Biblio

Leading steganography systems make use of the Syndrome-Trellis Code (STC) algorithm to minimize a distortion function while encoding the desired payload, but this constrains the distortion function to be additive. The Gibbs Embedding algorithm works for a certain class of non-additive distortion functions, but has its own limitations and is highly complex. In this short paper we show that it is possible to modify the STC algorithm in a simple way, to minimize a non-additive distortion function suboptimally. We use it for two examples. First, applying it to the S-UNIWARD distortion function, we show that it does indeed reduce distortion, compared with minimizing the additive approximation currently used in image steganography, but that it makes the payload more – not less – detectable. This parallels research attempting to use Gibbs Embedding for the same task. Second, we apply it to distortion defined by the output of a specific detector, as a counter-move in the steganography game. However, unless the Warden is forced to move first (by fixing the detector) this is highly detectable.

This paper presents an assessment of continuous verification using linguistic style as a cognitive biometric. In stylometry, it is widely known that linguistic style is highly characteristic of authorship using representations that capture authorial style at character, lexical, syntactic, and semantic levels. In this work, we provide a contrast to previous efforts by implementing a one-class classification problem using Isolation Forests. Our approach demonstrates the usefulness of this classifier for accurately verifying the genuine user, and yields recognition accuracy exceeding 98% using very small training samples of 50 and 100-character blocks.

This work introduces a novel adaptation framework to energy-efficiently adapt small-sized circuits operating under scarce resources in dynamic environments, as autonomous swarm of sensory agents. This framework makes it possible to optimally configure the circuit based on three key mechanisms: (a) an off-line optimization phase relying on R2 indicator based Evolutionary Multi-objective Optimization Algorithm (EMOA), (b) an on-line phase based on hardware instincts and (c) the possibility to include the environment in the optimization loop. Specifically, the evolutionary algorithm is able to simultaneously determine an optimal combination of static settings and dynamic instinct for the hardware, considering highly dynamic environments. The instinct is then run on-line with minimal on-chip resources so that the circuit efficiently react to environmental changes. This framework is demonstrated on an ultrasonic communication system between energy-scarce wireless nodes. The proposed approach is environment-adaptive and enables power savings up to 45% for the same performance on the considered case studies.

Industrial Wireless Sensor Networks (IWSNs) are an extension of the Internet of Things paradigm that integrates smart sensors in industrial processes. However, the unattended open environment makes IWSNs vulnerable to malicious attacks, such as node compromise in addition to eavesdropping. The compromised nodes can again launch notorious attacks such as the sinkhole or sybil attack which may degrade the network performance. In this paper, we propose a lightweight, Secure Directed Diffusion (SDD) protocol. The algorithm for the proposed protocol uses bilinear pairing to derive a location-based key (LK) by binding the ID and geographic location of a node, thereby ensuring neighborhood authentication. Thus, authenticated nodes can prevent eavesdropping, node compromise including sinkhole and sybil attacks while ensuring confidentiality, authenticity, integrity with reduced latency. Finally, through security analysis, we prove that basic security is maintained and above-mentioned attacks are also prevented. We also compute storage, computation and communication overheads which show that SDD performs at least 2.6 times better in terms of storage overhead and at least 1.3 times better in terms of communication overhead over the other state-of-the-art competing schemes for attack preventions in WSN domain.

Resilient control systems should efficiently restore control into physical systems not only after the sabotage of themselves, but also after breaking physical systems. To enhance resilience of control systems, given an originally minimal-input controlled linear-time invariant(LTI) physical system, we address the problem of efficient control recovery into it after removing a known system vertex by finding the minimum number of inputs. According to the minimum input theorem, given a digraph embedded into LTI model and involving a precomputed maximum matching, this problem is modeled into recovering controllability of it after removing a known network vertex. Then, we recover controllability of the residual network by efficiently finding a maximum matching rather than recomputation. As a result, except for precomputing a maximum matching and the following removed vertex, the worst-case execution time of control recovery into the residual LTI physical system is linear.

Secret passwords are very widely used for user authentication to websites, despite their known shortcomings. Most websites using passwords also implement password recovery to allow users to re-establish a shared secret if the existing value is forgotten; many such systems involve sending a password recovery email to the user, e.g. containing a secret link. The security of password recovery, and hence the entire user-website relationship, depends on the email being acted upon correctly; unfortunately, as we show, such emails are not always designed to maximise security and can introduce vulnerabilities into recovery. To understand better this serious practical security problem, we surveyed password recovery emails for 50 of the top English language websites. We investigated a range of security and usability issues for such emails, covering their design, structure and content (including the nature of the user instructions), the techniques used to recover the password, and variations in email content from one web service to another. Many well-known web services, including Facebook, Dropbox, and Microsoft, suffer from recovery email design, structure and content issues. This is, to our knowledge, the first study of its type reported in the literature. This study has enabled us to formulate a set of recommendations for the design of such emails.

The physical unclonable functions (PUFs) have been attracted attention to prevent semiconductor counterfeits. However, the risk of machine learning attack for an arbiter PUF, which is one of the typical PUFs, has been reported. Therefore, an XOR arbiter PUF, which has a resistance against the machine learning attack, was proposed. However, in recent years, a new machine learning attack using power consumption during the operation of the PUF circuit was reported. Also, it is important that the detailed tamper resistance verification of the PUFs to consider the security of the PUFs in the future. Therefore, this study proposes a new machine learning attack using electromagnetic waveforms for the XOR arbiter PUF. Experiments by an actual device evaluate the validity of the proposed method and the security of the XOR arbiter PUF.

Several algorithms were introduced in data encryption and decryptionsto protect threats and intruders from stealing and destroying data. A DNA cryptography is a new concept that has attracted great interest in the information security. In this paper, we propose a new enhanced polyalphabetic cipher algorithm (EPCA) as enhanced algorithm for the Vigenere cipher to avoid the limitations and the weakness of Vigenere cipher. A DNA technology is used to convert binary data to DNA strand. We compared the EPCA with Vigenere cipher in terms of memory space and run time. The EPCA has theoretical run time of O(N), at worst case. The EPCA shows better performance in average memory space and closed results in average running time, for the tested data.

Remote attestation (RA) is a popular means of detecting malware in embedded and IoT devices. RA is usually realized as a protocol via which a trusted verifier measures software integrity of an untrusted remote device called prover. All prior RA techniques require on-demand operation. We identify two drawbacks of this approach in the context of unattended devices: First, it fails to detect mobile malware that enters and leaves the prover between successive RA instances. Second, it requires the prover to engage in a potentially expensive computation, which can negatively impact safety-critical or real-time devices. To this end, we introduce the concept of self-measurement whereby a prover periodically (and securely) measures and records its own software state. A verifier then collects and verifies these measurements. We demonstrate a concrete technique called ERASMUS, justify its features, and evaluate its performance. We show that ERASMUS is well-suited for safety-critical applications. We also define a new metric - Quality of Attestation (QoA).

Inner-product encryption (IPE) is a well-known functional encryption primitive that allows decryption when the inner-product of the attribute vectors, upon which the encrypted data and the decryption key depend, is equal to zero. Using IPE, it is possible to define fine-grained access policies over encrypted data whose enforcement can be outsourced to the cloud where the data are stored. However, current IPE schemes do not support efficient access policy changes. In this paper, we propose an efficient inner-product proxy re-encryption (E-IPPRE) scheme that provides the proxy server with a transformation key, with which a ciphertext associated with an attribute vector can be transformed to a new ciphertext associated with a different attribute vector, providing a policy update mechanism with a performance suitable for many practical applications. We experimentally assess the efficiency of our protocol and show that it is selective attribute-secure against chosen-plaintext attacks in the standard model under the Asymmetric Decisional Bilinear Diffie-Hellman assumption.

eAssessment uses technology to support online evaluation of students' knowledge and skills. However, challenging problems must be addressed such as trustworthiness among students and teachers in blended and online settings. The TeSLA system proposes an innovative solution to guarantee correct authentication of students and to prove the authorship of their assessment tasks. Technologically, the system is based on the integration of five instruments: face recognition, voice recognition, keystroke dynamics, forensic analysis, and plagiarism. The paper aims to analyze and compare the results achieved after the second pilot performed in an online and a blended university revealing the realization of trust-driven solutions for eAssessment.

User authentication on smartphones must satisfy both security and convenience, an inherently difficult balancing art. Apple's FaceID is arguably the latest of such efforts, at the cost of additional hardware (e.g., dot projector, flood illuminator and infrared camera). We propose a novel user authentication system EchoPrint, which leverages acoustics and vision for secure and convenient user authentication, without requiring any special hardware. EchoPrint actively emits almost inaudible acoustic signals from the earpiece speaker to "illuminate" the user's face and authenticates the user by the unique features extracted from the echoes bouncing off the 3D facial contour. To combat changes in phone-holding poses thus echoes, a Convolutional Neural Network (CNN) is trained to extract reliable acoustic features, which are further combined with visual facial landmark locations to feed a binary Support Vector Machine (SVM) classifier for final authentication. Because the echo features depend on 3D facial geometries, EchoPrint is not easily spoofed by images or videos like 2D visual face recognition systems. It needs only commodity hardware, thus avoiding the extra costs of special sensors in solutions like FaceID. Experiments with 62 volunteers and non-human objects such as images, photos, and sculptures show that EchoPrint achieves 93.75% balanced accuracy and 93.50% F-score, while the average precision is 98.05%, and no image/video based attack is observed to succeed in spoofing.

It is time-consuming and labor-intensive to learn and locate the correct API for programming tasks. Thus, it is beneficial to perform API recommendation automatically. The graph-based statistical model has been shown to recommend top-10 API candidates effectively. It falls short, however, in accurately recommending an actual top-1 API. To address this weakness, we propose RecRank, an approach and tool that applies a novel ranking-based discriminative approach leveraging API usage path features to improve top-1 API recommendation. Empirical evaluation on a large corpus of (1385+8) open source projects shows that RecRank significantly improves top-1 API recommendation accuracy and mean reciprocal rank when compared to state-of-the-art API recommendation approaches.

Loitering is a suspicious behavior that often leads to criminal actions, such as pickpocketing and illegal entry. Tracking methods can determine suspicious behavior based on trajectory, but require continuous appearance and are difficult to scale up to multi-camera systems. Using the duration of appearance of features works on multiple cameras, but does not consider major aspects of loitering behavior, such as repeated appearance and trajectory of candidates. We introduce an entropy model that maps the location of a person's features on a heatmap. It can be used as an abstraction of trajectory tracking across multiple surveillance cameras. We evaluate our method over several datasets and compare it to other loitering detection methods. The results show that our approach has similar results to state of the art, but can provide additional interesting candidates.

Although the deep learning technology effectively improves the effect of person re-identification (re-ID) in video surveillance, there is still a lack of efficient framework in practical, especially in terms of computational cost, which usually requires GPU support. So this paper explores to solve the actual running performance and an effective person re-ID framework is proposed. A tiny network is designed for person detection and a triplet network is adopted for training feature extraction network. The motion detection and person detection is combined to speed up the whole process. The proposed framework is tested in practice and the results show that it can run in real-time on an ordinary PC machine. And the accuracy achieves 91.6% in actual data set. It has a good guidance for person re-ID in actual application.

This paper proposes an efficient auditing scheme for checking the integrity of dynamic data shared among a static group of users outsourced at untrusted cloud storage. The scheme is designed based on CDH-based ring signature scheme. The scheme enables a third party auditor to audit the client's data without knowing the content while also preserving the identity privacy of the group member who is signing the data from the auditor as well as from the cloud server. The identity of the group member who is signing the data block can be revealed only by the authorized opener, if needed. The paper presents a comparative performance study and security analysis of the proposed scheme.

Cloud computing belongs to distributed network technology for computing and storage capabilities purpose. It is a kind of cost-effective technology dedicated to information technology. Using the Internet, the accessibility and retrieving of cloud data have become much more accessible. The service providers can expand the storage space in a cloud environment. Security is well-thought-out to be the essential attribute in a distributed system. Cryptography can be described as a method of securing the data from attackers and eavesdroppers. Third Party Auditor is responsible for the authentication of secret files in cloud system on behalf of the data owner. The data auditability technique allows the user to make the data integrity check using a third party. Cloud computing offers unlimited data space for storage to its users and also serves sharing of data and planned use of heterogeneous resources in distributed systems. This paper describes privacy-preserving enabled public auditing method using cryptographic techniques for low-performance based end devices.

Due to the rapid development of internet in our daily life, protecting privacy has become a focus of attention. To create privacy-preserving database and prevent illegal user access the database, oblivious transfer with access control (OTAC) was proposed, which is a cryptographic primitive that extends from oblivious transfer (OT). It allows a user to anonymously query a database where each message is protected by an access control policy and only if the user' s attribute satisfy that access control policy can obtain it. In this paper, we propose a new protocol for OTAC by using elliptic curve cryptography, which is more efficient compared to the existing similar protocols. In our scheme, we also preserves user's anonymity and ensures that the user's attribute is not disclosed to the sender. Additionally, our construction guarantees the user to verify the correctness of messages recovered at the end of each transfer phase.

Personal privacy is an important issue when publishing social network data. An attacker may have information to reidentify private data. So, many researchers developed anonymization techniques, such as k-anonymity, k-isomorphism, l-diversity, etc. In this paper, we focus on graph k-degree anonymity by editing edges. Our method is divided into two steps. First, we propose an efficient algorithm to find a new degree sequence with theoretically minimal edit cost. Second, we insert and delete edges based on the new degree sequence to achieve k-degree anonymity.

The following article shows the precision, the recall and the F1-measure for three knowledge extraction methods under Open Information Extraction paradigm. These methods are: ReVerb, OLLIE and ClausIE. For the calculation of these three measures, a representative sample of Reuters-21578 was used; 103 newswire texts were taken randomly from that database. A big discrepancy was observed, after analyzing the obtained results, between the expected and the observed precision for ClausIE. In order to save the observed gap in ClausIE precision, a simple improvement is proposed for the method. Although the correction improved the precision of Clausie, ReVerb turned out to be the most precise method; however ClausIE is the one with the better F1-measure.

In this paper, the rotor unbalanced magnetic pull (UMP) characteristics of different field winding inter-turn short-circuit (FWISC) positions in turbo-generator are studied. Firstly, the qualitative analysis on the air gap magnetic flux density (MFD), as well as the rotor UMPs in X-direction and Y-direction, is carried out. Then the finite element numerical simulations are respectively taken to calculate the quantitative data of rotor UMP under normal condition and three different short-circuit positions. Finally, the variation rules based on rotor UMP characteristics by experimental analysis are obtained. It is shown that the occurrence of FWISC will induce generally fundamental-frequency UMP acting on the rotor in X-direction. Moreover, the different positions of FWISC are found to be sensitive to the rotor UMP amplitudes. The closer the short-circuit position is to the big teeth, the larger the rotor UMP amplitudes in X-direction will be.

With the capability of support mobile computing demand with small delay, fog computing has gained tremendous popularity. Nevertheless, its highly virtualized environment is vulnerable to cyber attacks such as emerging Advanced Persistent Threats attack. In this paper, we propose a novel approach of cyber risk management for the fog computing platform. Particularly, we adopt the cyber-insurance as a tool for neutralizing cyber risks from fog computing platform. We consider a fog computing platform containing a group of fog nodes. The platform is composed of three main entities, i.e., the fog computing provider, attacker, and cyber-insurer. The fog computing provider dynamically optimizes the allocation of its defense computing resources to improve the security of the fog computing platform. Meanwhile, the attacker dynamically adjusts the allocation of its attack resources to improve the probability of successful attack. Additionally, to prevent from the potential loss due to attacks, the provider also makes a dynamic decision on the purchases ratio of cyber-insurance from the cyber-insurer for each fog node. Thereafter, the cyber-insurer accordingly determines the premium of cyber-insurance for each fog node. In our formulated dynamic Stackelberg game, the attacker and provider act as the followers, and the cyber-insurer acts as the leader. In the lower level, we formulate an evolutionary subgame to analyze the provider's defense and cyber-insurance subscription strategies as well as the attacker's attack strategy. In the upper level, the cyber-insurer optimizes its premium determination strategy, taking into account the evolutionary equilibrium at the lower-level evolutionary subgame. We analytically prove that the evolutionary equilibrium is unique and stable. Moreover, we provide a series of insightful analytical and numerical results on the equilibrium of the dynamic Stackelberg game.

As evidenced by numerous high-profile security incidents such as the Target data breach and the Equifax hack, APTs (Advanced Persistent Threats) can significantly compromise the trustworthiness of cyber space. This work explores how to improve the effectiveness of cyber deception in hardening FTP (File Transfer Protocol) services against APTs. The main objective of our work is to ensure deception consistency: when the attackers are trapped, they can only make observations that are consistent with what they have seen already so that they cannot recognize the deceptive environment. To achieve deception consistency, we use logic constraints to characterize an attacker's best knowledge (either positive, negative, or uncertain). When migrating the attacker's FTP connection into a contained environment, we use these logic constraints to instantiate a new FTP file system that is guaranteed free of inconsistency. We performed deception experiments with student participants who just completed a computer security course. Following the design of Turing tests, we find that the participants' chances of recognizing deceptive environments are close to random guesses. Our experiments also confirm the importance of observation consistency in identifying deception.

Embedded and cyber-physical systems are critically dependent on the integrity of input and output signals for proper operation. Input signals acquired from sensors are assumed to correspond to the phenomenon the system is monitoring and responding to. Similarly, when such systems issue an actuation signal it is expected that the mechanism being controlled will respond in a predictable manner. Recent work has shown that sensors can be manipulated through the use of intentional electromagnetic interference (IEMI). In this work, we demonstrate thatboth input and output signals, analog and digital, can be remotely manipulated via the physical layer—thus bypassing traditional integrity mechanisms. Through the use of specially crafted IEMI it is shown that the physical layer signaling used for sensor input to, and digital communications between, embedded systems may be undermined to an attacker's advantage. Three attack scenarios are analyzed and their efficacy demonstrated. In the first scenario the analog sensing channel is manipulated to produce arbitrary sensor readings, while in the second it is shown that an attacker may induce bit flips in serial communications. Finally, a commonly used actuation signal is shown to be vulnerable to IEMI. The attacks are effective over appreciable distances and at low power.

An ideal audio retrieval method should be not only highly efficient in identifying an audio track from a massive audio dataset, but also robust to any distortion. Unfortunately, none of the audio retrieval methods is robust to all types of distortions. An audio retrieval method has to do with both the audio fingerprint and the strategy, especially how they are combined. We argue that the Sampling and Counting Method (SC), a state-of-the-art audio retrieval method, would be promising towards an ideal audio retrieval method, if we could make it robust to time-stretch and pitch-stretch. Towards this objective, this paper proposes a turning point alignment method to enhance SC with resistance to time-stretch, which makes Philips and Philips-like fingerprints resist to time-stretch. Experimental results show that our approach can resist to time-stretch from 70% to 130%, which is on a par to the state-of-the-art methods. It also marginally improves the retrieval performance with various noise distortions.