Biblio

Network monitoring is vital to the administration and operation of networks, but it requires privileged access that only highly trusted parties are granted. This severely limits the opportunity for external parties, such as service or equipment providers, auditors, or even clients, to measure the health or operation of a network in which they are stakeholders, but do not have access to its internal structure. In this position paper we propose the use of middleboxes to open up network monitoring to external parties using privacy-preserving technology. This will allow distrusted parties to make more inferences about the network state than currently possible, without learning any precise information about the network or the data that crosses it. Thus the state of the network will be more transparent to external stakeholders, who will be empowered to verify claims made by network operators. Network operators will be able to provide more information about their network without compromising security or privacy.

In-vehicle network security is becoming a major concern for the automotive industry. Although there is significant research done in this area, there is still a significant gap between research and what is actually applied in practice. Controller area network (CAN) gains the most concern of community but little attention is given to FlexRay. Many signs indicate the approaching end of CAN usage and starting with other promising technologies. FlexRay is considered one of the main players in the near future. We believe that migration era is near enough to change our mindset in order to supply industry with complete and mature security proposals with FlexRay. This changing mindset is important to fix the lagging issue appeared in CAN between research and industry. Then, we provide a complete migration of CAN authentication protocol towards FlexRay shows the availability of the protocol over different technologies.

Despite a long history and numerous proposed defenses, memory corruption attacks are still viable. A secure and low-overhead defense against return-oriented programming (ROP) continues to elude the security community. Currently proposed solutions still must choose between either not fully protecting critical data and relying instead on information hiding, or using incomplete, coarse-grain checking that can be circumvented by a suitably skilled attacker. In this paper, we present a light-weighted memory protection approach (LMP) that uses Intel's MPX hardware extensions to provide complete, fast ROP protection without having to rely in information hiding. We demonstrate a prototype that defeats ROP attacks while incurring an average runtime overhead of 3.9%.

The popularity of digital currencies, especially cryptocurrencies, has been continuously growing since the appearance of Bitcoin. Bitcoin is a peer-to-peer (P2P) cryptocurrency protocol enabling transactions between individuals without the need of a trusted authority. Its network is formed from resources contributed by individuals known as miners. Users of Bitcoin currency create transactions that are stored in a specialised data structure called a block chain. Bitcoin's security lies in a proof-of-work scheme, which requires high computational resources at the miners. These miners have to be synchronised with any update in the network, which produces high data traffic rates. Despite advances in mobile technology, no cryptocurrencies have been proposed for mobile devices. This is largely due to the lower processing capabilities of mobile devices when compared with conventional computers and the poorer Internet connectivity to that of the wired networking. In this work, we propose LocalCoin, an alternative cryptocurrency that requires minimal computational resources, produces low data traffic and works with off-the-shelf mobile devices. LocalCoin replaces the computational hardness that is at the root of Bitcoin's security with the social hardness of ensuring that all witnesses to a transaction are colluders. It is based on opportunistic networking rather than relying on infrastructure and incorporates characteristics of mobile networks such as users' locations and their coverage radius in order to employ an alternative proof-of-work scheme. Localcoin features (i) a lightweight proof-of-work scheme and (ii) a distributed block chain.

This paper introduces quarantine, a new security primitive for an operating system to use in order to protect information and isolate malicious behavior. Quarantine's core feature is the ability to fork a protection domain on-the-fly to isolate a specific principal's execution of untrusted code without risk of a compromise spreading. Forking enables the OS to ensure service continuity by permitting even high-risk operations to proceed, albeit subject to greater scrutiny and constraints. Quarantine even partitions executing threads that share resources into isolated protection domains. We discuss the design and implementation of quarantine within the LockDown OS, a security-focused evolution of the Composite component-based microkernel OS. Initial performance results for quarantine show that about 98% of the overhead comes from the cost of copying memory to the new protection domain.

IoT applications will rely on the connections between sensors and actuators and the internet. This will likely be wireless, and it will have to be low power.

How to get two Raspberry Pis to communicate over a 6LoWPAN network.

Wearable personal health monitoring systems can offer a cost effective solution for human healthcare. These systems must provide both highly accurate, secured and quick processing and delivery of vast amount of data. In addition, wearable biomedical devices are used in inpatient, outpatient, and at home e-Patient care that must constantly monitor the patient's biomedical and physiological signals 24/7. These biomedical applications require sampling and processing multiple streams of physiological signals with strict power and area footprint. The processing typically consists of feature extraction, data fusion, and classification stages that require a large number of digital signal processing and machine learning kernels. In response to these requirements, in this paper, a low-power, domain-specific many-core accelerator named Power Efficient Nano Clusters (PENC) is proposed to map and execute the kernels of these applications. Experimental results show that the manycore is able to reduce energy consumption by up to 80% and 14% for DSP and machine learning kernels, respectively, when optimally parallelized. The performance of the proposed PENC manycore when acting as a coprocessor to an Intel Atom processor is compared with existing commercial off-the-shelf embedded processing platforms including Intel Atom, Xilinx Artix-7 FPGA, and NVIDIA TK1 ARM-A15 with GPU SoC. The results show that the PENC manycore architecture reduces the energy by as much as 10X while outperforming all off-the-shelf embedded processing platforms across all studied machine learning classifiers.

In the last decade, the number of available cores increased and heterogeneity grew. In this work, we ask the question whether the design of the current operating systems (OSes) is still appropriate if these trends continue and lead to abundantly available but heterogeneous cores, or whether it forces a fundamental rethinking of how systems are designed. We argue that: 1. hiding heterogeneity behind a common hardware interface unifies, to a large extent, the control and coordination of cores and accelerators in the OS, 2. isolating at the network-on-chip rather than with processor features (like privileged mode, memory management unit, ...), allows running untrusted code on arbitrary cores, and 3. providing OS services via protocols over the network-on-chip, instead of via system calls, makes them accessible to arbitrary types of cores as well. In summary, this turns accelerators into first-class citizens and enables a single and convenient programming environment for all cores without the need to trust any application. In this paper, we introduce network-on-chip-level isolation, present the design of our microkernel-based OS, M3, and the common hardware interface, and evaluate the performance of our prototype in comparison to Linux. A bit surprising, without using accelerators, M3 outperforms Linux in some application-level benchmarks by more than a factor of five.

Nowadays, sentiment analysis methods become more and more popular especially with the proliferation of social media platform users number. In the same context, this paper presents a sentiment analysis approach which can faithfully translate the sentimental orientation of Arabic Twitter posts, based on a novel data representation and machine learning techniques. The proposed approach applied a wide range of features: lexical, surface-form, syntactic, etc. We also made use of lexicon features inferred from two Arabic sentiment words lexicons. To build our supervised sentiment analysis system, we use several standard classification methods (Support Vector Machines, K-Nearest Neighbour, Naïve Bayes, Decision Trees, Random Forest) known by their effectiveness over such classification issues. In our study, Support Vector Machines classifier outperforms other supervised algorithms in Arabic Twitter sentiment analysis. Via an ablation experiments, we show the positive impact of lexicon based features on providing higher prediction performance.

With the growth of internet world has transformed into a global market with all monetary and business exercises being carried online. Being the most imperative resource of the developing scene, it is the vulnerable object and hence needs to be secured from the users with dangerous personality set. Since the Internet does not have focal surveillance component, assailants once in a while, utilizing varied and advancing hacking topologies discover a path to bypass framework's security and one such collection of assaults is Intrusion. An intrusion is a movement of breaking into the framework by compromising the security arrangements of the framework set up. The technique of looking at the system information for the conceivable intrusions is known intrusion detection. For the last two decades, automatic intrusion detection system has been an important exploration point. Till now researchers have developed Intrusion Detection Systems (IDS) with the capability of detecting attacks in several available environments; latest on the scene are Machine Learning approaches. Machine learning techniques are the set of evolving algorithms that learn with experience, have improved performance in the situations they have already encountered and also enjoy a broad range of applications in speech recognition, pattern detection, outlier analysis etc. There are a number of machine learning techniques developed for different applications and there is no universal technique that can work equally well on all datasets. In this work, we evaluate all the machine learning algorithms provided by Weka against the standard data set for intrusion detection i.e. KddCupp99. Different measurements contemplated are False Positive Rate, precision, ROC, True Positive Rate.

Traditional vibration inspection systems, equipped with separated sensing and communication modules, are either very expensive (e.g., hundreds of dollars) and/or suffer from occlusion and narrow field of view (e.g., laser). In this work, we present an RFID-based solution, Tagbeat, to inspect mechanical vibration using COTS RFID tags and readers. Making sense of micro and high-frequency vibration using random and low-frequency readings of tag has been a daunting task, especially challenging for achieving sub-millisecond period accuracy. Our system achieves these three goals by discerning the change pattern of backscatter signal replied from the tag, which is attached on the vibrating surface and displaced by the vibration within a small range. This work introduces three main innovations. First, it shows how one can utilize COTS RFID to sense mechanical vibration and accurately discover its period with a few periods of short and noisy samples. Second, a new digital microscope is designed to amplify the micro-vibration-induced weak signals. Third, Tagbeat introduces compressive reading to inspect high-frequency vibration with relatively low RFID read rate. We implement Tagbeat using a COTS RFID device and evaluate it with a commercial centrifugal machine. Empirical benchmarks with a prototype show that Tagbeat can inspect the vibration period with a mean accuracy of 0.36ms and a relative error rate of 0.03%. We also study three cases to demonstrate how to associate our inspection solution with the specific domain requirements.

We present a novel type of Trojan trigger targeted at the field-programmable gate array (FPGA) design flow. Traditional triggers base on rare events, such as rare values or sequences. While in most cases these trigger circuits are able to hide a Trojan attack, exhaustive functional simulation and testing will reveal the Trojan due to violation of the specification. Our trigger behaves functionally and formally equivalent to the hardware description language (HDL) specification throughout the entire FPGA design flow, until the design is written by the place-and-route tool as bitstream configuration file . From then, Trojan payload is always on. We implement the trigger signal using a 4-input lookup table (LUT), each of the inputs connecting to the same signal. This lets us directly address the least significant bit (LSB) and most significant bit (MSB) of the LUT. With the remaining 14 bits, we realize a "magic" unary operation. This way, we are able to implement 16 different Triggers. We demonstrate the attack with a simple example and discuss the effectiveness of the recent detection techniques unused circuit identification (UCI), functional analysis for nearly-unused circuit identification (FANCI) and VeriTrust in order to reveal our trigger.

We consider the task of secure multi-party computation of arithmetic circuits over a finite field. Unlike Boolean circuits, arithmetic circuits allow natural computations on integers to be expressed easily and efficiently. In the strongest setting of malicious security with a dishonest majority –- where any number of parties may deviate arbitrarily from the protocol –- most existing protocols require expensive public-key cryptography for each multiplication in the preprocessing stage of the protocol, which leads to a high total cost. We present a new protocol that overcomes this limitation by using oblivious transfer to perform secure multiplications in general finite fields with reduced communication and computation. Our protocol is based on an arithmetic view of oblivious transfer, with careful consistency checks and other techniques to obtain malicious security at a cost of less than 6 times that of semi-honest security. We describe a highly optimized implementation together with experimental results for up to five parties. By making extensive use of parallelism and SSE instructions, we improve upon previous runtimes for MPC over arithmetic circuits by more than 200 times.

We present new applications for cryptographic secret handshakes between mobile devices on top of Bluetooth Low-Energy (LE). Secret handshakes enable mutual authentication, with the property that the parties learn nothing about each other unless they have been both issued credentials by a group administrator. This property provides strong privacy guarantees that enable interesting applications. One of them is proximity-based discovery for private communities. We introduce MASHaBLE, a mobile application that enables participants to discover and interact with nearby users if and only if they belong to the same secret community. We use direct peer-to-peer communication over Bluetooth LE, rather than relying on a central server. We discuss the specifics of implementing secret handshakes over Bluetooth LE and present our prototype implementation.

Standard routing protocols for IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) are mainly designed for data collection applications and work by establishing a tree-based network topology, which enables packets to be sent upwards, from the leaves to the root, adapting to dynamics of low-power communication links. The routing tables in such unidirectional networks are very simple and small since each node just needs to maintain the address of its parent in the tree, providing the best-quality route at every moment. In this work, we propose Matrix, a platform-independent routing protocol that utilizes the existing tree structure of the network to enable reliable and efficient any-to-any data traffic. Matrix uses hierarchical IPv6 address assignment in order to optimize routing table size, while preserving bidirectional routing. Moreover, it uses a local broadcast mechanism to forward messages to the right subtree when persistent node or link failures occur. We implemented Matrix on TinyOS and evaluated its performance both analytically and through simulations on TOSSIM. Our results show that the proposed protocol is superior to available protocols for 6LoWPAN, when it comes to any-to-any data communication, in terms of reliability, message efficiency, and memory footprint.

IP tracking and cloaking are practices for identifying users which are used legitimately by websites to provide services and content tailored to particular users. However, it is believed that these practices are also used by malicious websites to avoid detection by anti-virus companies crawling the web to find malware. In addition, malicious websites are also believed to use IP tracking in order to deliver targeted malware based upon a history of previous visits by users. In this paper we empirically investigate these beliefs and collect a large dataset of suspicious URLs in order to identify at what level IP tracking takes place that is at the level of an individual address or at the level of their network provider or organisation (Network tracking). Our results illustrate that IP tracking is used in a small subset of domains within our dataset while no strong indication of network tracking was observed.

Distributed Denial-of-Service (DDoS) attacks have steadily gained in popularity over the last decade, their intensity ranging from mere nuisance to severe. The increased number of attacks, combined with the loss of revenue for the targets, has given rise to a market for DDoS Protection Service (DPS) providers, to whom victims can outsource the cleansing of their traffic by using traffic diversion. In this paper, we investigate the adoption of cloud-based DPSs worldwide. We focus on nine leading providers. Our outlook on adoption is made on the basis of active DNS measurements. We introduce a methodology that allows us, for a given domain name, to determine if traffic diversion to a DPS is in effect. It also allows us to distinguish various methods of traffic diversion and protection. For our analysis we use a long-term, large-scale data set that covers well over 50\textbackslash% of all names in the global domain namespace, in daily snapshots, over a period of 1.5 years. Our results show that DPS adoption has grown by 1.24x in our measurement period, a prominent trend compared to the overall expansion of the namespace. Our study also reveals that adoption is often lead by big players such as large Web hosters, which activate or deactivate DDoS protection for millions of domain names at once.

The IETF has developed protocols that promote a healthy IPv4 and IPv6 co-existence. The Happy Eyeballs (HE) algorithm, for instance, prevents bad user experience in situations where IPv6 connectivity is broken. Using an active test (happy) that measures TCP connection establishment times, we evaluate the effects of the HE algorithm. The happy test measures against ALEXA top 10K websites from 80 SamKnows probes connected to dual-stacked networks representing 58 different ASes. Using a 3-years long (2013 - 2016) dataset, we show that TCP connect times to popular websites over IPv6 have considerably improved over time. As of May 2016, 18% of these websites are faster over IPv6 with 91% of the rest at most 1 ms slower. The historical trend shows that only around 1% of the TCP connect times over IPv6 were ever above the HE timer value (300 ms), which leaves around 2% chance for IPv4 to win a HE race towards these websites. As such, 99% of these websites prefer IPv6 connections more than 98% of the time. We show that although absolute TCP connect times (in ms) are not that far apart in both address families, HE with a 300 ms timer value tends to prefer slower IPv6 connections in around 90% of the cases. We show that lowering the HE timer value to 150 ms gives us a margin benefit of 10% while retaining same preference levels over IPv6.

To this date the majority of the existing instruments to measure trustworthiness in an online context are based on Likert scaling [1,3,11]. These however are somewhat restricted in applicability. Statements formed in Likert scaling are typically addressing one specific website. Therefore, adjusting these statements for other websites can be accompanied with a loss of validity. To meet these limitations, we propose to use semantic differential. Research has shown that using semantic differential is appropriate to measure multidimensional constructs [8,12] such as trust. Our novel approach in measuring trustworthiness exceeds Likert based scaling in its effortless application in different online context and its better translatability. After one pre-study and two online-studies with a total of 554 participants we achieved to develop a questionnaire with nine items which is comparable to other existing questionnaires in terms of reliability and internal consistency. But it overcomes the limitation of Likert scale based questionnaire.

The increasing complexity of cyber-attacks necessitates the design of more efficient hardware architectures for real-time Intrusion Detection Systems (IDSs). String matching is the main performance-demanding component of an IDS. An effective technique to design high-performance string matching engines is to partition the target set of strings into multiple subgroups and to use a parallel string matching hardware unit for each subgroup. This paper introduces a novel pattern grouping algorithm for heterogeneous bit-split string matching architectures. The proposed algorithm presents a reliable method to estimate the correlation between strings. The correlation factors are then used to find a preferred group for each string in a seed growing approach. Experimental results demonstrate that the proposed algorithm achieves an average of 41% reduction in memory consumption compared to the best existing approach found in the literature, while offering orders of magnitude faster execution time compared to an exhaustive search.

A key requirement for most security solutions is to provide secure cryptographic key storage in a way that will easily scale in the age of the Internet of Things. In this paper, we focus on providing such a solution based on Physical Unclonable Functions (PUFs). To this end, we focus on microelectromechanical systems (MEMS)-based gyroscopes and show via wafer-level measurements and simulations, that it is feasible to use the physical and electrical properties of these sensors for cryptographic key generation. After identifying the most promising features, we propose a novel quantization scheme to extract bit strings from the MEMS analog measurements. We provide upper and lower bounds for the minimum entropy of the derived bit strings and fully analyze the intra- and inter-class distributions across the operation range of the MEMS device. We complement these measurements via Monte-Carlo simulations based on the distributions of the parameters measured on actual devices. We also propose and evaluate a complete cryptographic key generation chain based on fuzzy extractors. We derive a full entropy 128-bit key using the obtained min-entropy estimates, requiring 1219 bits of helper data with an (authentication) failure probability of 4 . 10-7. In addition, we propose a dedicated MEMS-PUF design, which is superior to our measured sensor, in terms of chip area, quality and quantity of key seed features.

Obfuscation is a mechanism used to hinder reverse engineering of programs. To cope with the large number of obfuscated programs, especially malware, reverse engineers automate the process of deobfuscation i.e. extracting information from obfuscated programs. Deobfuscation techniques target specific obfuscation transformations, which requires reverse engineers to manually identify the transformations used by a program, in what is known as metadata recovery attack. In this paper, we present Oedipus, a Python framework that uses machine learning classifiers viz., decision trees and naive Bayes, to automate metadata recovery attacks against obfuscated programs. We evaluated Oedipus' performance using two datasets totaling 1960 unobfuscated C programs, which were used to generate 11.075 programs obfuscated using 30 configurations of 6 different obfuscation transformations. Our results empirically show the feasibility of using machine learning to implement the metadata recovery attacks with classification accuracies of 100% in some cases.

Software fault prediction is a significant part of software quality assurance and it is commonly used to detect faulty software modules based on software measurement data. Several machine learning based approaches have been proposed for generating predictive models from collected data, although none has become standard given the specificities of each software project. Hence, we believe that recommending the best algorithm for each project is much more important and useful than developing a single algorithm for being used in any project. For achieving that goal, we propose in this paper a novel framework for recommending machine learning algorithms that is capable of automatically identifying the most suitable algorithm according to the software project that is being considered. Our solution, namely SFP-MLF, makes use of the meta-learning paradigm in order to learn the best learner for a particular project. Results show that the SFP-MLF framework provides both the best single algorithm recommendation and also the best ranking recommendation for the software fault prediction problem.

Wearable tracking devices have gained widespread usage and popularity because of the valuable services they offer, monitoring human's health parameters and, in general, assisting persons to take a better care of themselves. Nevertheless, the security risks associated with such devices can represent a concern among consumers, because of the sensitive information these devices deal with, like sleeping patterns, eating habits, heart rate and so on. In this paper, we analyse the key security and privacy features of two entry level health trackers from leading vendors (Jawbone and Fitbit), exploring possible attack vectors and vulnerabilities at several system levels. The results of the analysis show how these devices are vulnerable to several attacks (perpetrated with consumer-level devices equipped with just bluetooth and Wi-Fi) that can compromise users' data privacy and security, and eventually call the tracker vendors to raise the stakes against such attacks.