack surface of a system. However, they can be difficult to generate due to the exponential growth of the state space, often times making exhaustive search impractical. This paper discusses an approach for generating large attack graphs with an emphasis on scalable generation over a distributed system. First, a serial algorithm is presented, highlighting bottlenecks and opportunities to exploit inherent concurrency in the generation process. Then a strategy to parallelize this process is presented. Finally, we discuss plans for future work to implement the parallel algorithm using a hybrid distributed/shared memory programming model on a heterogeneous compute node cluster.

With the rapid growth of network bandwidth, increases in CPU cores on a single machine, and application API models demanding more short-lived connections, a scalable TCP stack is performance-critical. Although many clean-state designs have been proposed, production environments still call for a bottom-up parallel TCP stack design that is backward-compatible with existing applications. We present Fastsocket, a BSD Socket-compatible and scalable kernel socket design, which achieves table-level connection partition in TCP stack and guarantees connection locality for both passive and active connections. Fastsocket architecture is a ground up partition design, from NIC interrupts all the way up to applications, which naturally eliminates various lock contentions in the entire stack. Moreover, Fastsocket maintains the full functionality of the kernel TCP stack and BSD-socket-compatible API, and thus applications need no modifications. Our evaluations show that Fastsocket achieves a speedup of 20.4x on a 24-core machine under a workload of short-lived connections, outperforming the state-of-the-art Linux kernel TCP implementations. When scaling up to 24 CPU cores, Fastsocket increases the throughput of Nginx and HAProxy by 267% and 621% respectively compared with the base Linux kernel. We also demonstrate that Fastsocket can achieve scalability and preserve BSD socket API at the same time. Fastsocket is already deployed in the production environment of Sina WeiBo, serving 50 million daily active users and billions of requests per day.

While cloud computing has become an attractive platform for supporting data intensive applications, a major obstacle to the adoption of cloud computing in sectors such as health and defense is the privacy risk associated with releasing datasets to third-parties in the cloud for analysis. A widely-adopted technique for data privacy preservation is to anonymize data via local recoding. However, most existing local-recoding techniques are either serial or distributed without directly optimizing scalability, thus rendering them unsuitable for big data applications. In this paper, we propose a highly scalable approach to local-recoding anonymization in cloud computing, based on Locality Sensitive Hashing (LSH). Specifically, a novel semantic distance metric is presented for use with LSH to measure the similarity between two data records. Then, LSH with the MinHash function family can be employed to divide datasets into multiple partitions for use with MapReduce to parallelize computation while preserving similarity. By using our efficient LSH-based scheme, we can anonymize each partition through the use of a recursive agglomerative \$k\$-member clustering algorithm. Extensive experiments on real-life datasets show that our approach significantly improves the scalability and time-efficiency of local-recoding anonymization by orders of magnitude over existing approaches.

We propose the first verifier-local revocation scheme for privacy-enhancing attribute-based credentials (PABCs) that is practically usable in large-scale applications, such as national eID cards, public transportation and physical access control systems. By using our revocation scheme together with existing PABCs, it is possible to prove attribute ownership in constant time and verify the proof and the revocation status in the time logarithmic in the number of revoked users, independently of the number of all valid users in the system. Proofs can be efficiently generated using only offline constrained devices, such as existing smart-cards. These features are achieved by using a new construction called \$n\$-times unlinkable proofs. We show the full cryptographic description of the scheme, prove its security, discuss parameters influencing scalability and provide details on implementation aspects. As a side result of independent interest, we design a more efficient proof of knowledge of weak Boneh-Boyen signatures, that does not require any pairing computation on the prover side.

Software-Defined Networking (SDN) has emerged as a framework for centralized command and control in cloud data centric environments. SDN separates data and control plane, which provides network administrator better visibility and policy enforcement capability compared to traditional networks. The SDN controller can assess reachability information of all the hosts in a network. There are many critical assets in a network which can be compromised by a malicious attacker through a multistage attack. Thus we make use of centralized controller to assess the security state of the entire network and pro-actively perform attack analysis and countermeasure selection. This approach is also known as Moving Target Defense (MTD). We use the SDN controller to assess the attack scenarios through scalable Attack Graphs (AG) and select necessary countermeasures to perform network reconfiguration to counter network attacks. Moreover, our framework has a comprehensive conflict detection and resolution module that ensures that no two flow rules in a distributed SDN-based cloud environment have conflicts at any layer; thereby assuring consistent conflict-free policy implementation and preventing information leakage.

Software Defined Networking (SDN) is the new promise towards an easily configured and remotely controlled network. Based on Centralized control, SDN technology has proved its positive impact on the world of network communications from different aspects. Security in SDN, as in traditional networks, is an essential feature that every communication system should possess. In this paper, we propose an SDN security design approach, which strikes a good balance between network performance and security features. We show how such an approach can be used to prevent DDoS attacks targeting either the controller or the different hosts in the network, and how to trace back the source of the attack. The solution lies in introducing a third plane, the security plane, in addition to the data plane, which is responsible for forwarding data packets between SDN switches, and parallel to the control plane, which is responsible for rule and data exchange between the switches and the SDN controller. The security plane is designed to exchange security-related data between a third party agent on the switch and a third party software module alongside the controller. Our evaluation shows the capability of the proposed system to enforce different levels of real-time user-defined security with low overhead and minimal configuration.

A group of wireless nodes forming a dynamic wireless network without any infrastructure is a MANET. As network is becoming an important technology for commercial and military based distributed applications, implementation of security over MANET has proved to be mandatory, as such networks are more vulnerable to attacks. When dealing with data transfer between the nodes in MANET, confidentiality and message integrity are the two important factors that need to be focused carefully. This paper proposes the implementation of a security algorithm over data transfer in Optimized Link State Routing protocol providing Trust Management in MANET by implementing confidentiality through Digital Signatures and Message Integrity through 256-bit strong AES cryptographic techniques using Openssl.

Text-based Captchas have been widely used to deter misuse of services on the Internet. However, many designs have been broken. It is intellectually interesting and practically relevant to look for alternative designs, which are currently a topic of active research. We motivate the study of Chinese Captchas as an interesting alternative design - co-unterintuitively, it is possible to design Chinese Captchas that are universally usable, even to those who have never studied Chinese language. More importantly, we ask a fundamental question: is the segmentation-resistance principle established for Roman-character based Captchas applicable to Chinese based designs? With deep learning techniques, we offer the first evidence that computers do recognize individual Chinese characters well, regardless of distortion levels. This suggests that many real-world Chinese schemes are insecure, in contrast to common beliefs. Our result offers an essential guideline to the design of secure Chinese Captchas, and it is also applicable to Captchas using other large-alphabet languages such as Japanese.

A digital microfluidic biochip (DMFB) is an emerging technology that enables miniaturized analysis systems for point-of-care clinical diagnostics, DNA sequencing, and environmental monitoring. A DMFB reduces the rate of sample and reagent consumption, and automates the analysis of assays. In this paper, we provide the first assessment of the security vulnerabilities of DMFBs. We identify result-manipulation attacks on a DMFB that maliciously alter the assay outcomes. Two practical result-manipulation attacks are shown on a DMFB platform performing enzymatic glucose assay on serum. In the first attack, the attacker adjusts the concentration of the glucose sample and thereby modifies the final result. In the second attack, the attacker tampers with the calibration curve of the assay operation. We then identify denial-of-service attacks, where the attacker can disrupt the assay operation by tampering either with the droplet-routing algorithm or with the actuation sequence. We demonstrate these attacks using a digital microfluidic synthesis simulator. The results show that the attacks are easy to implement and hard to detect. Therefore, this work highlights the need for effective protections against malicious modifications in DMFBs.

With the discovery of the Stuxnet malware in June 2010, Industrial Control System (ICS) security has gained global attention and scrutiny. Due to the unique industrial control operating environment, standard information technology host-based defenses such as operating system upgrades are not always feasible. Therefore, ICS security strategies must rely upon layered network infrastructure and enclave boundary defenses. As ICS threats evolve, so too must ICS security practices and strategies. ICS security innovation rely upon understanding the effectiveness of established defenses and countermeasures. In an effort to evaluate the security effectiveness of ICS layered perimeter defenses, a Red Team security assessment was conducted on an ICS test network. This experiment offers insight to the effectiveness of ICS perimeter defenses by demonstrating the reduction of attack vectors, decreased adversarial network access, and perimeter network defenses are an effective ICS security strategy.

Modern smart surveillance systems can not only record the monitored environment but also identify the targeted objects and detect anomaly activities. These advanced functions are often facilitated by deep neural networks, achieving very high accuracy and large data processing throughput. However, inappropriate design of the neural network may expose such smart systems to the risks of leaking the target being searched or even the adopted learning model itself to attackers. In this talk, we will present the security challenges in the design of smart surveillance systems. We will also discuss some possible solutions that leverage the unique properties of emerging nano-devices, including the incurred design and performance cost and optimization methods for minimizing these overheads.

Video surveillance, closed-circuit TV and IP-camera systems became virtually omnipresent and indispensable for many organizations, businesses, and users. Their main purpose is to provide physical security, increase safety, and prevent crime. They also became increasingly complex, comprising many communication means, embedded hardware and non-trivial firmware. However, most research to date focused mainly on the privacy aspects of such systems, and did not fully address their issues related to cyber-security in general, and visual layer (i.e., imagery semantics) attacks in particular. In this paper, we conduct a systematic review of existing and novel threats in video surveillance, closed-circuit TV and IP-camera systems based on publicly available data. The insights can then be used to better understand and identify the security and the privacy risks associated with the development, deployment and use of these systems. We study existing and novel threats, along with their existing or possible countermeasures, and summarize this knowledge into a comprehensive table that can be used in a practical way as a security checklist when assessing cyber-security level of existing or new CCTV designs and deployments. We also provide a set of recommendations and mitigations that can help improve the security and privacy levels provided by the hardware, the firmware, the network communications and the operation of video surveillance systems. We hope the findings in this paper will provide a valuable knowledge of the threat landscape that such systems are exposed to, as well as promote further research and widen the scope of this field beyond its current boundaries.

Controllers for software defined networks (SDNs) are quickly maturing to offer network operators more intuitive programming frameworks and greater abstractions for network application development. Likewise, many security solutions now exist within SDN environments for detecting and blocking clients who violate network policies. However, many of these solutions stop at triggering the security measure and give little thought to amending it. As a consequence, once the violation is addressed, no clear path exists for reinstating the flagged client beyond having the network operator reset the controller or manually implement a state change via an external command. This presents a burden for the network and its clients and administrators. Hence, we present a security policy transition framework for revoking security measures in an SDN environment once said measures are activated.

Ensuring security in the military applications of IoT is a big challenge. The main reasons for this state of affairs is that the sensor nodes of the network are usually mobile, use wireless links, have a small processing power and have a little energy resources. The paper presents the solution for cryptographic protection of transmission between sensor nodes in the data link layer and for cryptographic protection of data stored in the sensor node resources. For this purpose, the Trusted Platform Module (TPM) was used. The proposed solution makes it possible to build secure and fault tolerant sensor network. The following aspects were presented in the paper: the model of such a network, applied security solutions, analysis of the security in the network and selected investigation results of such a network were presented.

Technology coined as the vehicular ad hoc network (VANET) is harmonizing with Intelligent Transportation System (ITS) and Intelligent Traffic System (ITF). An application scenario of VANET is the military communication where vehicles move as a convoy on roadways, requiring secure and reliable communication. However, utilization of radio frequency (RF) communication in VANET limits its usage in military applications, due to the scarce frequency band and its vulnerability to security attacks. Visible Light Communication (VLC) has been recently introduced as a more secure alternative, limiting the reception of neighboring nodes with its directional transmission. However, secure vehicular VLC that ensures confidential data transfer among the participating vehicles, is an open problem. In this paper, we propose a secure military light communication protocol (SecVLC) for enabling efficient and secure data sharing. We use the directionality property of VLC to ensure that only target vehicles participate in the communication. Vehicles use full-duplex communication where infra-red (IR) is utilized to share a secret key and VLC is used to receive encrypted data. We experimentally demonstrate the suitability of SecVLC in outdoor scenarios at varying inter-vehicular distances with key metrics of interest, including the security, data packet delivery ratio and delay.

Physical layer security can ensure secure communication over noisy channels in the presence of an eavesdropper with unlimited computational power. We adopt an information theoretic variant of semantic-security (SS) (a cryptographic gold standard), as our secrecy metric and study the open problem of the type II wiretap channel (WTC II) with a noisy main channel is, whose secrecy-capacity is unknown even under looser metrics than SS. Herein the secrecy-capacity is derived and shown to be equal to its SS capacity. In this setting, the legitimate users communicate via a discrete-memory less (DM) channel in the presence of an eavesdropper that has perfect access to a subset of its choosing of the transmitted symbols, constrained to a fixed fraction of the block length. The secrecy criterion is achieved simultaneously for all possible eavesdropper subset choices. On top of that, SS requires negligible mutual information between the message and the eavesdropper's observations even when maximized over all message distributions. A key tool for the achievability proof is a novel and stronger version of Wyner's soft covering lemma. Specifically, the lemma shows that a random codebook achieves the soft-covering phenomenon with high probability. The probability of failure is doubly-exponentially small in the block length. Since the combined number of messages and subsets grows only exponentially with the block length, SS for the WTC II is established by using the union bound and invoking the stronger soft-covering lemma. The direct proof shows that rates up to the weak-secrecy capacity of the classic WTC with a DM erasure channel (EC) to the eavesdropper are achievable. The converse follows by establishing the capacity of this DM wiretap EC as an upper bound for the WTC II. From a broader perspective, the stronger soft-covering lemma constitutes a tool for showing the existence of codebooks that satisfy exponentially many constraints, a beneficial ability for many other applications in information theoretic security.