Biblio

With the rapid development of the Internet, flow-based approach has attracted more and more attention. To this end, this paper presents a new and efficient architecture to balance accountability and privacy based on network flows. A self-certifying identifier is proposed to efficiently identify a flow. In addition, a delegate-registry cooperation scheme and a multi-delegate mechanism are developed to ensure users' privacy. The effectiveness and overhead of the proposed architecture are evaluated by virtue of the real trace collected from an Internet service provider. The experimental results show that our architecture can achieve a better network performance in terms of lower resource consumption, lower response time, and higher stability.

Recent insights on language and vision with neural networks have been successfully applied to simple single-image visual question answering. However, to tackle real-life question answering problems on multimedia collections such as personal photos, we have to look at whole collections with sequences of photos or videos. When answering questions from a large collection, a natural problem is to identify snippets to support the answer. In this paper, we describe a novel neural network called Focal Visual-Text Attention network (FVTA) for collective reasoning in visual question answering, where both visual and text sequence information such as images and text metadata are presented. FVTA introduces an end-to-end approach that makes use of a hierarchical process to dynamically determine what media and what time to focus on in the sequential data to answer the question. FVTA can not only answer the questions well but also provides the justifications which the system results are based upon to get the answers. FVTA achieves state-of-the-art performance on the MemexQA dataset and competitive results on the MovieQA dataset.

Automatic speaker verification systems are increasingly used as the primary means to authenticate costumers. Recently, it has been proposed to train speaker verification systems using end-to-end deep neural models. In this paper, we show that such systems are vulnerable to adversarial example attacks. Adversarial examples are generated by adding a peculiar noise to original speaker examples, in such a way that they are almost indistinguishable, by a human listener. Yet, the generated waveforms, which sound as speaker A can be used to fool such a system by claiming as if the waveforms were uttered by speaker B. We present white-box attacks on a deep end-to-end network that was either trained on YOHO or NTIMIT. We also present two black-box attacks. In the first one, we generate adversarial examples with a system trained on NTIMIT and perform the attack on a system that trained on YOHO. In the second one, we generate the adversarial examples with a system trained using Mel-spectrum features and perform the attack on a system trained using MFCCs. Our results show that one can significantly decrease the accuracy of a target system even when the adversarial examples are generated with different system potentially using different features.

Computer vision techniques that can anticipate people»s actions ahead of time could create more responsive and natural human-robot interaction systems. In this paper, we present a new human gesture forecasting framework for human-drone interaction. Our primary motivation is that despite growing interest in early recognition, little work has tried to understand how people experience these early recognition-based systems, and our human-drone forecasting framework will serve as a basis for conducting this human subjects research in future studies. We also introduce a new dataset with 22 videos of two human-drone interaction scenarios, and use it to test our gesture forecasting approach. Finally, we suggest follow-up procedures to investigate people»s experience in interacting with these early recognition-enabled systems.

We present a formal OpenFlow-based network programming language (OF) including various flow rules, which can not only describe the behaviors of an individual switch, but also support to model a network of switches connected in the point-to-point topology. Besides, a topology-oriented operational semantics of the proposed language is explored to specify how the packet is processed and delivered in the OpenFlow-based networks. Based on the formal framework, we also propose an approach to detect potential security threats caused by the conflict of dynamic flow rules imposed by dynamic OpenFlow applications.

IP piracy, reverse engineering, and tampering with FPGA based IP is increasing over time. ROPUF based IP obfuscation can provide a feasible solution. In this paper, a novel approach of FPGA IP obfuscation is implemented using Ring Oscillator based Physical Unclonable Function (ROPUF) and random logic gates. This approach provides a lock and key mechanism as well as authentication of FPGA based designs to protect from security threats. Using the Xilinx ISE design tools and ISCAS 89 benchmarks we have designed a secure FPGA based IP protection scheme with an average of 15% area and 10% of power overhead.

The persistence of search rank fraud in online, peer-opinion systems, made possible by crowdsourcing sites and specialized fraud workers, shows that the current approach of detecting and filtering fraud is inefficient. We introduce a fraud de-anonymization approach to disincentivize search rank fraud: attribute user accounts flagged by fraud detection algorithms in online peer-opinion systems, to the human workers in crowdsourcing sites, who control them. We model fraud de-anonymization as a maximum likelihood estimation problem, and introduce UODA, an unconstrained optimization solution. We develop a graph based deep learning approach to predict ownership of account pairs by the same fraudster and use it to build discriminative fraud de-anonymization (DDA) and pseudonymous fraudster discovery algorithms (PFD). To address the lack of ground truth fraud data and its pernicious impacts on online systems that employ fraud detection, we propose the first cheating-resistant fraud de-anonymization validation protocol, that transforms human fraud workers into ground truth, performance evaluation oracles. In a user study with 16 human fraud workers, UODA achieved a precision of 91%. On ground truth data that we collected starting from other 23 fraud workers, our co-ownership predictor significantly outperformed a state-of-the-art competitor, and enabled DDA and PFD to discover tens of new fraud workers, and attribute thousands of suspicious user accounts to existing and newly discovered fraudsters.

Mobile Ad-hoc Networks are dynamic in nature and do not have fixed infrastructure to govern nodes in the networks. The mission lies ahead in coordinating among such dynamically shifting nodes. The root problem of identifying and isolating misbehaving nodes that refuse to forward packets in multi-hop ad hoc networks is solved by the development of a comprehensive system called Audit-based Misbehavior Detection (AMD) that can efficiently isolates selective and continuous packet droppers. AMD evaluates node behavior on a per-packet basis, without using energy-expensive overhearing techniques or intensive acknowledgment schemes. Moreover, AMD can detect selective dropping attacks even in end-to-end encrypted traffic and can be applied to multi-channel networks. Game theoretical approaches are more suitable in deciding upon the reward mechanisms for which the mobile nodes operate upon. Rewards or penalties have to be decided by ensuring a clean and healthy MANET environment. A non-routine yet surprise alterations are well required in place in deciding suitable and safe reward strategies. This work focuses on integrating a Audit-based Misbehaviour Detection (AMD)scheme and an incentive based reputation scheme with game theoretical approach called Supervisory Game to analyze the selfish behavior of nodes in the MANETs environment. The proposed work GAMD significantly reduces the cost of detecting misbehavior nodes in the network.

Real-time crowdsourced maps, such as Waze provide timely updates on traffic, congestion, accidents, and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based Sybil devices that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. To defend against Sybil devices, we propose a new approach based on co-location edges, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large proximity graphs that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and how they can be used to dramatically reduce the impact of the attacks. We have informed Waze/Google team of our research findings. Currently, we are in active collaboration with Waze team to improve the security and privacy of their system.

Deterministic chaotic systems have been studied and developed in various fields of research. Dynamical systems with chaotic dynamics have different applications in communication, security and computation. Chaotic behaviors can be created by even simple nonlinear systems which can be implemented on low-cost hardware platforms. This paper presents a high-speed and low-cost hardware of three-dimensional chaotic flows without equilibrium. The proposed chaotic hardware is able to reproduce the main mechanism and dynamical behavior of the 3D chaotic flows observed in simulation, then a Chaotic Pseudo Random Number Generator is designed based on a 3D chaotic system. The proposed hardware is implemented with low computational overhead on an FPGA board, as a proof of concept. This low-cost chaotic hardware can be utilized in embedded and lightweight systems for a variety of chaotic based digital systems such as digital communication systems, and cryptography systems based on chaos theory for Security and IoT applications.

Because major smartphone platforms are equipped with Bluetooth Low Energy (BLE) capabilities, more and more smart devices have adopted BLE technologies to communicate with smartphones. In order to support the mesh topology in BLE networks, several proposals have been designed. Among them, the Bluetooth Special Interest Group (SIG) recently released a specification for Bluetooth mesh networks based upon BLE technology. This paper focuses on this standard solution and analyses its security protocol with hardware security in mind. As it is expected that internet of things (IoT) devices will be deployed everywhere, the risk of physical attacks must be assessed. First, we provide a comprehensive survey of the security features involved in Bluetooth mesh. Then, we introduce some physical attacks identified as serious threats for the IoT and discuss their relevance in the case of Bluetooth mesh networks. Finally, we briefly discuss possible countermeasures to reach a secure implementation.

With all the exciting benefits of IoT in healthcare - from mobile applications to wearable and implantable health gadgets-it becomes prominent to ensure that patients, their medical data and the interactions to and from their medical devices are safe and secure. The security and privacy is being breached when the mobile applications are mishandled or tampered by the hackers by performing reverse engineering on the application leading to catastrophic consequences. To combat against these vulnerabilities, there is need to create an awareness of the potential risks of these devices and effective strategies are needed to be implemented to achieve a level of security defense. In this paper, the benefits of healthcare IoT system and the possible vulnerabilities that may result are presented. Also, we propose to develop solutions against these vulnerabilities by protecting mobile applications using obfuscation and return oriented programming techniques. These techniques convert an application into a form which makes difficult for an adversary to interpret or alter the code for illegitimate purpose. The mobile applications use keys to control communication with the implantable medical devices, which need to be protected as they are the critical component for securing communications. Therefore, we also propose access control schemes using white box encryption to make the keys undiscoverable to hackers.

We present a new locally differentially private algorithm for the heavy hitters problem which achieves optimal worst-case error as a function of all standardly considered parameters. Prior work obtained error rates which depend optimally on the number of users, the size of the domain, and the privacy parameter, but depend sub-optimally on the failure probability. We strengthen existing lower bounds on the error to incorporate the failure probability, and show that our new upper bound is tight with respect to this parameter as well. Our lower bound is based on a new understanding of the structure of locally private protocols. We further develop these ideas to obtain the following general results beyond heavy hitters. (1) Advanced Grouposition: In the local model, group privacy for k users degrades proportionally to root k, instead of linearly in k as in the central model. Stronger group privacy yields improved max-information guarantees, as well as stronger lower bounds (via "packing arguments"), over the central model. (2) Building on a transformation of Bassily and Smith (STOC 2015), we give a generic transformation from any non-interactive approximate-private local protocol into a pure-private local protocol. Again in contrast with the central model, this shows that we cannot obtain more accurate algorithms by moving from pure to approximate local privacy.

Voice interfaces are increasingly becoming integrated into a variety of Internet of Things (IoT) devices. Such systems can dramatically simplify interactions between users and devices with limited displays. Unfortunately voice interfaces also create new opportunities for exploitation. Specifically any sound-emitting device within range of the system implementing the voice interface (e.g., a smart television, an Internet-connected appliance, etc) can potentially cause these systems to perform operations against the desires of their owners (e.g., unlock doors, make unauthorized purchases, etc). We address this problem by developing a technique to recognize fundamental differences in audio created by humans and electronic speakers. We identify sub-bass over-excitation, or the presence of significant low frequency signals that are outside of the range of human voices but inherent to the design of modern speakers, as a strong differentiator between these two sources. After identifying this phenomenon, we demonstrate its use in preventing adversarial requests, replayed audio, and hidden commands with a 100%/1.72% TPR/FPR in quiet environments. In so doing, we demonstrate that commands injected via nearby audio devices can be effectively removed by voice interfaces.

Despite the popularity of wireless sensor networks (WSNs) in a wide range of applications, security problems associated with them have not been completely resolved. Middleware is generally introduced as an intermediate layer between WSNs and the end user to resolve some limitations, but most of the existing middleware is unable to protect data from malicious and unknown attacks during transmission. This paper introduces an intelligent middleware based on an unsupervised learning technique called Generative Adversarial Networks (GANs) algorithm. GANs contain two networks: a generator (G) network and a detector (D) network. The G creates fake data similar to the real samples and combines it with real data from the sensors to confuse the attacker. The D contains multi-layers that have the ability to differentiate between real and fake data. The output intended for this algorithm shows an actual interpretation of the data that is securely communicated through the WSN. The framework is implemented in Python with experiments performed using Keras. Results illustrate that the suggested algorithm not only improves the accuracy of the data but also enhances its security by protecting data from adversaries. Data transmission from the WSN to the end user then becomes much more secure and accurate compared to conventional techniques.

This work describes a three-times (\$3$\backslash$times\$) improvement to the performance of secure computation of AES over a network of three parties with an honest majority. The throughput that is achieved is even better than that of computing AES in some scenarios of local (non-private) computation. The performance improvement is achieved through an optimization of the generic secure protocol, and, more importantly, through an optimization of the description of the AES function to support more efficient secure computation, and an optimization of the protocol to the underlying architecture. This demonstrates that the development process of efficient secure computation must include adapting the description of the computed function to be tailored to the protocol, and adapting the implementation of the protocol to the architecture. This work focuses on the secure computation of AES since it has been widely investigated as a de-facto standard performance benchmark for secure computation, and is also important by itself for many applications. Furthermore, parts of the improvements are general and not specific to AES, and can be applied to secure computation of arbitrary functions.

While it is relatively easy to imitate and evolve natural swarm behavior in simulations, less is known about the social characteristics of simulated, evolved swarms, such as the optimal (evolutionary) group size, why individuals in a swarm perform certain actions, and how behavior would change in swarms of different sizes. To address these questions, we used a genetic algorithm to evolve animats equipped with Markov Brains in a spatial navigation task that facilitates swarm behavior. The animats' goal was to frequently cross between two rooms without colliding with other animats. Animats were evolved in swarms of various sizes. We then evaluated the task performance and social behavior of the final generation from each evolution when placed with swarms of different sizes in order to evaluate their generalizability across conditions. According to our experiments, we find that swarm size during evolution matters: animats evolved in a balanced swarm developed more flexible behavior, higher fitness across conditions, and, in addition, higher brain complexity.

In internet based communication, various types of attacks have been evolved. Hence, attacker easily breaches the securities. Traditional intrusion detection techniques to observe these attacks have failed and thus hefty systems are required to remove these attacks before they expose entire network. With the ability of artificial intelligence systems to adapt high computational speed, boost fault tolerance, and error resilience against noisy information, a hybrid particle swarm optimization(PSO) fuzzy rule based inference engine has been designed in this paper. The fuzzy logic based on degree of truth while the PSO algorithm based on population stochastic technique helps in learning from the scenario, thus their combination will increase the toughness of intrusion detection system. The proposed network intrusion detection system will be able to classify normal as well as anomalism behaviour in the network. DARPA-KDD99 dataset examined on this system to address the behaviour of each connection on network and compared with existing system. This approach improves the result on the basis of precision, recall and F1-score.

Swarm intelligence is rather a simple implementation but has a good performance in function optimization. There are a variety of instances of swarm model and has its inherent dynamic property. In this study we consider a hybrid swarm model where agents complement each other using its native property. Employing popular swarm intelligence model Particle swarm and Firefly we consider hybridization methods in this study. This paper presents a hybridization that agents in Particle swarm selected by a simple rule or a random choice are changing its property to Firefly. Numerical studies are carried out by using complex function optimization benchmarks, the proposed method gives better performance compared with standard PSO.

HYDRA is an automated mechanism to repair code in response to successful attacks. Given a set of malicious inputs that include the attack and a set of benign inputs that do not, along with an ability to test the victim application with these labelled inputs, HYDRA quickly provides rank ordered patches to close the exploited vulnerability. HYDRA also produces human-readable summaries of its findings and repair actions to aid the manual vulnerability mitigation process. We tested HYDRA using 8 zero-days, HYDRA produced patches that stopped the attacks in all 8 cases and preserved application functionality in 7 of the 8 cases.

Reversible logic is a building block for adiabatic and quantum computing in addition to other applications. Since common functions are non-reversible, one needs to embed them into proper-size reversible functions by adding ancillary inputs and garbage outputs. We explore the Intellectual Property (IP) piracy of reversible circuits. The number of embeddings of regular functions in a reversible function and the percent of leaked ancillary inputs measure the difficulty of recovering the embedded function. To illustrate the key concepts, we study reversible logic circuits designed using reversible logic synthesis tools based on Binary Decision Diagrams and Quantum Multi-valued Decision Diagrams.

In this paper, a novel chaotic image encryption scheme based on the inverse fractal interpolation function system is proposed. The inverse fractal interpolation function system associated with fractal interpolation surface is applied to generate chaotic sequences. The derived sequences are then employed to permute the pixel positions to get the shuffled image by chaotic sequence sorting. The obtained chaotic sequences are then quantized to yield one pseudo-random gray value sequence used to perform diffusion to enhance the security. The security and performance of the proposed image encryption scheme have been analysed, including histograms, correlation coefficients, information entropy, differential analysis, etc. All the experimental results suggest that the proposed image encryption scheme is robust and secure and can be used for secure image and video communication applications.

Mobile network operators choose Self Organizing Network (SON) concept as a cost-effective method to deploy LTE/4G networks and meet user expectations for high quality of service and bandwidth. The main objective of SON is to introduce automation into network management activities and reduce human intervention. SON enabled LTE networks heavily rely on the information acquired from mobile phones to provide self-configuration, self-optimization, and self-healing features. However, mobile phones can be attacked over-the-air using rogue base stations. In this paper, we carefully study SON related LTE/4G security specifications and reveal several vulnerabilities. Our key idea is to introduce a rogue eNodeB that uses legitimate mobile devices as a covert channel to launch attacks against SON enabled LTE networks. We demonstrate low-cost, practical, silent and persistent Denial of Service attacks against the network and end-users by injecting fake measurement and configuration information into the SON system. An active attacker can shut down network services in 2 km2 area of a city for a certain period of time and also block network services to a selective set of mobile phones in a targeted area of 200 m to 2 km in radius. With the help of low cost tools, we design an experimental setup and evaluate these attacks on commercial networks. We present strategies to mitigate our attacks and outline possible reasons that may explain why these vulnerabilities exist in the system.

With the advent of the Internet of Things (IoT), wireless sensor and actuator networks, subsequently referred to as IoT networks (IoTNs), are proliferating at an unprecedented rate in several newfound areas such as smart cities, health care, and transportation, and consequently, securing them is of paramount importance. In this paper, we present several useful insights from an exploratory study of the impacts of network layer attacks on IoTNs. We envision that these insights will guide the design of future frameworks to defend against network layer attacks. We also present a preliminary such framework and demonstrate its effectiveness in detecting network layer attacks through experiments on a real IoTN test-bed.

Vehicular ad hoc networks (VANETs) are eminent type of Mobile ad hoc Networks. The network created in VANETs is quite prone to security problem. In this work, a new mechanism is proposed to study the security of VANETs against DDoS attack. The proposed mechanism focuses on distributed denial of service attacks. The main idea of the paper is to detect the DDoS attack and mitigate it. The work consists of two stages, initially attack topology and network congestion is created. The second stage is to detect and mitigate the DDoS attack. The existing method is compared with the proposed method for mitigating DDoS attacks in VANETs. The existing solutions presented by the various researchers are also compared and analyzed. The solution for such kind of problem is provided which is used to detect and mitigate DDoS attack by using greedy approach. The network environment is created using NS-2. The results of simulation represent that the proposed approach is better in the terms of network packet loss, routing overhead and network throughput.