Biblio

Monitoring network behaviors of mobile applications, controlling their resource access and detecting potentially harmful apps are becoming increasingly important for the security protection within today's organizational, ISP and carriers. For this purpose, apps need to be identified from their communication, based upon their individual traffic signatures (called imprints in our research). Creating imprints for a large number of apps is nontrivial, due to the challenges in comprehensively analyzing their network activities at a large scale, for millions of apps on today's rapidly-growing app marketplaces. Prior research relies on automatic exploration of an app's user interfaces (UIs) to trigger its network activities, which is less likely to scale given the cost of the operation (at least 5 minutes per app) and its effectiveness (limited coverage of an app's behaviors). In this paper, we present Tiger (Traffic Imprint Generator), a novel technique that makes comprehensive app imprint generation possible in a massive scale. At the center of Tiger is a unique instantiated slicing technique, which aggressively prunes the program slice extracted from the app's network-related code by evaluating each variable's impact on possible network invariants, and removing those unlikely to contribute through assigning them concrete values. In this way, Tiger avoids exploring a large number of program paths unrelated to the app's identifiable traffic, thereby reducing the cost of the code analysis by more than one order of magnitude, in comparison with the conventional slicing and execution approach. Our experiments show that Tiger is capable of recovering an app's full network activities within 18 seconds, achieving over 98% coverage of its identifiable packets and 0.742% false detection rate on app identification. Further running the technique on over 200,000 real-world Android apps (including 78.23% potentially harmful apps) leads to the discovery of surprising new types of traffic invariants, including fake device information, hardcoded time values, session IDs and credentials, as well as complicated trigger conditions for an app's network activities, such as human involvement, Intent trigger and server-side instructions. Our findings demonstrate that many network activities cannot easily be invoked through automatic UI exploration and code-analysis based approaches present a promising alternative.

Information-Centric Networking (ICN) 1 is a significant networking paradigm for the Internet of Things, which is an information-centric network in essence. The ICN paradigm owns inherently some security features, but also brings several new vulnerabilities. The most significant one among them is Interest flooding, which is a new type of Denial of Service (DoS) attack, and has even more serious effects to the whole network in the ICN paradigm than in the traditional IP paradigm. In this paper, we suggest a new mechanism to mitigate Interest flooding attack. The detection of Interest flooding and the corresponding mitigation measures are implemented on the edge routers, which are directly connected with the attackers. By using statistics of Interest satisfaction rate on the incoming interface of some edge routers, malicious name-prefixes or interfaces can be discovered, and then dropped or slowed down accordingly. With the help of the network information, the detected malicious name-prefixes and interfaces can also be distributed to the whole network quickly, and the attack can be mitigated quickly. The simulation results show that the suggested mechanism can reduce the influence of the Interest flooding quickly, and the network performance can recover automatically to the normal state without hurting the legitimate users.

Bayesian Network is the popular and important data mining model for representing uncertain knowledge. For large scale data it is often too costly to learn the accurate structure. To resolve this problem, much work has been done on migrating the structure learning algorithms to the MapReduce framework. In this paper, we introduce a distributed hybrid structure learning algorithm by combining the advantages of constraint-based and score-and-search-based algorithms. By reusing the intermediate results of MapReduce, the algorithm greatly simplified the computing work and got good results in both efficiency and accuracy.

The IEC 61850 protocol suite for electrical sub-station automation enables substation configuration and design for protection, communication, and control. These power system applications can be formally verified through use of object models, common data classes, and message classes. The IEC 61850-7-420 DER (Distributed Energy Resource) extension further defines object classes for assets such as types of DER (e.g., energy storage, photovoltaic), DER unit controllers, and other DER-associated devices (e.g., inverter). These object classes describe asset-specific attributes such as state of charge, capacity limits, and ramp rate. Attributes can be fixed (rated capacity of the device) dynamic (state of charge), or binary (on or off, dispatched or off-line, operational or fault state). We sketch out a proposed ontology based on the 61850 and 61850-7-420 DER object classes to model threats against a micro-grid, which is an electrical system consisting of controllable loads and distributed generation that can function autonomously (in island mode) or connected to a larger utility grid. We consider threats against the measurements on which the control loop is based, as well as attacks against the control directives and the communication infrastructure. We use this ontology to build a threat model using the ADversary View Security Evaluation (ADVISE) framework, which enables identification of attack paths based on adversary objectives (for example, destabilize the entire micro-grid by reconnecting to the utility without synchronization) and helps identify defender strategies. Furthermore, the ADVISE method provides quantitative security metrics that can help inform trade-off decisions made by system architects and controls.

This work targets the automated minimum-energy optimization of Quantized Neural Networks (QNNs) - networks using low precision weights and activations. These networks are trained from scratch at an arbitrary fixed point precision. At iso-accuracy, QNNs using fewer bits require deeper and wider network architectures than networks using higher precision operators, while they require less complex arithmetic and less bits per weights. This fundamental trade-off is analyzed and quantified to find the minimum energy QNN for any benchmark and hence optimize energy-efficiency. To this end, the energy consumption of inference is modeled for a generic hardware platform. This allows drawing several conclusions across different benchmarks. First, energy consumption varies orders of magnitude at iso-accuracy depending on the number of bits used in the QNN. Second, in a typical system, BinaryNets or int4 implementations lead to the minimum energy solution, outperforming int8 networks up to 2-10× at iso-accuracy. All code used for QNN training is available from https://github.com/BertMoons/.

Phasor measurement units (PMUs) provide high-fidelity situational awareness of electric power grid operations. PMU data are used in real-time to inform wide area state estimation, monitor area control error, and event detection. As PMU data becomes more reliable, these devices are finding roles within control systems such as demand response programs and early fault detection systems. As with other cyber physical systems, maintaining data integrity and security are significant challenges for power system operators. In this paper, we present a comprehensive study of multiple machine learning techniques for detecting malicious data injection within PMU data streams. The two datasets used in this study are from the Bonneville Power Administration's PMU network and an inter-university PMU network among three universities, located in the U.S. Pacific Northwest. These datasets contain data from both the transmission level and the distribution level. Our results show that both SVM and ANN are generally effective in detecting spoofed data, and TensorFlow, the newly released tool, demonstrates potential for distributing the training workload and achieving higher performance. We expect these results to shed light on future work of adopting machine learning and data analytics techniques in the electric power industry.

Random Subdomain DDoS attacks on the Domain Name System (DNS) infrastructure are becoming a popular vector in recent attacks (e.g., recent Mirai attack on Dyn). In these attacks, many queries are sent for a single or a few victim domains, yet they include highly varying non-existent subdomains generated randomly. Motivated by these attacks we designed and implemented novel and efficient algorithms for distinct heavy hitters (dHH). A (classic) heavy hitter (HH) in a stream of elements is a key (e.g., the domain of a query) which appears in many elements (e.g., requests). When stream elements consist of ¡key, subkey¿ pairs, (¡domain, subdomain¿) a distinct heavy hitter (dhh) is a key that is paired with a large number of different subkeys. Our algorithms dominate previous designs in both the asymptotic (theoretical) sense and practicality. Specifically the new fixed-size algorithms are simple to code and with asymptotically optimal space accuracy tradeoffs. Based on these algorithms, we build and implement a system for detection and mitigation of Random Subdomain DDoS attacks. We perform experimental evaluation, demonstrating the effectiveness of our algorithms.

The use of machine learning models has become ubiquitous. Their predictions are used to make decisions about healthcare, security, investments and many other critical applications. Given this pervasiveness, it is not surprising that adversaries have an incentive to manipulate machine learning models to their advantage. One way of manipulating a model is through a poisoning or causative attack in which the adversary feeds carefully crafted poisonous data points into the training set. Taking advantage of recently developed tamper-free provenance frameworks, we present a methodology that uses contextual information about the origin and transformation of data points in the training set to identify poisonous data, thereby enabling online and regularly re-trained machine learning applications to consume data sources in potentially adversarial environments. To the best of our knowledge, this is the first approach to incorporate provenance information as part of a filtering algorithm to detect causative attacks. We present two variations of the methodology - one tailored to partially trusted data sets and the other to fully untrusted data sets. Finally, we evaluate our methodology against existing methods to detect poison data and show an improvement in the detection rate.

A hardware Trojan is a malicious circuit inserted into a device by a malicious designer or manufacturer in the circuit design or fabrication phase. With the globalization of semiconductor industry, more and more chips and devices are designed, integrated and fabricated by untrusted manufacturers, who can potentially insert hardware Trojans for launching attacks after the devices are deployed. Moreover, the most damaging attack in a smart grid is a large scale electricity failure, which can cause very serious consequences that are worse than any disaster. Unfortunately, this attack can be implemented very easily by synchronized hardware Trojans acting as a collective offline time bomb; the Trojans do not need to interact with one another and can affect a large fraction of nodes in a power grid. More sophisticatedly, this attack can also be realized by online hardware Trojans which keep listening to the communication channel and wait for a trigger event to trigger their malicious payloads; here, a broadcast message triggers all the Trojans at the same time. In this paper, we address the offline synchronized hardware Trojan attack, as it does not require the adversary to penetrate the power grid network for sending triggers. We classify two types of offline synchronized hardware Trojan attacks as type A and B: type B requires communication between different nodes, and type A does not. The hardware Trojans needed for type B turn out to be much more complex (and therefore larger in area size) than those for type A. In order to prevent type A attacks we suggest to enforce each power grid node to work in an unique time domain which has a random time offset to Universal Coordinated Time (UTC). This isolation principle can mitigate type A offline synchronized hardware Trojan attacks in a smart grid, such that even if hardware Trojans are implanted in functional units, e.g. Phasor Measurement Units (PMUs) and Remote Terminal Units (RTUs), they can only cause a minimal damage, i.e. sporadic single node failures. The proposed solution only needs a trusted Global Positioning System (GPS) module which provides the correct UTC together with small additional interface circuitry. This means that our solution can be used to protect the current power grid infrastructure against type A offline attacks without replacing any untrusted functional unit, which may already have embedded hardware Trojans.

In this paper1 is proposed a graph model, designed to solve security challenges of information systems (IS). The model allows to describe information systems at two levels. The first is the transport layer, represented by the graph, and the second is functional level, represented by the semantic network. Proposed model uses "subject-object" terms to establish a security policy. Based on the proposed model, one can define information system security features location, and choose their deployment in the best way. In addition, it is possible to observe data access control security features inadequacy and calculate security value for the each IS node. Novelty of this paper is that one can get numerical evaluation of IS security according to its nodes communications and network structure.

Memory-corruption vulnerabilities pose a severe threat on modern systems security. Although this problem is known for almost three decades it is unlikely to be solved in the near future because a large amount of modern software is still programmed in unsafe, legacy languages such as C/C++. With new vulnerabilities in popular software discovered almost every day, and with high third party demand for (purchasing) the corresponding exploits, runtime attacks are more prevalent than ever. Even perfect cryptography can easily be undermined by exploiting software vulnerabilities. Typically, one vulnerability in wide-spread software (e.g., Tor Browser) is sufficient for the adversary to compromise all users. Moving target approaches such as software diversity [2] and system randomization techniques [7] are considered to be effective and practical means to strongly reduce the scale of such attacks because ideally, the adversary would require to craft a unique exploit per user. However, recently it was shown that existing software-randomization schemes can be circumvented by practical exploitation techniques such as Just-In-Time Return Oriented Programming (JIT-ROP) that takes advantage of information leakage [1]. The attack demonstrated that even a single disclosed code pointer can be exploited to defeat any (fine-grained) code randomization scheme. Later, it was shown that there are various sources of information leakage that can be exploited such as virtual function pointers [4]. JIT-ROP motivated a number of subsequent works to prevent the adversary from reading code such as Readactor [3,5], or ASLR Guard [8]. For instance, Readactor and its successor Readactor++ [3,5] use various techniques to prevent direct and indirect code disclosure, which seems to be non-trivial in general [6]. The arms race will continue.

The invent of distributed programming frameworks like Hadoop paved way for processing voluminous data known as big data. Due to exponential growth of data, enterprises started to exploit the availability of cloud infrastructure for storing and processing big data. Insider attacks on outsourced data causes leakage of sensitive data. Therefore, it is essential to sanitize data so as to preserve privacy or non-disclosure of sensitive data. Privacy Preserving Data Publishing (PPDP) and Privacy Preserving Data Mining (PPDM) are the areas in which data sanitization plays a vital role in preserving privacy. The existing anonymization techniques for MapReduce programming can be improved to have a misusability measure for determining the level of sanitization to be applied to big data. To overcome this limitation we proposed a framework known as M-Sanit which has mechanisms to exploit misusability score of big data prior to performing sanitization using MapReduce programming paradigm. Our empirical study using the real world cloud eco system such as Amazon Elastic Cloud Compute (EC2) and Amazon Elastic MapReduce (EMR) reveals the effectiveness of misusability score based sanitization of big data prior to publishing or mining it.

Recent advances on the integration of control systems with state of the art information technologies have brought into play new uncertainties, not only associated with the physical world, but also from a cyber-space's perspective. In cyber-physical environments, awareness and resilience are invaluable properties. The paper focuses on the development of an architecture relying on a hierarchical multi-agent framework for resilience enhancement. This framework was evaluated on a test-bed comprising several distributed computational devices and heterogeneous communications. Results from tests prove the relevance of the proposed approach.

During the last decade, the smart digital environment has become one of the most scientific challenges that occupy scientists and researchers. This new environment consists basically of smart connected products including three main parts: the physical mechanical/electrical product, the smart part of the product made from embedded software and human machine interface, and finally the connectivity part including antennas and routing protocols insuring the wired/wireless communication with other products, from our side, we are involved in the implementation of the latter part by developing a routing protocol that will meet the increasingly demanding requirements of today's systems (security, bandwidth, network lifetime, ...). Based on the researches carried out in other fields of application such as MANETS, multi-path routing fulfills our expectations. In this article, the MPOLSR protocol was chosen as an example, comparing its standard version and its improvements in order to choose the best solution that can be applied in the smart digital environment.

Today cyber security is one of the most active fields of re- search due to its wide range of impact in business, govern- ment and everyday life. In recent years machine learning methods and algorithms have been quite successful in a num- ber of security areas. In this paper, we explore an approach to classify intrusion called multi-perspective machine learn- ing (MPML). For any given cyber-attack there are multiple methods of detection. Every method of detection is built on one or more network characteristic. These characteristics are then represented by a number of network features. The main idea behind MPML is that, by grouping features that support the same characteristics into feature subsets called perspectives, this will encourage diversity among perspectives (classifiers in the ensemble) and improve the accuracy of prediction. Initial results on the NSL- KDD dataset show at least a 4% improvement over other ensemble methods such as bagging boosting rotation forest and random for- est.

JavaScript is the source of many security problems, including cross-site scripting attacks and malicious advertising code. Central to these problems is the fact that code from untrusted sources runs with full privileges. Information flow controls help prevent violations of data confidentiality and integrity. This article explores faceted values, a mechanism for providing information flow security in a dynamic manner that avoids the stuck executions of some prior approaches, such as the no-sensitive-upgrade technique. Faceted values simultaneously simulate multiple executions for different security levels to guarantee termination-insensitive noninterference. We also explore the interaction of faceted values with exceptions, declassification, and clearance.

Field Programmable Gate Arrays (FPGAs) are being increasingly deployed in diverse applications including the emerging Internet of Things (IoT), biomedical, and automotive systems. However, security of the FPGA configuration file (i.e. bitstream), especially during in-field reconfiguration, as well as effective safeguards against unauthorized tampering and piracy during operation, are notably lacking. The current practice of bitstreram encryption is only available in high-end FPGAs, incurs unacceptably high overhead for area/energy-constrained devices, and is susceptible to side channel attacks. In this paper, we present a fundamentally different and novel approach to FPGA security that can protect against all major attacks on FPGA, namely, unauthorized in-field reprogramming, piracy of FPGA intellectual property (IP) blocks, and targeted malicious modification of the bitstream. Our approach employs the security through diversity principle to FPGA, which is often used in the software domain. We make each device architecturally different from the others using both physical (static) and logical (time-varying) configuration keys, ensuring that attackers cannot use a priori knowledge about one device to mount an attack on another. It therefore mitigates the economic motivation for attackers to reverse engineering the bitstream and IP. The approach is compatible with modern remote upgrade techniques, and requires only small modifications to existing FPGA tool flows, making it an attractive addition to the FPGA security suite. Our experimental results show that the proposed approach achieves provably high security against tampering and piracy with worst-case 14% latency overhead and 13% area overhead.

Recently, both academia and industry have recognized the need for leveraging real-time information for the purposes of specifying, enforcing and maintaining rich and flexible authorization policies. In such a context, security-related properties, a.k.a., attributes, have been recognized as a convenient abstraction for providing a well-defined representation of such information, allowing for them to be created and exchanged by different independently-run organizational domains for authorization purposes. However, attackers may attempt to compromise the way attributes are generated and communicated by recurring to hacking techniques, e.g., forgery, in an effort to bypass authorization policies and their corresponding enforcement mechanisms and gain unintended access to sensitive resources as a result. In this paper, we propose a novel technique that allows for enterprises to pro-actively collect attributes from the different entities involved in the access request process, e.g., users, subjects, protected resources, and running environments. After the collection, we aim to carefully select the attributes that uniquely identify the aforementioned entities, and randomly mutate the original access policies over time by adding additional policy rules constructed from the newly-identified attributes. This way, even when attackers are able to compromise the original attributes, our mutated policies may offer an additional layer of protection to deter ongoing and future attacks. We present the rationale and experimental results supporting our proposal, which provide evidence of its suitability for being deployed in practice.

Human actuation is the idea of using people to provide large-scale force feedback to users. The Haptic Turk system, for example, used four human actuators to lift and push a virtual reality user; TurkDeck used ten human actuators to place and animate props for a single user. While the experience of human actuators was decent, it was still inferior to the experience these people could have had, had they participated as a user. In this paper, we address this issue by making everyone a user. We introduce mutual human actuation, a version of human actuation that works without dedicated human actuators. The key idea is to run pairs of users at the same time and have them provide human actuation to each other. Our system, Mutual Turk, achieves this by (1) offering shared props through which users can exchange forces while obscuring the fact that there is a human on the other side, and (2) synchronizing the two users' timelines such that their way of manipulating the shared props is consistent across both virtual worlds. We demonstrate mutual human actuation with an example experience in which users pilot kites though storms, tug fish out of ponds, are pummeled by hail, battle monsters, hop across chasms, push loaded carts, and ride in moving vehicles.

Weak security, excessive personal data collection for user profiling, and a poor user experience are just a few of the many problems that mobile authentication solutions suffer from. Despite being an interesting platform, mobile devices are still not being used to their full potential for authentication. n-Auth is a firm step in unlocking the full potential of mobile devices in authentication, by improving both security and usability whilst respecting the privacy of the user. Our focus is on the combined usage of several strong cryptographic techniques with secure HCI design principles to achieve a better user experience. We specified and built n-Auth, for which robust Android and iOS apps are openly available through the official stores.

Immersive, stereoscopic visualization enables scientists to better analyze structural and physical phenomena compared to traditional display mediums. Unfortunately, current head-mounted displays (HMDs) with the high rendering quality necessary for these complex datasets are prohibitively expensive, especially in educational settings where their high cost makes it impractical to buy several devices. To address this problem, we develop two tools: (1) An authoring tool allows domain scientists to generate a set of connected, 360° video paths for traversing between dimensional keyframes in the dataset. (2) A corresponding navigational interface is a video selection and playback tool that can be paired with a low-cost HMD to enable an interactive, non-linear, storytelling experience. We demonstrate the authoring tool's utility by conducting several case studies and assess the navigational interface with a usability study. Results show the potential of our approach in effectively expanding the accessibility of high-quality, immersive visualization to a wider audience using affordable HMDs.

Lately, mobile operators experience threats from SMS grey routes which are used by fraudsters to evade SMS fees and to deny them millions in revenues. But more serious are the threats to the user's security and privacy and consequently the operator's reputation. Therefore, it is crucial for operators to have adequate solutions to protect both their network and their customers against this kind of fraud. Unfortunately, so far there is no sufficiently efficient countermeasure against grey routes. This paper proposes a near real time SMS grey traffic detection which makes use of Counting Bloom Filters combined with blacklist and whitelist to detect SMS grey traffic on the fly and to block them. The proposed detection has been implemented and proved to be quite efficient. The paper provides also comprehensive explanation of SMS grey routes and the challenges in their detection. The implementation and verification are also described thoroughly.

Internet Protocol version 6 (IPv6) over Low-power Wireless Personal Area Networks (6LoWPAN) is extensively used in wireless sensor networks due to its capability to transmit IPv6 packets with low bandwidth and limited resources. 6LoWPAN has several operations in each layer. Most existing security challenges are focused on the network layer, which is represented by the Routing Protocol for Low-power and Lossy Networks (RPL). 6LoWPAN, with its routing protocol (RPL), usually uses nodes that have constrained resources (memory, power, and processor). In addition, RPL messages are exchanged among network nodes without any message authentication mechanism, thereby exposing the RPL to various attacks that may lead to network disruptions. A sinkhole attack utilizes the vulnerabilities in an RPL and attracts considerable traffic by advertising falsified data that change the routing preference for other nodes. This paper proposes the neighbor-passive monitoring technique (NPMT) for detecting sinkhole attacks in RPL-based networks. The proposed technique is evaluated using the COOJA simulator in terms of power consumption and detection accuracy. Moreover, NPMT is compared with popular detection mechanisms.

In recent years, cyber security oriented research is paying much close attention on Vehicular Adhoc NETworks (VANETs). However, existing vehicular networks do not meet current security requirements. Typically for dynamic networks, maximal decentralization and rapidly changing topology of moving hosts form a number of security issues associated with ensuring access control of hosts, security policy enforcement, and resistance of the routing methods. To solve these problems generally, the paper reviews SDN (software defined networks) based network security architectures of VANET. The following tasks are solved in our work: composing of network security architectures for SDN-VANET (architecture with the central control and shared security servers, decentralized (zoned) architecture, hierarchical architecture); implementation of these architectures in virtual modeling environment; and experimental study of effectiveness of the suggested architectures. With large-scale vehicular networks, architectures with multiple SDN controllers are most effective. In small networks, the architecture with the central control also significantly outperforms the traditional VANET architecture. For the suggested architectures, three control modes are discussed in the paper: central, distributed and hybrid modes. Unlike common architectures, all of the proposed security architectures allow us to establish a security policy in m2m-networks and increase resistance capabilities of self-organizing networks.

The traditional block-based compressive sensing (BCS) approach considers the image to be segmented. However, there is not much literature available on how many numbers of blocks or segments per image would be the best choice for the compression and recovery methods. In this article, we propose a BCS method to find out the optimal way of image retrieval, and the number of the blocks to which into image should be divided. In the theoretical analysis, we analyzed the effect of noise under compression perspective and derived the range of error probability. Experimental results show that the number of blocks of an image has a strong correlation with the image recovery process. As the sampling rate M/N increases, we can find the appropriate number of image blocks by comparing each line.