Biblio

Existing approaches to cyber defense have been inadequate at defending the targets from advanced persistent threats (APTs). APTs are stealthy and orchestrated attacks, which target both corporations and governments to exfiltrate important data. In this paper, we present a novel comprehensibility manipulation framework (CMF) to generate a haystack of hard to comprehend fake documents, which can be used for deceiving attackers and increasing the cost of data exfiltration by wasting their time and resources. CMF requires an original document as input and generates fake documents that are both believable and readable for the attacker, possess no important information, and are hard to comprehend. To evaluate CMF, we experimented with college aptitude tests and compared the performance of many readers on separate reading comprehension exercises with fake and original content. Our results showed a statistically significant difference in the correct responses to the same questions across the fake and original exercises, thus validating the effectiveness of CMF operations to mislead.

This paper advocates programming high-performance code using partial evaluation. We present a clean-slate programming system with a simple, annotation-based, online partial evaluator that operates on a CPS-style intermediate representation. Our system exposes code generation for accelerators (vectorization/parallelization for CPUs and GPUs) via compiler-known higher-order functions that can be subjected to partial evaluation. This way, generic implementations can be instantiated with target-specific code at compile time. In our experimental evaluation we present three extensive case studies from image processing, ray tracing, and genome sequence alignment. We demonstrate that using partial evaluation, we obtain high-performance implementations for CPUs and GPUs from one language and one code base in a generic way. The performance of our codes is mostly within 10%, often closer to the performance of multi man-year, industry-grade, manually-optimized expert codes that are considered to be among the top contenders in their fields.

Software-defined networking (SDN) continues to grow in popularity because of its programmable and extensible control plane realized through network applications (apps). However, apps introduce significant security challenges that can systemically disrupt network operations, since apps must access or modify data in a shared control plane state. If our understanding of how such data propagate within the control plane is inadequate, apps can co-opt other apps, causing them to poison the control plane’s integrity. 

We present a class of SDN control plane integrity attacks that we call cross-app poisoning (CAP), in which an unprivileged app manipulates the shared control plane state to trick a privileged app into taking actions on its behalf. We demonstrate how role-based access control (RBAC) schemes are insufficient for preventing such attacks because they neither track information flow nor enforce information flow control (IFC). We also present a defense, ProvSDN, that uses data provenance to track information flow and serves as an online reference monitor to prevent CAP attacks. We implement ProvSDN on the ONOS SDN controller and demonstrate that information flow can be tracked with low-latency overheads.

Mining is the foundation of blockchain-based cryptocurrencies such as Bitcoin rewarding the miner for finding blocks for new transactions. The Monero currency enables mining with standard hardware in contrast to special hardware (ASICs) as often used in Bitcoin, paving the way for in-browser mining as a new revenue model for website operators. In this work, we study the prevalence of this new phenomenon. We identify and classify mining websites in 138M domains and present a new fingerprinting method which finds up to a factor of 5.7 more miners than publicly available block lists. Our work identifies and dissects Coinhive as the major browser-mining stakeholder. Further, we present a new method to associate mined blocks in the Monero blockchain to mining pools and uncover that Coinhive currently contributes 1.18% of mined blocks having turned over 1293 Moneros in June 2018.

Mobile two-factor authentication (2FA) has become commonplace along with the popularity of mobile devices. Current mobile 2FA solutions all require some form of user effort which may seriously affect the experience of mobile users, especially senior citizens or those with disability such as visually impaired users. In this paper, we propose Proximity-Proof, a secure and usable mobile 2FA system without involving user interactions. Proximity-Proof automatically transmits a user's 2FA response via inaudible OFDM-modulated acoustic signals to the login browser. We propose a novel technique to extract individual speaker and microphone fingerprints of a mobile device to defend against the powerful man-in-the-middle (MiM) attack. In addition, Proximity-Proof explores two-way acoustic ranging to thwart the co-located attack. To the best of our knowledge, Proximity-Proof is the first mobile 2FA scheme resilient to the MiM and co-located attacks. We empirically analyze that Proximity-Proof is at least as secure as existing mobile 2FA solutions while being highly usable. We also prototype Proximity-Proof and confirm its high security, usability, and efficiency through comprehensive user experiments.

The traditional security system requires a lot of manpower, and the wireless security system has been developed to reduce costs. However, for wireless systems, stability and reliability are important system indicators. In order to effectively improve these two indicators, we have imported butterfly network coding algorithm into the wireless sensing network. Because this algorithm enables each node to play multiple roles, such as routing, encoding, decoding, sending and receiving, it can also improve the throughput of network transmission, and effectively improve the stability and reliability of the wireless security system. This paper used the Wi-Fi module to implement the butterfly network coding algorithm, and is actually installed in the building. The basis for transmission and reception of all nodes in the network is received signal strength indication (RSSI). On the other hand, this is an IoT system for security monitoring.

The primary objective of Cognitive Radio Networks (CRN) is to opportunistically utilize the available spectrum for efficient and seamless communication. Like all other radio networks, Cognitive Radio Network also suffers from a number of security attacks and Primary User Emulation Attack (PUEA) is vital among them. Primary user Emulation Attack not only degrades the performance of the Cognitive Radio Networks but also dissolve the objective of Cognitive Radio Network. Efficient and secure authentication of Primary Users (PU) is an only solution to mitigate Primary User Emulation Attack but most of the mechanisms designed for this are either complex or make changes to the spectrum. Here, we proposed a mechanism to authenticate Primary Users in Cognitive Radio Network which is neither complex nor make any changes to spectrum. The proposed mechanism is secure and also has improved the performance of the Cognitive Radio Network substantially.

Embedded systems that communicate with each other over the internet and build up a larger, loosely coupled (hardware) system with an unknown configuration at runtime is often referred to as a cyberphysical system. Many of these systems can become, due to its associated risks during their operation, safety critical. With increased complexity of such systems, the number of configurations can either be infinite or even unknown at design time. Hence, a certification at design time for such systems that documents a safe interaction for all possible configurations of all participants at runtime can become unfeasible. If such systems come together in a new configuration, a mechanism is required that can decide whether or not it is safe for them to interact. Such a mechanism can generally not be part of such systems for the sake of trust. Therefore, we present in the following sections the SEnSE device, short for Secure and Safe Embedded, that tackles these challenges and provides a secure and safe integration of safety-critical embedded systems.

Due to the recent technological development, home appliances and electric devices are equipped with high-performance hardware device. Since demand of hardware devices is increased, production base become internationalized to mass-produce hardware devices with low cost and hardware vendors outsource their products to third-party vendors. Accordingly, malicious third-party vendors can easily insert malfunctions (also known as "hardware Trojans'') into their products. In this paper, we design six kinds of hardware Trojans at a gate-level netlist, and apply a neural-network (NN) based hardware-Trojan detection method to them. The designed hardware Trojans are different in trigger circuits. In addition, we insert them to normal circuits, and detect hardware Trojans using a machine-learning-based hardware-Trojan detection method with neural networks. In our experiment, we learned Trojan-infected benchmarks using NN, and performed cross validation to evaluate the learned NN. The experimental results demonstrate that the average TPR (True Positive Rate) becomes 72.9%, the average TNR (True Negative Rate) becomes 90.0%.

Large-scale parallel applications with complex global data dependencies beyond those of reductions pose significant scalability challenges in an asynchronous runtime system. Internodal challenges include identifying the all-to-all communication of data dependencies among the nodes. Intranodal challenges include gathering together these data dependencies into usable data objects while avoiding data duplication. This paper addresses these challenges within the context of a large-scale, industrial coal boiler simulation using the Uintah asynchronous many-task runtime system on GPU architectures. We show significant reduction in time spent analyzing data dependencies through refinements in our dependency search algorithm. Multiple task graphs are used to eliminate subsequent analysis when task graphs change in predictable and repeatable ways. Using a combined data store and task scheduler redesign reduces data dependency duplication ensuring that problems fit within host and GPU memory. These modifications did not require any changes to application code or sweeping changes to the Uintah runtime system. We report results running on the DOE Titan system on 119K CPU cores and 7.5K GPUs simultaneously. Our solutions can be generalized to other task dependency problems with global dependencies among thousands of nodes which must be processed efficiently at large scale.

Software-Defined Networking (SDN) is an emerging paradigm that introduces a concept of programmable networks to enhance the agility in networking management. By separating concerns of the data plane and the control plane, implementing network switching as packet forwarding, and using centralized software to logically control the entire networks, SDN makes it simpler to automate and configure the network to respond to high-level policy enforcement and dynamically changing network conditions. As SDN becomes more prevalent, its security issues are increasingly critical. Eaves-dropping attacks are one of the most common and important network attacks because they are relatively easy to implement and their effects can escalate to more severe attacks. This paper addresses the issue of how to cope with eavesdropping attacks in the SDN data plane by using multiple routing paths to reduce the severity of data leakage. While this existing approach appears to be considerably effective, our simple analysis uncovers that without a proper strategy of data communication, it can still lead to 100% of data exposure. The paper describes a remedy along with illustrations both analytically and experimentally. The results show that our proposed remedy can avoid such catastrophe and further reduces the percentage of risk from data exposure approximately by a factor of 1/n where n is the number of alternate disjoint paths.

Summary form only given. All-optical switching (AOS) has been observed in ferromagnetic (FM) layers and synthetic ferrimagnetic heterostructures [1-4]. In this work, we use anomalous Hall effect (AHE) measurements to demonstrate controlled helicity-dependent switching in synthetic ferrimagnetic heterostructures. The two FM layers are engineered to have different Curie temperatures Tc1 (fixed) and Tc2 (variable). We show that irrespective of whether Tc2 is higher or lower than Tc1, the final magnetic configuration of the heterostructure is controlled by using the laser polarization to set the magnetic state of the FM layer with the highest Tc. All samples were grown on glass substrates at room temperature by DC magnetron sputtering. Two sets of samples were prepared. The first set are single FM layers with layer composition Ta (3 nm)/Pt (4 nm)/FM1(2)/Pt capping (4 nm), where FM1 = Co (0.6 nm) is a Co layer and FM2 = CoFeB (tCoFeB)/Pt(0.4 nm)/ CoFeB (tCoFeB) (0.2 ≤ tCoFeB ≤ 0.6 nm) is a composite CoFeB layer where both CoFeB layers are ferromagnetically coupled and act as a single layer. FM1 and FM2 were used to produce the second set of synthetic ferrimagnetic samples with layer structure Ta (3 nm)/Pt (4 nm)/FM1/Pt (0.4 nm)/Ru (0.9 nm)/Pt (0.4 nm)/FM2/Pt capping (4 nm). The Ru layer provides the antiferromagnetic RKKY interlayer exchange coupling between the adjacent FM1 and FM2 layers while the Pt layers on either side of the Ru layer can tune the strength of the coupling and stabilize their perpendicular anisotropy [5]. To study the AOS, we use a Ti: sapphire fs-laser with a wavelength of 800 nm and a pulse duration of 43 fs. A quarter-wave plate is used to create a circularly polarized [right(σ+) and left-handed (σ-)] beam. We first measured the magnetic properties of the FM1 and FM2 layers using vibrating sample magnetometry (VSM). All FM samples show full remanence in perpendicular hyst- resis loops at room temperature (not shown). The temperature-dependent magnetization scans (not shown) give a Curie temperature Tc1 of 524 K for FM1. For FM2, increasing tCoFeB increases its Curie temperatureTc2. At tCoFeB = 0.5 nm, Tc2 - Tc1. Hall crosses are patterned by optical lithography and ion milling. The width of the current carrying wire is - 5 um, giving a DC current density of - 6 x 109 A/m2 during the measurement. Figure 1(a) shows the resulting perpendicular Hall hysteresis loop of the synthetic ferrimagnetic sample with tCoFeB = 0.2 nm. At remanence, the stable magnetic configurations are the two antiparallel orientations of FM1 and FM2 [State I and II in Fig. 1(a)]. To study the AOS, we swept the laser beam with a power of 0.45 mW and a speed of 1 μm/sec across the Hall cross, and the corresponding Hall voltage was constantly monitored. In Fig. 1(b), we show the normalized Hall voltage, VHall, as a function of the laser beam position x for both beam polarizations σ+ and σ-. The initial magnetic configuration was State I. When the beam is at the center of the cross (position B), both beam polarizations give VHall - 0. As the beam leaves the cross (position C), the σbeam changes the magnetic configurations from State I to State II (FM1 magnetization pointing down), while the system reverts to State I using the σ+ beam. Changing the initial configuration from State I to State II results in the same final magnetic configurations, determined by the laser beam polarizations (not shown). Similar results (not shown) were obtained for samples with tCoFeB ≤ 0.4 nm. However, at tCoFeB = 0.6 nm, the σbeam results in the final magnetic configurations with FM2 magnetization pointing down (State I) and the σ+ beam results in the State II configuration, suggesting that the final state is determined by the beam polar

Various communication protocols are currently used in the Internet of Things (IoT) devices. One of the protocols that are already standardized by ISO is MQTT protocol (ISO / IEC 20922: 2016). Many IoT developers use this protocol because of its minimal bandwidth requirement and low memory consumption. Sometimes, IoT device sends confidential data that should only be accessed by authorized people or devices. Unfortunately, the MQTT protocol only provides authentication for the security mechanism which, by default, does not encrypt the data in transit thus data privacy, authentication, and data integrity become problems in MQTT implementation. This paper discusses several reasons on why there are many IoT system that does not implement adequate security mechanism. Next, it also demonstrates and analyzes how we can attack this protocol easily using several attack scenarios. Finally, after the vulnerabilities of this protocol have been examined, we can improve our security awareness especially in MQTT protocol and then implement security mechanism in our MQTT system to prevent such attack.

Android allows 20 consecutive fail attempts on unlocking a device. This makes it difficult for pure guessing attacks to crack user patterns on a stolen device before it permanently locks itself. We investigate the effectiveness of combining Markov model-based guessing attacks with smudge attacks on unlocking Android devices within 20 attempts. Detected smudges are used to pre-compute all the possible segments and patterns, significantly reducing the pattern space that needs to be brute-forced. Our Markov-model was trained using 70% of a real-world pattern dataset that consists of 312 patterns. We recruited 12 participants to draw the remaining 30% on Samsung Galaxy S4, and used smudges they left behind to analyze the performance of the combined attack. Our results show that this combined method can significantly improve the performance of pure guessing attacks, cracking 74.17% of patterns compared to just 13.33% when the Markov model-based guessing attack was performed alone—those results were collected from a naive usage scenario where the participants were merely asked to unlock a given device. Even under a more complex scenario that asked the participants to use the Facebook app for a few minutes—obscuring smudges were added as a result—our combined attack, at 31.94%, still outperformed the pure guessing attack at 13.33%. Obscuring smudges can significantly affect the performance of smudge-based attacks. Based on this finding, we recommend that a mitigation technique should be designed to help users add obscurity, e.g., by asking users to draw a second random pattern upon unlocking a device.

Just-in-time return-oriented programming (JIT-ROP) is a powerful memory corruption attack that bypasses various forms of code randomization. Execute-only memory (XOM) can potentially prevent these attacks, but requires source code. In contrast, destructive code reads (DCR) provide a trade-off between security and legacy compatibility. The common belief is that DCR provides strong protection if combined with a high-entropy code randomization. The contribution of this paper is twofold: first, we demonstrate that DCR can be bypassed regardless of the underlying code randomization scheme. To this end, we show novel, generic attacks that infer the code layout for highly randomized program code. Second, we present the design and implementation of BGDX (Byte-Granular DCR and XOM), a novel mitigation technique that protects legacy binaries against code inference attacks. BGDX enforces memory permissions on a byte-granular level allowing us to combine DCR and XOM for legacy, off-the-shelf binaries. Our evaluation shows that BGDX is not only effective, but highly efficient, imposing only a geometric mean performance overhead of 3.95 % on SPEC.

We always use CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans Apart) to prevent automated bot for data entry. Although there are various kinds of CAPTCHAs, text-based scheme is still applied most widely, because it is one of the most convenient and user-friendly way for daily user [1]. The fact is that segmentations of different types of CAPTCHAs are not always the same, which means one of CAPTCHA's bottleneck is the segmentation. Once we could accurately split the character, the problem could be solved much easier. Unfortunately, the best way to divide them is still case by case, which is to say there is no universal way to achieve it. In this paper, we present a novel algorithm to achieve state-of-the-art performance, what was more, we also constructed a new convolutional neural network as an add-on recognition part to stabilize our state-of-the-art performance of the whole CAPTCHA system. The CAPTCHA datasets we are using is from the State Administration for Industry& Commerce of the People's Republic of China. In this datasets, there are totally 33 entrances of CAPTCHAs. In this experiments, we assume that each of the entrance is known. Results are provided showing how our algorithms work well towards these CAPTCHAs.

Secure elements widely used in smartphones, digital consumer electronics, and payment systems are subject to fault attacks. To thwart such attacks, software protections are manually inserted requiring experts and time. The explosion of the Internet of Things (IoT) in home, business, and public spaces motivates the hardening of a wider class of applications and the need to offer security solutions to non-experts. This article addresses the automated protection of loops at compilation time, covering the widest range of control- and data-flow patterns, in both shape and complexity. The security property we consider is that a sensitive loop must always perform the expected number of iterations; otherwise, an attack must be reported. We propose a generic compile-time loop hardening scheme based on the duplication of termination conditions and of the computations involved in the evaluation of such conditions. We also investigate how to preserve the security property along the compilation flow while enabling aggressive optimizations. We implemented this algorithm in LLVM 4.0 at the Intermediate Representation (IR) level in the backend. On average, the compiler automatically hardens 95% of the sensitive loops of typical security benchmarks, and 98% of these loops are shown to be robust to simulated faults. Performance and code size overhead remain quite affordable, at 12.5% and 14%, respectively.

Existing distributed asynchronous graph processing systems employ checkpointing to capture globally consistent snapshots and rollback all machines to most recent checkpoint to recover from machine failures. In this paper we argue that recovery in distributed asynchronous graph processing does not require the entire execution state to be rolled back to a globally consistent state due to the relaxed asynchronous execution semantics. We define the properties required in the recovered state for it to be usable for correct asynchronous processing and develop CoRAL, a lightweight checkpointing and recovery algorithm. First, this algorithm carries out confined recovery that only rolls back graph execution states of the failed machines to affect recovery. Second, it relies upon lightweight checkpoints that capture locally consistent snapshots with a reduced peak network bandwidth requirement. Our experiments using real-world graphs show that our technique recovers from failures and finishes processing 1.5x to 3.2x faster compared to the traditional asynchronous checkpointing and recovery mechanism when failures impact 1 to 6 machines of a 16 machine cluster. Moreover, capturing locally consistent snapshots significantly reduces intermittent high peak bandwidth usage required to save the snapshots – the average reduction in 99th percentile bandwidth ranges from 22% to 51% while 1 to 6 snapshot replicas are being maintained.

The huge amount of sensory data collected from mobile devices has offered great potentials to promote more significant services based on user data extracted from sensor readings. However, releasing user data could also seriously threaten user privacy. It is possible to directly collect sensitive information from released user data without user permissions. Furthermore, third party users can also infer sensitive information contained in released data in a latent manner by utilizing data mining techniques. In this paper, we formally define these two types of threats as inherent data privacy and latent data privacy and construct a data-sanitization strategy that can optimize the tradeoff between data utility and customized two types of privacy. The key novel idea lies that the developed strategy can combat against powerful third party users with broad knowledge about users and launching optimal inference attacks. We show that our strategy does not reduce the benefit brought by user data much, while sensitive information can still be protected. To the best of our knowledge, this is the first work that preserves both inherent data privacy and latent data privacy.

To facilitate collaboration over sensitive data, we present DataSynthesizer, a tool that takes a sensitive dataset as input and generates a structurally and statistically similar synthetic dataset with strong privacy guarantees. The data owners need not release their data, while potential collaborators can begin developing models and methods with some confidence that their results will work similarly on the real dataset. The distinguishing feature of DataSynthesizer is its usability — the data owner does not have to specify any parameters to start generating and sharing data safely and effectively. DataSynthesizer consists of three high-level modules — DataDescriber, DataGenerator and ModelInspector. The first, DataDescriber, investigates the data types, correlations and distributions of the attributes in the private dataset, and produces a data summary, adding noise to the distributions to preserve privacy. DataGenerator samples from the summary computed by DataDescriber and outputs synthetic data. ModelInspector shows an intuitive description of the data summary that was computed by DataDescriber, allowing the data owner to evaluate the accuracy of the summarization process and adjust any parameters, if desired. We describe DataSynthesizer and illustrate its use in an urban science context, where sharing sensitive, legally encumbered data between agencies and with outside collaborators is reported as the primary obstacle to data-driven governance. The code implementing all parts of this work is publicly available at https://github.com/DataResponsibly/DataSynthesizer.

Because of the explosive growth of Android malware and due to the severity of its damages, the detection of Android malware has become an increasing important topic in cybersecurity. Currently, the major defense against Android malware is commercial mobile security products which mainly use signature-based method for detection. However, attackers can easily devise methods, such as obfuscation and repackaging, to evade the detection, which calls for new defensive techniques that are harder to evade. In this paper, resting on the analysis of Application Programming Interface (API) calls extracted from the smali files, we further categorize the API calls which belong to the some method in the smali code into a block. Based on the generated API call blocks, we then explore deep neural networks (i.e., Deep Belief Network (DBN) and Stacked AutoEncoders (SAEs)) for newly unknown Android malware detection. Using a real sample collection from Comodo Cloud Security Center, a comprehensive experimental study is performed to compare various malware detection approaches. The experimental results demonstrate that (1) our proposed feature extraction method (i.e., using API call blocks) outperforms using API calls directly in Android malware detection; (2) DBN works better than SAEs in this application; and (3) the detection performance of deep neural networks is better than shallow learning architectures.

Despite their ubiquity, many password meters provide inaccurate strength estimates. Furthermore, they do not explain to users what is wrong with their password or how to improve it. We describe the development and evaluation of a data-driven password meter that provides accurate strength measurement and actionable, detailed feedback to users. This meter combines neural networks and numerous carefully combined heuristics to score passwords and generate data-driven text feedback about the user's password. We describe the meter's iterative development and final design. We detail the security and usability impact of the meter's design dimensions, examined through a 4,509-participant online study. Under the more common password-composition policy we tested, we found that the data-driven meter with detailed feedback led users to create more secure, and no less memorable, passwords than a meter with only a bar as a strength indicator.

There are many object oriented design patterns and frameworks; to make the Information System robust, scalable and extensible. The objected oriented patterns are classified in the category of creational, structural, behavioral, security, concurrency, and user interface, relational, social and distributed. All the above classified design pattern doesn't work to provide a pathway and standards to make the Information system, to fulfill the requirement of confidentiality, Integrity and availability. This research work will explore the gap and suggest possible object oriented design pattern focusing the information security perspectives of the information system. At application level; this object oriented design pattern/framework shall try to ensure the Confidentiality, Integrity and Availability of the information systems intuitively. The main objective of this research work is to create a theoretical background of object oriented framework and design pattern which ensure confidentiality, integrity and availability of the system developed through the object oriented paradigm.

It has been discovered that quite a few organizations have become the victims of APT, which is a deliberate and malicious espionage threat to military, political, infrastructure targets for the purpose of stealing the core data or thwarting the normal operation of the organizations. Thus, working out a solution for detecting and predicting APT is a major goal for scientific research. But APT has a characteristic feature of good concealment which prevent we capturing it just in time by existing solutions. In this paper, through a deep study of Cyber Kill Chain, we proposed a solution to detect and predict APTs with hierarchical Knowledge reasoning on the basis of cyber-security-monitoring, intelligence-gathering, etc. The solution seeks for connections between real-time alarms and the intelligence from Hacker Profile, Cyber Resources Profile, Social Engineering Database, Cyber Attack Tool Fingerprint Database, Vulnerability Database, Malicious Code Genome Map, etc. According to our experiments, it is effective and has high accuracy.