Biblio

Fast, accurate three dimensional reconstructions of plasma equilibria, crucial for physics interpretation of fusion data generated within confinement devices like stellarators/ tokamaks, are computationally very expensive and routinely require days, even weeks, to complete using serial approaches. Here, we present a parallel implementation of the three dimensional plasma reconstruction code, V3FIT. A formal analysis to identify the performance bottlenecks and scalability limits of this new parallel implementation, which combines both task and data parallelism, is presented. The theoretical findings are supported by empirical performance results on several thousands of processor cores of a Cray XC30 supercomputer. Parallel V3FIT is shown to deliver over 40X speedup, enabling fusion scientists to carry out three dimensional plasma equilibrium reconstructions at unprecedented scales in only a few hours (instead of in days/weeks) for the first time.

Despite the continuous shrinking of the transistor dimensions, advanced modeling tools going beyond the ballistic limit of transport are still critically needed to ensure accurate device investigations. For that purpose we present here a straight-forward approach to include phonon confinement effects into dissipative quantum transport calculations based on the effective mass approximation (EMA) and the k·p method. The idea is to scale the magnitude of the deformation potentials describing the electron-phonon coupling to obtain the same low-field mobility as with full-band simulations and confined phonons. This technique is validated by demonstrating that after adjusting the mobility value of n- and p-type silicon nanowire transistors, the resulting EMA and k·p I-V characteristics agree well with those derived from full-band studies.

Certification of keys and attributes is in practice typically realized by a hierarchy of issuers. Revealing the full chain of issuers for certificate verification, however, can be a privacy issue since it can leak sensitive information about the issuer's organizational structure or about the certificate owner. Delegatable anonymous credentials solve this problem and allow one to hide the full delegation (issuance) chain, providing privacy during both delegation and presentation of certificates. However, the existing delegatable credentials schemes are not efficient enough for practical use. In this paper, we present the first hierarchical (or delegatable) anonymous credential system that is practical. To this end, we provide a surprisingly simple ideal functionality for delegatable credentials and present a generic construction that we prove secure in the UC model. We then give a concrete instantiation using a recent pairing-based signature scheme by Groth and describe a number of optimizations and efficiency improvements that can be made when implementing our concrete scheme. The latter might be of independent interest for other pairing-based schemes as well. Finally, we report on an implementation of our scheme in the context of transaction authentication for blockchain, and provide concrete performance figures.

This paper investigates closed-form expressions to evaluate the performance of the Compressive Sensing (CS) based Energy Detector (ED). The conventional way to approximate the probability density function of the ED test statistic invokes the central limit theorem and considers the decision variable as Gaussian. This approach, however, provides good approximation only if the number of samples is large enough. This is not usually the case in CS framework, where the goal is to keep the sample size low. Moreover, working with a reduced number of measurements is of practical interest for general spectrum sensing in cognitive radio applications, where the sensing time should be sufficiently short since any time spent for sensing cannot be used for data transmission on the detected idle channels. In this paper, we make use of low-complexity approximations based on algebraic transformations of the one-dimensional Gaussian Q-function. More precisely, this paper provides new closed-form expressions for accurate evaluation of the CS-based ED performance as a function of the compressive ratio and the Signal-to-Noise Ratio (SNR). Simulation results demonstrate the increased accuracy of the proposed equations compared to existing works.

The graphical pattern unlock scheme which requires users to connect a minimum of 4 nodes on 3X3 grid is one of the most popular authentication mechanism on mobile devices. However prior research suggests that users' pattern choices are highly biased and hence vulnerable to guessing attacks. Moreover, 3X3 pattern choices are devoid of features such as longer stroke lengths, direction changes and intersections that are considered to be important in preventing shoulder-surfing attacks. We attribute these insecure practices to the geometry of the grid and its complicated drawing rules which prevent users from realising the full potential of graphical passwords. In this paper, we propose and explore an alternate circular layout referred to as Pass-O which unlike grid layout allows connection between any two nodes, thus simplifying the pattern drawing rules. Consequently, Pass-O produces a theoretical search space of 9,85,824, almost 2.5 times greater than 3X3 grid layout. We compare the security of 3X3 and Pass-O patterns theoretically as well as empirically. Theoretically, Pass-O patterns are uniform and have greater visual complexity due to large number of intersections. To perform empirical analysis, we conduct a large-scale web-based user study and collect more than 1,23,000 patterns from 21,053 users. After examining user-chosen 3X3 and Pass-O patterns across different metrics such as pattern length, stroke length, start point, end point, repetitions, number of direction changes and intersections, we find that Pass-O patterns are much more secure than 3X3 patterns.

Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for antivirus systems. However, the suitability of clustering algorithms for security-sensitive settings has been recently questioned by showing that they can be significantly compromised if an attacker can exercise some control over the input data. In this paper, we revisit this problem by focusing on behavioral malware clustering approaches, and investigate whether and to what extent an attacker may be able to subvert these approaches through a careful injection of samples with poisoning behavior. To this end, we present a case study on Malheur, an open-source tool for behavioral malware clustering. Our experiments not only demonstrate that this tool is vulnerable to poisoning attacks, but also that it can be significantly compromised even if the attacker can only inject a very small percentage of attacks into the input data. As a remedy, we discuss possible countermeasures and highlight the need for more secure clustering algorithms.

Malware technology makes it difficult for malware analyst to detect same malware files with different obfuscation technique. In this paper we are trying to tackle that problem by analyzing the sequence of system call from an executable file. Malware files which actually are the same should have almost identical or at least a similar sequence of system calls. In this paper, we are going to create a model for each malware class consists of malwares from different families based on its sequence of system calls. Method/algorithm that's used in this paper is profile hidden markov model which is a very well-known tool in the biological informatics field for comparing DNA and protein sequences. Malware classes that we are going to build are trojan and worm class. Accuracy for these classes are pretty high, it's above 90% with also a high false positive rate around 37%.

Malicious applications have become increasingly numerous. This demands adaptive, learning-based techniques for constructing malware detection engines, instead of the traditional manual-based strategies. Prior work in learning-based malware detection engines primarily focuses on dynamic trace analysis and byte-level n-grams. Our approach in this paper differs in that we use compiler intermediate representations, i.e., the callgraph representation of binaries. Using graph-based program representations for learning provides structure of the program, which can be used to learn more advanced patterns. We use the Shortest Path Graph Kernel (SPGK) to identify similarities between call graphs extracted from binaries. The output similarity matrix is fed into a Support Vector Machine (SVM) algorithm to construct highly-accurate models to predict whether a binary is malicious or not. However, SPGK is computationally expensive due to the size of the input graphs. Therefore, we evaluate different parallelization methods for CPUs and GPUs to speed up this kernel, allowing us to continuously construct up-to-date models in a timely manner. Our hybrid implementation, which leverages both CPU and GPU, yields the best performance, achieving up to a 14.2x improvement over our already optimized OpenMP version. We compared our generated graph-based models to previously state-of-the-art feature vector 2-gram and 3-gram models on a dataset consisting of over 22,000 binaries. We show that our classification accuracy using graphs is over 19% higher than either n-gram model and gives a false positive rate (FPR) of less than 0.1%. We are also able to consider large call graphs and dataset sizes because of the reduced execution time of our parallelized SPGK implementation.

Security is the most important issue which needs to be given utmost importance and as both `Mobile Ad hoc Networks (MANET) and Wireless Sensor Networks (WSN) have similar system models, their security issues are also similar. This study deals in analysing the various lapses in security and the characteristics of various routing protocol's functionality and structure. This paper presents the implementation of ECC algorithm in the prevention of Denial of Service (DoS) attack through fictitious node. Optimized Link State Routing (OLSR) protocol is a MANET routing protocol and is evaluated mainly for two things. Primarily OLSR is less secure like AODV and others. The reason for it being less secure is that it is a table-driven in nature and uses a methodology called selective flooding technique, where redundancy is reduced and thus the security possibilities of the protocol is reduced. Another reason for selecting OLSR is that is an highly effective routing protocol for MANET. A brief information about formal routing is provided by the proposed methodology termed Denial Contradictions with Fictitious Node Mechanism (DCFM) which provides brief information about formal routing. Here, fictitious node acts as a virtual node and large networks are managed from attacks. More than 95% of attacks are prevented by this proposed methodology and the solution is applicable all the other DoS attacks of MANET.

Secure routing over VANET is a major issue due to its high mobility environment. Due to dynamic topology, routes are frequently updated and also suffers from link breaks due to the obstacles i.e. buildings, tunnels and bridges etc. Frequent link breaks can cause packet drop and thus result in degradation of network performance. In case of VANETs, it becomes very difficult to identify the reason of the packet drop as it can also occur due to the presence of a security threat. VANET is a type of wireless adhoc network and suffer from common attacks which exist for mobile adhoc network (MANET) i.e. Denial of Services (DoS), Black hole, Gray hole and Sybil attack etc. Researchers have already developed various security mechanisms for secure routing over MANET but these solutions are not fully compatible with unique attributes of VANET i.e. vehicles can communicate with each other (V2V) as well as communication can be initiated with infrastructure based network (V2I). In order to secure the routing for both types of communication, there is need to develop a solution. In this paper, a method for secure routing is introduced which can identify as well as eliminate the existing security threat.

Routing security has a great importance to the security of Mobile Ad Hoc Networks (MANETs). There are various kinds of attacks when establishing routing path between source and destination. The adversaries attempt to deceive the source node and get the privilege of data transmission. Then they try to launch the malicious behaviors such as passive or active attacks. Due to the characteristics of the MANETs, e.g. dynamic topology, open medium, distributed cooperation, and constrained capability, it is difficult to verify the behavior of nodes and detect malicious nodes without revealing any privacy. In this paper, we present PVad, an approach conducting privacy-preserving verification in the routing discovery phase of MANETs. PVad tries to find the existing communication rules by association rules instead of making the rules. PVad consists of two phases, a reasoning phase deducing the expected log data of the peers, and a verification phase using Merkle Hash Tree to verify the correctness of derived information without revealing any privacy of nodes on expected routing paths. Without deploying any special nodes to assist the verification, PVad can detect multiple malicious nodes by itself. To show our approach can be used to guarantee the security of the MANETs, we conduct our experiments in NS3 as well as the real router environment, and we improved the detection accuracy by 4% on average compared to our former work.

The underlying element that supports the device communication in the MANET is the wireless connection capability. Each node has the ability to communicate with other nodes via the creation of routing path. However, due to the fact that nodes in MANET are autonomous and the routing paths created are only based on current condition of the network, some of the paths are extremely instable. In light of these shortcomings, many research works emphasizes on the improvement of routing path algorithm. Regardless of the application the MANET can support, the MANET possesses unique characteristics, which enables mobile nodes to form dynamic communication irrespective the availability of a fixed network. However the inherent nature of MANET has led to nodes in MANET to be vulnerable to denied services. A typical Denial of Service (DoS) in MANET is the Black Hole attack, caused by a malicious node, or a set of nodes advertising false routing updates. Typically, the malicious nodes are difficult to be detected. Each node is equipped with a particular type of routing protocol and voluntarily participates in relaying the packets. However, some nodes may not be genuine and has been tampered to behave maliciously, which causes the Black Hole attack. Several on demand routing protocol e.g. Ad hoc On Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) are susceptible to such attack. In principle, the attack exploits the Route Request (RREQ) discovery operation and falsifies the sequence number and the shortest path information. The malicious nodes are able to utilize the loophole in the RREQ discovery process due to the absence of validation process. As a result, genuine RREQ packets are exploited and erroneously relayed to a false node(s). This paper highlights the effect Black Hole nodes to the network performance and therefore substantiates the previous work done [1]. In this paper, several simulation experiments are iterated using NS-2, which employed various scenarios and traffic loads. The simulation results show the presence of Black Hole nodes in a network can substantially affects the packet delivery ratio and throughput by as much as 100%.

Since MANETs are infrastructure-less, they heavily use secret sharing techniques to distribute and decentralize the role of a trusted third party, where the MANET secret s is shared among the legitimate nodes using (t, n) threshold secret sharing scheme. For long lived MANETs, the shared secret is periodically updated without changing the MANET secret based on proactive secret sharing using Elliptic Curve Cryptography(ECC). Hence, the adversary trying to learn the secret, needs to gain at-least t partial shares in the same time period. If the time period and the threshold value t are selected properly, proactive verifiable secret sharing can maintain the overall security of the information in long lived MANETs. The conventional cryptographic algorithms are heavy weight, require lot of computation power thus consuming lot of resources. In our proposal we used Elliptic Curve Cryptography to verify commitments as it requires smaller keys compared to existing proactive secret sharing techniques and makes it useful for MANETs, Which are formed of resource constraint devices.

Untrusted third-party vendors and manufacturers have raised security concerns in hardware supply chain. Among all existing solutions, formal verification methods provide powerful solutions in detection malicious behaviors at the pre-silicon stage. However, little work have been done towards built-in hardware runtime verification at the post-silicon stage. In this paper, a runtime formal verification framework is proposed to evaluate the trust of hardware during its execution. This framework combines the symbolic execution and SAT solving methods to validate the user defined properties. The proposed framework has been demonstrated on an FPGA platform using an SoC design with untrusted IPs. The experimentation results show that the proposed approach can provide high-level security assurance for hardware at runtime.

Mobile cloud computing paradigm enables cloud servers to extend the limited hardware resources of mobile devices improving availability and reliability of the services provided. Consequently, private, financial, business and critical data pass through wireless access media exposed to malicious attacks. Mobile cloud infrastructure requires new security mechanisms, at the same time as offloading operations need to maintain the advantages of saving processing and energy of the device. Thus, this paper implements a middleware-based computational offloading with cryptographic algorithms and evaluates two mechanisms (symmetric and asymmetric), to provide the integrity and authenticity of data that a smartphone offloads to mobile cloud servers. Also, the paper discusses the factors that impact on power consumption and performance on smartphones that's run resource-intensive applications.

Approximate nearest neighbor (ANN) search in high-dimensional space plays an essential role in many multimedia applications. Recently, product quantization (PQ) based methods for ANN search have attracted enormous attention in the community of computer vision, due to its good balance between accuracy and space requirement. PQ based methods embed a high-dimensional vector into a short binary code (called PQ code), and the squared Euclidean distance is estimated by asymmetric quantizer distance (AQD) with pretty high precision. Thus, ANN search in the original space can be converted to similarity search on AQD using the PQ approach. All existing PQ methods are in-memory solutions, which may not handle massive data if they cannot fit entirely in memory. In this paper, we propose an I/O-efficient PQ based solution for ANN search. We design an index called PQB+-forest to support efficient similarity search on AQD. PQB+-forest first creates a number of partitions of the PQ codes by a coarse quantizer and then builds a B+-tree, called PQB+-tree, for each partition. The search process is greatly expedited by focusing on a few selected partitions that are closest to the query, as well as by the pruning power of PQB+-trees. According to the experiments conducted on two large-scale data sets containing up to 1 billion vectors, our method outperforms its competitors, including the state-of-the-art PQ method and the state-of-the-art LSH methods for ANN search.

In vehicular networks, each message is signed by the generating node to ensure accountability for the contents of that message. For privacy reasons, each vehicle uses a collection of certificates, which for accountability reasons are linked at a central authority. One such design is the Security Credential Management System (SCMS) [1], which is the leading credential management system in the US. The SCMS is composed of multiple components, each of which has a different task for key management, which are logically separated. The SCMS is designed to ensure privacy against a single insider compromise, or against outside adversaries. In this paper, we demonstrate that the current SCMS design fails to achieve its design goal, showing that a compromised authority can gain substantial information about certificate linkages. We propose a solution that accommodates threshold-based detection, but uses relabeling and noise to limit the information that can be learned from a single insider adversary. We also analyze our solution using techniques from differential privacy and validate it using traffic-simulator based experiments. Our results show that our proposed solution prevents privacy information leakage against the compromised authority in collusion with outsider attackers.

With the advancement of sensor electronic devices, wireless sensor networks have attracted more and more attention. Range query has become a significant part of sensor networks due to its availability and convenience. However, It is challenging to process range query while still protecting sensitive data from disclosure. Existing work mainly focuses on privacy- preserving range query, but neglects the damage of collusion attacks, probability attacks and differential attacks. In this paper, we propose a privacy- preserving, energy-efficient and multi-dimensional range query protocol called PERQ, which not only achieves data privacy, but also considers collusion attacks, probability attacks and differential attacks. Generalized distance-based and modular arithmetic range query mechanism are used. In addition, a novel cyclic modular verification scheme is proposed to verify the data integrity. Extensive theoretical analysis and experimental results confirm the high performance of PERQ in terms of energy efficiency, security and accountability requirements.

Named Data Networking (NDN) is a new network architecture design that led to the evolution of a network architecture based on data-centric. Questions have been raised about how to compare its performance with the old architecture such as IP network which is generally based on Internet Protocol version 4 (IPv4). Differs with the old one, source and destination addresses in the delivery of data are not required on the NDN network because the addresses function is replaced by a data name (Name) which serves to identify the data uniquely. In a computer network, a network routing is an essential factor to support data communication. The network routing on IP network relies only on Routing Information Base (RIB) derived from the IP table on the router. So that, if there is a problem on the network such as there is one node exposed to a dangerous attack, the IP router should wait until the IP table is updated, and then the routing channel is changed. The issue of how to change the routing path without updating IP table has received considerable critical attention. The NDN network has an advantage such as its capability to execute an adaptive forwarding mechanism, which FIB (Forwarding Information Base) of the NDN router keeps information for routing and forwarding planes. Therefore, if there is a problem on the network, the NDN router can detect the problem more quickly than the IP router. The contribution of this study is important to explain the benefit of the forwarding mechanism of the NDN network compared to the IP network forwarding mechanism when there is a node which is suffered a hijack attack.

Due to the proliferation of reprogrammable hardware, core designs built from modules drawn from a variety of sources execute with direct access to critical system resources. Expressing guarantees that such modules satisfy, in particular the dynamic conditions under which they release information about their unbounded streams of inputs, and automatically proving that they satisfy such guarantees, is an open and critical problem.,,To address these challenges, we propose a domain-specific language, named STREAMS, for expressing information-flow policies with declassification over unbounded input streams. We also introduce a novel algorithm, named SIMAREL, that given a core design C and STREAMS policy P, automatically proves or falsifies that C satisfies P. The key technical insight behind the design of SIMAREL is a novel algorithm for efficiently synthesizing relational invariants over pairs of circuit executions.,,We expressed expected behavior of cores designed independently for research and production as STREAMS policies and used SIMAREL to check if each core satisfies its policy. SIMAREL proved that half of the cores satisfied expected behavior, but found unexpected information leaks in six open-source designs: an Ethernet controller, a flash memory controller, an SD-card storage manager, a robotics controller, a digital-signal processing (DSP) module, and a debugging interface.

Persistent spread measurement is to count the number of distinct elements that persist in each network flow for predefined time periods. It has many practical applications, including detecting long-term stealthy network activities in the background of normal-user activities, such as stealthy DDoS attack, stealthy network scan, or faked network trend, which cannot be detected by traditional flow cardinality measurement. With big network data, one challenge is to measure the persistent spreads of a massive number of flows without incurring too much memory overhead as such measurement may be performed at the line speed by network processors with fast but small on-chip memory. We propose a highly compact Virtual Intersection HyperLogLog (VI-HLL) architecture for this purpose. It achieves far better memory efficiency than the best prior work of V-Bitmap, and in the meantime drastically extends the measurement range. Theoretical analysis and extensive experiments demonstrate that VI-HLL provides good measurement accuracy even in very tight memory space of less than 1 bit per flow.

Packet leak on network-on-chip (NoC) is one of the key security concerns in the MPSoC design, where the NoC of the system can come from a third-party vendor and can be illegitimately implanted with hardware trojans. Those trojans are usually small so that they can escape the scrutiny of circuit level testing and perform attacks when activated. This paper targets the trojan that leaks packets to malicious applications by altering the packet source and destination addresses. To detect such a packet leak, we present a cost effective authentication design where the packet source and destination addresses are tagged with a dynamic random value and the tag is scrambled with the packet data. Our design has two features: 1) If the adversary attempts to play with tag to escape detection, the data in the packet may likely be changed – hence invalidating the leaked packet; 2) If the attacker only alters the packet addresses without twiddling tag in the packet, the attack will be100% detected.

This paper aims to address the security challenges on physical unclonable functions (PUFs) raised by modeling attacks and denial of service (DoS) attacks. We develop a hardware isolation-based secure architecture extension, namely PUFSec, to protect the target PUF from security compromises without modifying the internal PUF design. PUFSec achieves the security protection by physically isolating the PUF hardware and data from the attack surfaces accessible by the adversaries. Furthermore, we deploy strictly enforced security policies within PUFSec, which authenticate the incoming PUF challenges and prevent attackers from collecting sufficient PUF responses to issue modeling attacks or interfering with the PUF workflow to launch DoS attacks. We implement our PUFSec framework on a Xilinx SoC equipped with ARM processor. Our experimental results on the real hardware prove the enhanced security and the low performance and power overhead brought by PUFSec.

Probabilistic model checking is a formal verification technique that has been applied successfully in a variety of domains, providing identification of system errors through quantitative verification of stochastic system models. One domain that can benefit from probabilistic model checking is cloud computing, which must provide highly reliable and secure computational and storage services to large numbers of mission-critical software systems. For real-world domains like cloud computing, external system factors and environmental changes must be estimated accurately in the form of probabilities in system models; inaccurate estimates for the model probabilities can lead to invalid verification results. To address the effects of uncertainty in probability estimates, in previous work we have developed a variety of techniques for perturbation analysis of discrete- and continuous-time Markov chains (DTMCs and CTMCs). These techniques determine the consequences of the uncertainty on verification of system properties. In this paper, we present the first approach for perturbation analysis of Markov decision processes (MDPs), a stochastic formalism that is especially popular due to the significant expressive power it provides through the combination of both probabilistic and nondeterministic choice. Our primary contribution is a novel technique for efficiently analyzing the effects of perturbations of model probabilities on verification of reachability properties of MDPs. The technique heuristically explores the space of adversaries of an MDP, which encode the different ways of resolving the MDP’s nondeterministic choices. We demonstrate the practical effectiveness of our approach by applying it to two case studies of cloud systems.

Channel state information (CSI) has been recently shown to be useful in performing security attacks in public WiFi environments. By analyzing how CSI is affected by the finger motions, CSI-based attacks can effectively reconstruct text-based passwords and locking patterns. This paper presents WiGuard, a novel system to protect sensitive on-screen gestures in a public place. Our approach carefully exploits the WiFi channel interference to introduce noise into the attacker's CSI measurement to reduce the success rate of the attack. Our approach automatically detects when a CSI-based attack happens. We evaluate our approach by applying it to protect text-based passwords and pattern locks on mobile devices. Experimental results show that our approach is able to reduce the success rate of CSI attacks from 92% to 42% for text-based passwords and from 82% to 22% for pattern lock.