Biblio

This paper reviews the challenges faced when securing data on mobile devices. After a discussion of the state-of-the-art of secure storage for iOS and Android, the paper introduces an attack which demonstrates how Full Disk Encryption (FDE) on Android can be ineffective in practice.

The capability to reliably and accurately identify the attacker has long been believed as one of the most effective deterrents to an attack. Ideally, the attribution of cyber attack should be automated from the attack target all the way toward the attack source on the Internet in real-time. Real-time, network-wide attack attribution, however, is every challenging, and many people have doubted whether it is feasible to have practical attack attribution on the Internet. In this paper, we look into the problem, challenges of real-time attack attribution on the Internet, and analyze what it takes to have the real-time attack attribution on the Internet. We show that it is indeed feasible and practical to attribute certain cyber attacks on the Internet in real-time. We build such a real-time attack attribution system upon the malware immunization and packet flow watermarking techniques we have developed. We demonstrate the unprecedented real-time attack attribution capability via live experiments on the Internet and Tor nodes all over the world.
 

The capability to reliably and accurately identify the attacker has long been believed as one of the most effective deterrents to an attack. Ideally, the attribution of cyber attack should be automated from the attack target all the way toward the attack source on the Internet in real-time. Real-time, network-wide attack attribution, however, is every challenging, and many people have doubted whether it is feasible to have practical attack attribution on the Internet. In this paper, we look into the problem, challenges of real-time attack attribution on the Internet, and analyze what it takes to have the real-time attack attribution on the Internet. We show that it is indeed feasible and practical to attribute certain cyber attacks on the Internet in real-time. We build such a real-time attack attribution system upon the malware immunization and packet flow watermarking techniques we have developed. We demonstrate the unprecedented real-time attack attribution capability via live experiments on the Internet and Tor nodes all over the world.

The speech emotion recognition accuracy of prosody feature and voice quality feature declines with the decrease of SNR (Signal to Noise Ratio) of speech signals. In this paper, we propose novel sub-band spectral centroid weighted wavelet packet cepstral coefficients (W-WPCC) for robust speech emotion recognition. The W-WPCC feature is computed by combining the sub-band energies with sub-band spectral centroids via a weighting scheme to generate noise-robust acoustic features. And Deep Belief Networks (DBNs) are artificial neural networks having more than one hidden layer, which are first pre-trained layer by layer and then fine-tuned using back propagation algorithm. The well-trained deep neural networks are capable of modeling complex and non-linear features of input training data and can better predict the probability distribution over classification labels. We extracted prosody feature, voice quality features and wavelet packet cepstral coefficients (WPCC) from the speech signals to combine with W-WPCC and fused them by Deep Belief Networks (DBNs). Experimental results on Berlin emotional speech database show that the proposed fused feature with W-WPCC is more suitable in speech emotion recognition under noisy conditions than other acoustics features and proposed DBNs feature learning structure combined with W-WPCC improve emotion recognition performance over the conventional emotion recognition method.

Malware detection increasingly relies on machine learning techniques, which utilize multiple features to separate the malware from the benign apps. The effectiveness of these techniques primarily depends on the manual feature engineering process, based on human knowledge and intuition. However, given the adversaries' efforts to evade detection and the growing volume of publications on malware behaviors, the feature engineering process likely draws from a fraction of the relevant knowledge. We propose an end-to-end approach for automatic feature engineering. We describe techniques for mining documents written in natural language (e.g. scientific papers) and for representing and querying the knowledge about malware in a way that mirrors the human feature engineering process. Specifically, we first identify abstract behaviors that are associated with malware, and then we map these behaviors to concrete features that can be tested experimentally. We implement these ideas in a system called FeatureSmith, which generates a feature set for detecting Android malware. We train a classifier using these features on a large data set of benign and malicious apps. This classifier achieves a 92.5% true positive rate with only 1% false positives, which is comparable to the performance of a state-of-the-art Android malware detector that relies on manually engineered features. In addition, FeatureSmith is able to suggest informative features that are absent from the manually engineered set and to link the features generated to abstract concepts that describe malware behaviors.

For single-owner multi-user wireless sensor networks, there is the demand to implement the user privacy-preserving access control protocol in WSNs. Firstly, we propose a new access control protocol based on an efficient attribute-based signature. In the protocol, users need to pay for query, and the protocol achieves fine-grained access control and privacy protection. Then, the protocol is analyzed in detail. Finally, the comparison of protocols indicates that our scheme is more efficient. Our scheme not only protects the privacy of users and achieves fine-grained access control, but also provides the query command validation with low overhead. The scheme can better satisfy the access control requirements of wireless sensor networks.

Mobile code distribution relies on digital signatures to guarantee code authenticity. Unfortunately, standard signature schemes are not well suited for use in conjunction with program transformation techniques, such as aspect-oriented programming. With these techniques, code development is performed in sequence by multiple teams of programmers. This is fundamentally different from traditional single-developer/ single-user models, where users can verify end-to-end (i.e., developer-to-user) authenticity of the code using digital signatures. To address this limitation, we introduce FLEX, a flexible code authentication framework for mobile applications. FLEX allows semi-trusted intermediaries to modify mobile code without invalidating the developer's signature, as long as the modification complies with a "contract" issued by the developer. We introduce formal definitions for secure code modification, and show that our instantiation of FLEX is secure under these definitions. Although FLEX can be instantiated using any language, we design AMJ–a novel programming language that supports code annotations–and implement a FLEX prototype based on our new language.

Public-key cryptography schemes are widely used due to their high level of security. As a very efficient one among public-key cryptosystems, elliptic curve cryptography (ECC) has been studied for years. Researchers used to improve the efficiency of ECC through point multiplication, which is the most important and complex operation of ECC. In our research, we use special families of curves and prime fields which have special properties. After that, we introduce the instruction set architecture (ISA) extension method to accelerate this algorithm (192-bit private key) and build an ECC\_ASIP model with six new ECC custom instructions. Finally, the ECC\_ASIP model is implemented in a field-programmable gate array (FPGA) platform. The persuasive experiments have been conducted to evaluate the performance of our new model in the aspects of the performance, the code storage space and hardware resources. Experimental results show that our processor improves 69.6% in the execution efficiency and requires only 6.2% more hardware resources.

Non-Technical Loss (NTL) fraud is a very common fraud in power systems. In traditional power grid, energy theft, via meter tampering, is the main form of NTL fraud. With the rise of Smart Grid, adversaries can take advantage of two-way communication to commit NTL frauds by meter manipulation or network intrusion. Previous schemes were proposed to detect NTL frauds but are not efficient. In this paper, we propose a Fast NTL Fraud Detection and verification scheme (FNFD). FNFD is based on Recursive Least Square (RLS) to model adversary behavior. Experimental results show that FNFD outperforms existing schemes in terms of efficiency and overhead.

We address the known problem of detecting a previous compression in JPEG images, focusing on the challenging case of high and very high quality factors (textgreater= 90) as well as repeated compression with identical or nearly identical quality factors. We first revisit the approaches based on Benford–Fourier analysis in the DCT domain and block convergence analysis in the spatial domain. Both were originally conceived for specific scenarios. Leveraging decision tree theory, we design a combined approach complementing the discriminatory capabilities. We obtain a set of novel detectors targeted to high quality grayscale JPEG images.

Mobile devices store a diverse set of private user data and have gradually become a hub to control users' other personal Internet-of-Things devices. Access control on mobile devices is therefore highly important. The widely accepted solution is to protect access by asking for a password. However, password authentication is tedious, e.g., a user needs to input a password every time she wants to use the device. Moreover, existing biometrics such as face, fingerprint, and touch behaviors are vulnerable to forgery attacks. We propose a new touch-based biometric authentication system that is passive and secure against forgery attacks. In our touch-based authentication, a user's touch behaviors are a function of some random "secret". The user can subconsciously know the secret while touching the device's screen. However, an attacker cannot know the secret at the time of attack, which makes it challenging to perform forgery attacks even if the attacker has already obtained the user's touch behaviors. We evaluate our touch-based authentication system by collecting data from 25 subjects. Results are promising: the random secrets do not influence user experience and, for targeted forgery attacks, our system achieves 0.18 smaller Equal Error Rates (EERs) than previous touch-based authentication.

Mobile devices store a diverse set of private user data and have gradually become a hub to control users' other personal Internet-of-Things devices. Access control on mobile devices is therefore highly important. The widely accepted solution is to protect access by asking for a password. However, password authentication is tedious, e.g., a user needs to input a password every time she wants to use the device. Moreover, existing biometrics such as face, fingerprint, and touch behaviors are vulnerable to forgery attacks. We propose a new touch-based biometric authentication system that is passive and secure against forgery attacks. In our touch-based authentication, a user's touch behaviors are a function of some random "secret". The user can subconsciously know the secret while touching the device's screen. However, an attacker cannot know the secret at the time of attack, which makes it challenging to perform forgery attacks even if the attacker has already obtained the user's touch behaviors. We evaluate our touch-based authentication system by collecting data from 25 subjects. Results are promising: the random secrets do not influence user experience and, for targeted forgery attacks, our system achieves 0.18 smaller Equal Error Rates (EERs) than previous touch-based authentication.

This work is motivated by the rapid increase of the number of attacks in computer networks and software engineering. In this paper we study identity snowball attacks and formally prove the correctness of suggested solutions to this type of attack (solutions that are based on the graph reachability reduction) using a proof assistant. We propose a model of an attack graph that captures technical informations about the calculation of reachability of the graph. The model has been implemented with the proof assistant PVS 6.0 (Prototype Verification System). It makes it possible to prove algorithms of reachability reduction such as Sparsest\_cut.

A new paradigm in wireless network access is presented and analyzed. In this concept, certain classes of wireless terminals can be turned temporarily into an access point (AP) anytime while connected to the Internet. This creates a dynamic network architecture (DNA) since the number and location of these APs vary in time. In this paper, we present a framework to optimize different aspects of this architecture. First, the dynamic AP association problem is addressed with the aim to optimize the network by choosing the most convenient APs to provide the quality-of-service (QoS) levels demanded by the users with the minimum cost. Then, an economic model is developed to compensate the users for serving as APs and, thus, augmenting the network resources. The users' security investment is also taken into account in the AP selection. A preclustering process of the DNA is proposed to keep the optimization process feasible in a high dense network. To dynamically reconfigure the optimum topology and adjust it to the traffic variations, a new specific encoding of genetic algorithm (GA) is presented. Numerical results show that GA can provide the optimum topology up to two orders of magnitude faster than exhaustive search for network clusters, and the improvement significantly increases with the cluster size.

Cloud has gained a wide acceptance across the globe. Despite wide acceptance and adoption of cloud computing, certain apprehensions and diffidence, related to safety and security of data still exists. The service provider needs to convince and demonstrate to the client, the confidentiality of data on the cloud. This can be broadly translated to issues related to the process of identifying, developing, maintaining and optimizing trust with clients regarding the services provided. Continuous demonstration, maintenance and optimization of trust of the agreed upon services affects the relationship with a client. The paper proposes a framework of integration of trust at the IAAS level in the cloud. It proposes a novel method of generation of trust index factor, considering the performance and the agility of the feedback received using fuzzy logic.

More and more current industrial control systems (e.g, smart grids, oil and gas systems, connected cars and trucks) have the capability to collect and transmit users' data in order to provide services that are tailored to the specific needs of the customers. Such smart industrial control systems fall into the category of Internet of Things (IoT). However, in many cases, the data transmitted by such IoT devices includes sensitive information and users are faced with an all-or-nothing choice: either they adopt the proposed services and release their private data, or refrain from using services which could be beneficial but pose significant privacy risks. Unfortunately, encryption alone does not solve the problem, though techniques to counter these privacy risks are emerging (e.g., by using applications that alter, merge or bundle data to ensure they cannot be linked to a particular user). In this paper, we propose a general framework, whereby users can not only specify how their data is managed, but also restrict data collection from their connected devices. More precisely, we propose to use data collection policies to govern the transmission of data from IoT devices, coupled with policies to ensure that once the data has been transmitted, it is stored and shared in a secure way. To achieve this goal, we have designed a framework for secure data collection, storage and management, with logical foundations that enable verification of policy properties.

We propose a modular framework which deploys state-of-the art techniques in dynamic pattern matching as well as machine learning algorithms for Big Data predictive and be-havioural analytics to detect threats and attacks in Managed File Transfer and collaboration platforms. We leverage the use of the kill chain model by looking for indicators of compromise either for long-term attacks as Advanced Persistent Threats, zero-day attacks or DDoS attacks. The proposed engine can act complimentary to existing security services as SIEMs, IDS, IPS and firewalls.

Botnets have become one of the most significant cyber threats over the last decade. The diffusion of the "Internet of Things" and its for-profit exploitation, contributed to botnets spread and sophistication, thus providing real, efficient and profitable criminal cyber-services. Recent research on botnet detection focuses on traffic pattern-based detection, and on analyzing the network traffic generated by the infected hosts, in order to find behavioral patterns independent from the specific payloads, architectures and protocols. In this paper we address the periodic behavioral patterns of infected hosts communicating with their Command-and-Control servers. The main novelty introduced is related to the traffic analysis in the frequency domain without using the well-known Fast Fourier Transform. Moreover, the mentioned analysis is performed through the exploitation of the proxy logs, easily deployable on almost every real-world scenario, from enterprise networks to mobile devices.

As the use of social media technologies proliferates in organizations, it is important to understand the nefarious behaviors, such as cyberbullying, that may accompany such technology use and how to discourage these behaviors. We draw from neutralization theory and the criminological theory of general deterrence to develop and empirically test a research model to explain why cyberbullying may occur and how the behavior may be discouraged. We created a research model of three second-order formative constructs to examine their predictive influence on intentions to cyberbully. We used PLS- SEM to analyze the responses of 174 Facebook users in two different cyberbullying scenarios. Our model suggests that neutralization techniques enable cyberbullying behavior and while sanction certainty is an important deterrent, sanction severity appears ineffective. We discuss the theoretical and practical implications of our model and results.

Lattice-based cryptography offers some of the most attractive primitives believed to be resistant to quantum computers. Following increasing interest from both companies and government agencies in building quantum computers, a number of works have proposed instantiations of practical post-quantum key exchange protocols based on hard problems in ideal lattices, mainly based on the Ring Learning With Errors (R-LWE) problem. While ideal lattices facilitate major efficiency and storage benefits over their non-ideal counterparts, the additional ring structure that enables these advantages also raises concerns about the assumed difficulty of the underlying problems. Thus, a question of significant interest to cryptographers, and especially to those currently placing bets on primitives that will withstand quantum adversaries, is how much of an advantage the additional ring structure actually gives in practice. Despite conventional wisdom that generic lattices might be too slow and unwieldy, we demonstrate that LWE-based key exchange is quite practical: our constant time implementation requires around 1.3ms computation time for each party; compared to the recent NewHope R-LWE scheme, communication sizes increase by a factor of 4.7x, but remain under 12 KiB in each direction. Our protocol is competitive when used for serving web pages over TLS; when partnered with ECDSA signatures, latencies increase by less than a factor of 1.6x, and (even under heavy load) server throughput only decreases by factors of 1.5x and 1.2x when serving typical 1 KiB and 100 KiB pages, respectively. To achieve these practical results, our protocol takes advantage of several innovations. These include techniques to optimize communication bandwidth, dynamic generation of public parameters (which also offers additional security against backdoors), carefully chosen error distributions, and tight security parameters.

We consider the problem of translating a deterministic \textbackslashemph\simulation model\ (like Matlab-Simunk, Modelica or Ptolemy models) into a \textbackslashemphěrification model\ expressed by a network of hybrid automata. The goal is to verify safety using reachability analysis on the verification model. Simulation models typically use transitions with urgent semantics, which must be taken as soon as possible. Urgent transitions also make it possible to decompose systems that would otherwise need to be modeled with a monolithic hybrid automaton. In this paper, we include urgent transitions in our verification models and propose a suitable adaptation of our reachability algorithm. However, the simulation model, due to its imperfections, may be unsafe even though the corresponding hybrid automata are safe. Conversely, set-based reachability may not be able to show safety of an ideal formal model, since complex dynamics necessarily entail overapproximations. Taken as a whole, the formal modeling and verification process can both falsely claim safety and fail to show safety of the concrete system. We address this inconsistency by relaxing the model as follows. The standard semantics of hybrid automata is a mathematical idealization, where reactions are considered to be instantaneous and physical measurements infinitely precise. We propose semantics that relax these assumptions, where guard conditions are sampled in discrete time and admit measurement errors. The relaxed semantics can be translated to an equivalent relaxed model in standard semantics. The relaxed model is realistic in the sense that it can be implemented on hardware fast and precise enough, and in a way that safety is preserved. Finally, we show that overapproximative reachability analysis can show safety of relaxed models, which is not the case in general.

A compiler is fully-abstract if the compilation from source language programs to target language programs reflects and preserves behavioural equivalence. Such compilers have important security benefits, as they limit the power of an attacker interacting with the program in the target language to that of an attacker interacting with the program in the source language. Proving compiler full-abstraction is, however, rather complicated. A common proof technique is based on the back-translation of target-level program contexts to behaviourally-equivalent source-level contexts. However, constructing such a back-translation is problematic when the source language is not strong enough to embed an encoding of the target language. For instance, when compiling from the simply-typed λ-calculus (λτ) to the untyped λ-calculus (λu), the lack of recursive types in λτ prevents such a back-translation. We propose a general and elegant solution for this problem. The key insight is that it suffices to construct an approximate back-translation. The approximation is only accurate up to a certain number of steps and conservative beyond that, in the sense that the context generated by the back-translation may diverge when the original would not, but not vice versa. Based on this insight, we describe a general technique for proving compiler full-abstraction and demonstrate it on a compiler from λτ to λu . The proof uses asymmetric cross-language logical relations and makes innovative use of step-indexing to express the relation between a context and its approximate back-translation. We believe this proof technique can scale to challenging settings and enable simpler, more scalable proofs of compiler full-abstraction.

Availability is one of the most important requirements in the production system. Keeping the level of high availability in Infrastructure-as-a-Service (IaaS) cloud computing is a challenge task because of the complexity of service providing. By definition, the availability can be maintain by using fault tolerance approaches. Recently, many fault tolerance methods have been developed, but few of them focus on the fault detection aspect. In this paper, after a rigorous analysis on the nature of failures, we would like to introduce a technique to identified the failures occurring in IaaS system. By using fuzzy logic algorithm, this proposed technique can provide better performance in terms of accuracy and detection speed, which is critical for the cloud system.

In this paper, we extend the Maximum Satisfiability (MaxSAT) problem to Łukasiewicz logic. The MaxSAT problem for a set of formulae Φ is the problem of finding an assignment to the variables in Φ that satisfies the maximum number of formulae. Three possible solutions (encodings) are proposed to the new problem: (1) Disjunctive Linear Relations (DLRs), (2)Mixed Integer Linear Programming (MILP) and (3)Weighted Constraint Satisfaction Problem (WCSP). Like its Boolean counterpart, the extended fuzzy MaxSAT will have numerous applications in optimization problems that involve vagueness.

Wireless sensor networks (WSN) are useful in many practical applications including agriculture, military and health care systems. However, the nodes in a sensor network are constrained by energy and hence the lifespan of such sensor nodes are limited due to the energy problem. Temporal logics provide a facility to predict the lifetime of sensor nodes in a WSN using the past and present traffic and environmental conditions. Moreover, fuzzy logic helps to perform inference under uncertainty. When fuzzy logic is combined with temporal constraints, it increases the accuracy of decision making with qualitative information. Hence, a new data collection and cluster based energy efficient routing algorithm is proposed in this paper by extending the existing LEACH protocol. Extensions are provided in this work by including fuzzy temporal rules for making data collection and routing decisions. Moreover, this proposed work uses fuzzy temporal logic for forming clusters and to perform cluster based routing. The main difference between other cluster based routing protocols and the proposed protocol is that two types of cluster heads are used here, one for data collection and other for routing. In this research work we conducted an experiment and it is observed that the proposed fuzzy cluster based routing algorithm with temporal constrains enhances the network life time reduces the energy consumption and enhances the quality of service by increasing the packet delivery ratio by reducing the delay.