Biblio

Information shared on Twitter is ever increasing and users-recipients are overwhelmed by the number of tweets they receive, many of which of no interest. Filters that estimate the interest of each incoming post can alleviate this problem, for example by allowing users to sort incoming posts by predicted interest (e.g., "top stories" vs. "most recent" in Facebook). Global and personal filters have been used to detect interesting posts in social networks. Global filters are trained on large collections of posts and reactions to posts (e.g., retweets), aiming to predict how interesting a post is for a broad audience. In contrast, personal filters are trained on posts received by a particular user and the reactions of the particular user. Personal filters can provide recommendations tailored to a particular user's interests, which may not coincide with the interests of the majority of users that global filters are trained to predict. On the other hand, global filters are typically trained on much larger datasets compared to personal filters. Hence, global filters may work better in practice, especially with new users, for which personal filters may have very few training instances ("cold start" problem). Following Uysal and Croft, we devised a hybrid approach that combines the strengths of both global and personal filters. As in global filters, we train a single system on a large, multi-user collection of tweets. Each tweet, however, is represented as a feature vector with a number of user-specific features.

In recent works, numerous physical-layer security systems have been proposed as alternatives to classic cryptography. Such systems aim to use the intrinsic properties of radio signals and the wireless medium to provide confidentiality and authentication to wireless devices. However, fundamental vulnerabilities are often discovered in these systems shortly after their inception. We therefore challenge the assumptions made by existing physical-layer security systems, and postulate that weaker assumptions are needed in order to adapt for practical scenarios. We also argue that if no computational advantage over an adversary can be ensured, secure communication cannot be realistically achieved.

Recent research endeavors have shown the potential of using feed-forward convolutional neural networks to accomplish fast style transfer for images. In this work, we take one step further to explore the possibility of exploiting a feed-forward network to perform style transfer for videos and simultaneously maintain temporal consistency among stylized video frames. Our feed-forward network is trained by enforcing the outputs of consecutive frames to be both well stylized and temporally consistent. More specifically, a hybrid loss is proposed to capitalize on the content information of input frames, the style information of a given style image, and the temporal information of consecutive frames. To calculate the temporal loss during the training stage, a novel two-frame synergic training mechanism is proposed. Compared with directly applying an existing image style transfer method to videos, our proposed method employs the trained network to yield temporally consistent stylized videos which are much more visually pleasant. In contrast to the prior video style transfer method which relies on time-consuming optimization on the fly, our method runs in real time while generating competitive visual results.

Recommender systems are typically evaluated on their ability to provide items that satisfy the needs and interests of the end user. However, in many real world applications, users are not the only stakeholders involved. There may be a variety of individuals or organizations that benefit in different ways from the delivery of recommendations. In this paper, we re-define the recommender system as a multistakeholder environment in which different stakeholders are served by delivering recommendations, and we suggest a utility-based approach to evaluating recommendations in such an environment that is capable of distinguishing among the distributions of utility delivered to different stakeholders.

Route planner systems support commuters and city visitors in finding the best route between two arbitrary points. More advanced route planners integrate different transportation modes such as private transport, public transport, car- and bicycle sharing or walking and are able combine these to multi-modal routes. Nevertheless, state-of-the-art planner systems usually do not consider the users' personal preferences or the wisdom of the crowd when suggesting multi-modal routes. Including the knowledge and experience of locals who are familiar with local transport allows identification of alternative routes which are, for example, less crowded during peak hours. Collaborative filtering (CF) is a technique that allows recommending items such as multi-modal routes based on the ratings of users with similar preferences. In this paper, we introduce RouteMe, a mobile recommender system for personalized, multi-modal routes which combines CF with knowledge-based recommendations to increase the quality of route recommendations. We present our hybrid algorithm in detail and show how we integrate it in a working prototype. The results of a user study show that our prototype combining CF, knowledge-based and popular route recommendations outperforms state-of-the-art route planners.

Confidentiality concerns are important in the context of cloud databases. In this paper, the technique of vertical fragmentation is explored to break sensitive associations between columns of several database tables according to confidentiality constraints. By storing insensitive portions of the database at different non-communicating servers it is possible to overcome confidentiality concerns. In addition, visibility constraints and data dependencies are supported. Moreover, to provide some control over the distribution of columns among different servers, novel closeness constraints are introduced. Finding confidentiality-preserving fragmentations is studied in the context of mathematical optimization and a corresponding integer linear program formulation is presented. Benchmarks were performed to evaluate the suitability of our approach.

The rapid growth of science and technology in the telecommunications world can come up with new ways for some people bent on abusing for threatening information security as hackers, crackers, carder, phreaker and so on. If the information is on the wrong side will result in losses. Information that must be considered is the security of confidential information. Steganography is a method that can be used to hide a message by using digital media. Digital Steganography using digital media as the container vessel such as images, sounds, text, and video. Hidden secret data can also include images, audio, text, and video. In this final audio steganography implemented. One method that can be used in steganography is the Least Significant Bit (LSB). Steganography implementation will be accompanied by the application of cryptography in the form of encryption and decryption. This method works is messages that have been encrypted beforehand will be hidden evenly on each region in MP3 or WAV already divided, with modify / change the LSB of the media container with the bits of information to be hidden. In making the steganography application, the author uses the Java programming language eclipse, because the program is quite easy and can be run in the Android smartphone operating system.

Peer-To-Peer (P2P) networks open up great possibilities for intercommunication, collaborative and social projects like file sharing, communication protocols or social networks while offering advantages over the conventional Client-Server model of computing pattern. Such networks counter the problems of centralized servers such as that P2P networks can scale to millions without additional costs. In previous work, we presented Distributed Data Structure (DDS) which offers a middle-ware scheme for distributed applications. This scheme builds on top of DHT (Distributed Hash Table) based P2P overlays, and offers distributed data storage services as a middle-ware it still needs to address security issues. The main objective of this paper is to investigate possible ways to handle the security problem for DDS, and to develop a possibly reusable security architecture for access control for secure distributed data structures in P2P networks without depending on trusted third parties.

Cyber Physical Systems (CPS) operating in modern critical infrastructures (CIs) are increasingly being targeted by highly sophisticated cyber attacks. Threat actors have quickly learned of the value and potential impact of targeting CPS, and numerous tailored multi-stage cyber-physical attack campaigns, such as Advanced Persistent Threats (APTs), have been perpetrated in the last years. They aim at stealthily compromising systems' operations and cause severe impact on daily business operations such as shutdowns, equipment damage, reputation damage, financial loss, intellectual property theft, and health and safety risks. Protecting CIs against such threats has become as crucial as complicated. Novel distributed detection and reaction methodologies are necessary to effectively uncover these attacks, and timely mitigate their effects. Correlating large amounts of data, collected from a multitude of relevant sources, is fundamental for Security Operation Centers (SOCs) to establish cyber situational awareness, and allow to promptly adopt suitable countermeasures in case of attacks. In our previous work we introduced three methods for security information correlation. In this paper we define metrics and benchmarks to evaluate these correlation methods, we assess their accuracy, and we compare their performance. We finally demonstrate how the presented techniques, implemented within our cyber threat intelligence analysis engine called CAESAIR, can be applied to support incident handling tasks performed by SOCs.

Organizations are experiencing an ever-growing concern of how to identify and defend against insider threats. Those who have authorized access to sensitive organizational data are placed in a position of power that could well be abused and could cause significant damage to an organization. This could range from financial theft and intellectual property theft to the destruction of property and business reputation. Traditional intrusion detection systems are neither designed nor capable of identifying those who act maliciously within an organization. In this paper, we describe an automated system that is capable of detecting insider threats within an organization. We define a tree-structure profiling approach that incorporates the details of activities conducted by each user and each job role and then use this to obtain a consistent representation of features that provide a rich description of the user's behavior. Deviation can be assessed based on the amount of variance that each user exhibits across multiple attributes, compared against their peers. We have performed experimentation using ten synthetic data-driven scenarios and found that the system can identify anomalous behavior that may be indicative of a potential threat. We also show how our detection system can be combined with visual analytics tools to support further investigation by an analyst.

Assured Mission Delivery Network (AMDN) is a collaborative network to support data-intensive scientific collaborations in a multi-cloud environment. Each scientific collaboration group, called a mission, specifies a set of rules to handle computing and network resources. Security is an integral part of the AMDN design since the rules must be set by authorized users and the data generated by each mission may be privacy-sensitive. In this paper, we propose a CertificateLess cryptography-based Rule-management Protocol (CL-RP) for AMDN, which supports authenticated rule registrations and updates with non-repudiation. We evaluate CL-RP through test-bed experiments and compare it with other standard protocols.

Due to the unavailability of signatures for previously unknown malware, non-signature malware detection schemes typically rely on analyzing program behavior. Prior behavior based non-signature malware detection schemes are either easily evadable by obfuscation or are very inefficient in terms of storage space and detection time. In this paper, we propose GZero, a graph theoretic approach fast and accurate non-signature malware detection at end hosts. GZero it is effective while being efficient in terms of both storage space and detection time. We conducted experiments on a large set of both benign software and malware. Our results show that GZero achieves more than 99% detection rate and a false positive rate of less than 1%, with less than 1 second of average scan time per program and is relatively robust to obfuscation attacks. Due to its low overheads, GZero can complement existing malware detection solutions at end hosts.

The heterogeneous SIS model for virus spread in any finite size graph characterizes the influence of factors of SIS model and could be analyzed by the extended N-Intertwined model introduced in [1]. We specifically focus on the heterogeneous virus spread in the star network in this paper. The epidemic threshold and the average meta-stable state fraction of infected nodes are derived for virus spread in the star network. Our results illustrate the effect of the factors of SIS model on the steady state infection.

The broadcast nature of visible light beam has aroused great concerns about the privacy and confidentiality of visible light communication (VLC) systems.In this paper, in order to enhance the physical layer security, we propose a channel scrambling scheme, which realizes orthogonal factorized channel scrambling with location information embedded (OFCS-LIE) for the VLC systems. We firstly embed the location information of the legitimate user, including the transmission angle and the distance, into a location information embedded (LIE) matrix, then the LIE matrix is factorized orthogonally in order that the LIE matrix is approximately uncorrelated to the multiple-input, multiple-output (MIMO) channels by the iterative orthogonal factorization method, where the iteration number is determined based on the orthogonal error. The resultant OFCS-LIE matrix is approximately orthogonal and used to enhance both the reliability and the security of information transmission. Furthermore, we derive the information leakage at the eavesdropper and the secrecy capacity to analyze the system security. Simulations are performed, and the results demonstrate that with the aid of the OFCS-LIE scheme, MIMO-based VLC system has achieved higher security when compared with the counterpart scrambling scheme and the system without scrambling.

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.

The principal mission of Multi-Source Multicast (MSM) is to disseminate all messages from all sources in a network to all destinations. MSM is utilized in numerous applications. In many of them, securing the messages disseminated is critical. A common secure model is to consider a network where there is an eavesdropper which is able to observe a subset of the network links, and seek a code which keeps the eavesdropper ignorant regarding all the messages. While this is solved when all messages are located at a single source, Secure MSM (SMSM) is an open problem, and the rates required are hard to characterize in general. In this paper, we consider Individual Security, which promises that the eavesdropper has zero mutual information with each message individually. We completely characterize the rate region for SMSM under individual security, and show that such a security level is achievable at the full capacity of the network, that is, the cut-set bound is the matching converse, similar to non-secure MSM. Moreover, we show that the field size is similar to non-secure MSM and does not have to be larger due to the security constraint.

This publication presents some techniques for insider threats and cryptographic protocols in secure processes. Those processes are dedicated to the information management of strategic data splitting. Strategic data splitting is dedicated to enterprise management processes as well as methods of securely storing and managing this type of data. Because usually strategic data are not enough secure and resistant for unauthorized leakage, we propose a new protocol that allows to protect data in different management structures. The presented data splitting techniques will concern cryptographic information splitting algorithms, as well as data sharing algorithms making use of cognitive data analysis techniques. The insider threats techniques will concern data reconstruction methods and cognitive data analysis techniques. Systems for the semantic analysis and secure information management will be used to conceal strategic information about the condition of the enterprise. Using the new approach, which is based on cognitive systems allow to guarantee the secure features and make the management processes more efficient.

In this paper, we propose a hardware-based defense system in Software-Defined Networking architecture to protect against the HTTP GET Flooding attacks, one of the most dangerous Distributed Denial of Service (DDoS) attacks in recent years. Our defense system utilizes per-URL counting mechanism and has been implemented on FPGA as an extension of a NetFPGA-based OpenFlow switch.

As system of systems (SoS) models become increasingly complex and interconnected a new approach is needed to capture the effects of humans within the SoS. Many real-life events have shown the detrimental outcomes of failing to account for humans in the loop. This research introduces a novel and cross-disciplinary methodology for modeling humans interacting with technologies to perform tasks within an SoS specifically within a layered physical security system use case. Metrics and formulations developed for this new way of looking at SoS termed sociotechnical SoS allow for the quantification of the interplay of effectiveness and efficiency seen in detection theory to measure the ability of a physical security system to detect and respond to threats. This methodology has been applied to a notional representation of a small military Forward Operating Base (FOB) as a proof-of-concept.

In this paper, machine learning attacks are performed on a novel hybrid delay based Arbiter Ring Oscillator PUF (AROPUF). The AROPUF exhibits improved results when compared to traditional Arbiter Physical Unclonable Function (APUF). The challenge-response pairs (CRPs) from both PUFs are fed to the multilayered perceptron model (MLP) with one hidden layer. The results show that the CRPs generated from the proposed AROPUF has more training and prediction errors when compared to the APUF, thus making it more difficult for the adversary to predict the CRPs.

We prove polarization theorems for arbitrary classical-quantum (cq) channels. The input alphabet is endowed with an arbitrary Abelian group operation and an Arikan-style transformation is applied using this operation. It is shown that as the number of polarization steps becomes large, the synthetic cq-channels polarize to deterministic homomorphism channels that project their input to a quotient group of the input alphabet. This result is used to construct polar codes for arbitrary cq-channels and arbitrary classical-quantum multiple access channels (cq-MAC). The encoder can be implemented in O(N log N) operations, where N is the blocklength of the code. A quantum successive cancellation decoder for the constructed codes is proposed. It is shown that the probability of error of this decoder decays faster than 2-Nβ for any β textless; ½.

Just as cloud customers have different performance requirements, they also have different security requirements for their computations in the cloud. Researchers have suggested a "security on demand" service model for cloud computing, where secure computing environment are dynamically provisioned to cloud customers according to their specific security needs. The availability of secure computing platforms is a necessary but not a sufficient solution to convince cloud customers to move their sensitive data and code to the cloud. Cloud customers need further assurance to convince them that the security measures are indeed deployed, and are working correctly. In this paper, we present Policy-Customized Trusted Cloud Service architecture with a new remote attestation scheme and a virtual machine migration protocol, where cloud customer can custom security policy of computing environment and validate whether the current computing environment meets the security policy in the whole life cycle of the virtual machine. To prove the availability of proposed architecture, we realize a prototype that support customer-customized security policy and a VM migration protocol that support customer-customized migration policy and validation based on open source Xen Hypervisor.

Real-time localization of mobile target has been attracted much attention in recent years. With the limitation of unavailable GPS signals in the complex environments, wireless sensor networks can be applied to real-time locate and track the mobile targets in this paper. The multi wireless signals are used to weaken the effect of abnormal wireless signals in some areas. To verify the real-time localization performance for mobile targets, experiments and analyses are implemented. The results of the experiments reflect that the proposed location method can provide experimental basis for the applications, such as the garage, shopping center, underwater, etc.

The vision of cyber-physical systems (CPSs) considered the Internet as the future communication network for such systems. A challenge with this regard is to provide high communication reliability, especially, for CPSs applications in critical infrastructures. Examples include smart grid applications with reliability requirements between 99-99.9999% [2]. Even though the Internet is a cost effective solution for such applications, the reliability of its end-to-end (e2e) paths is inadequate (often less than 99%). In this paper, we propose Reliable Multipath Communication Approach for Internet-based CPSs (RC4CPS). RC4CPS is an e2e approach that utilizes the inherent redundancy of the Internet and multipath (MP) transport protocols concept to improve reliability measured in terms of availability. It provides online monitoring and MP selection in order to fulfill the application specific reliability requirement. In addition, our MP selection considers e2e paths dependency and unavailability prediction to maximize the reliability gains of MP communication. Our results show that RC4CPS dynamic MP selection satisfied the reliability requirement along with selecting e2e paths with low dependency and unavailability probability.

Used by both information systems designers and security personnel, the Attack Tree method provides a graphical analysis of the ways in which an entity (a computer system or network, an entire organization, etc.) can be attacked and indicates the countermeasures that can be taken to prevent the attackers to reach their objective. In this paper, we built an Attack Tree focused on the goal “compromising the security of a Web platform”, considering the most common vulnerabilities of the WordPress platform identified by CVE (Common Vulnerabilities and Exposures), a global reference system for recording information regarding computer security threats. Finally, based on the likelihood of the attacks, we made a quantitative analysis of the probability that the security of the Web platform can be compromised.