Biblio

This paper proposes a deep learning-based white-hat worm launcher in Botnet Defense System (BDS). BDS uses white-hat botnets to defend an IoT system against malicious botnets. White-hat worm launcher literally launches white-hat worms to create white-hat botnets according to the strategy decided by BDS. The proposed launcher learns with deep learning where is the white-hat worms' right place to successfully drive out malicious botnets. Given a system situation invaded by malicious botnets, it predicts a worms' placement by the learning result and launches them. We confirmed the effect of the proposed launcher through simulating evaluation.

Moving target defense (MTD) suggests a game-changing way of enhancing web security by increasing uncertainty and complexity for attackers. A good number of web MTD techniques have been investigated to counter various types of web attacks. However, in most MTD techniques, only fixed attributes of the attack surface are shifted, leaving the rest exploitable by the attackers. Currently, there are few mechanisms to support the whole attack surface movement and solve the partial coverage problem, where only a fraction of the possible attributes shift in the whole attack surface. To address this issue, this paper proposes a Web Gene Tree (WGT) based MTD mechanism. The key point is to extract all potential exploitable key attributes related to vulnerabilities as web genes, and mutate them using various MTD techniques to withstand various attacks. Experimental results indicate that, by randomly shifting web genes and diversely inserting deceptive ones, the proposed WGT mechanism outperforms other existing schemes and can significantly improve the security of web applications.

Nowadays, smartphones are everywhere. They play an indispensable role in our lives and makes people convenient to communicate, pay, socialize, etc. However, they also bring a lot of security and privacy risks. Keystroke operations of numeric keypad are often required when users input password to perform mobile payment or input other privacy-sensitive information. Different keystrokes may cause different finger movements that will bring different interference to WiFi signal, which may be reflected by channel state information (CSI). In this paper, we propose WiPass, a password-keystroke recognition system for numerical keypad input on smartphones, which especially occurs frequently in mobile payment APPs. Based on only a public WiFi hotspot deployed in the victim payment scenario, WiPass would extracts and analyzes the CSI data generated by the password-keystroke operation of the smartphone user, and infers the user's payment password by comparing the CSI waveforms of different keystrokes. We implemented the WiPass system by using COTS WiFi AP devices and smartphones. The average keystroke segmentation accuracy was 80.45%, and the average keystroke recognition accuracy was 74.24%.

The usage of K-anonymity to preserve location privacy for wireless sensor network (WSN) monitoring systems, where sensor nodes operate together to notify a server with anonymous shared positions. That k-anonymous position is a coated region with at least k people. However, we identify an attack model to show that overlapping aggregate locations remain privacy-risk because the enemy can infer certain overlapping areas with persons under k who violate the privacy requirement for anonymity. Within this paper we suggest a mutual WSN privacy protocol (REAL). Actual needs sensor nodes to arrange their sensing areas separately into a variety of non-overlapping, extremely precise anonymous aggregate positions. We also developed a state transfer framework, a locking mechanism and a time delay mechanism to address the three main REAL challenges, namely self-organisation, shared assets and high precision. We equate REAL's output with current protocols through virtual experiments. The findings demonstrate that REAL preserves the privacy of sites, offers more precise question answers and decreases connectivity and device expense.

Dynamic strain sensing is necessary for high-temperature harsh-environment applications, including powerplants, oil wells, aerospace, and metal manufacturing. Monitoring dynamic strain is important for structural health monitoring and condition-based maintenance in order to guarantee safety, increase process efficiency, and reduce operation and maintenance costs. Sensing in high-temperature (HT), harsh-environments (HE) comes with challenges including mounting and packaging, sensor stability, and data acquisition and processing. Wireless sensor operation at HT is desirable because it reduces the complexity of the sensor connection, increases reliability, and reduces costs. Surface acoustic wave resonators (SAWRs) are compact, can operate wirelessly and battery-free, and have been shown to operate above 1000°C, making them a potential option for HT HE dynamic strain sensing. This paper presents wirelessly interrogated SAWR dynamic strain sensors operating around 288.8MHz at room temperature and tested up to 400°C. The SAWRs were calibrated with a high-temperature wired commercial strain gauge. The sensors were mounted onto a tapered-type Inconel constant stress beam and the assembly was tested inside a box furnace. The SAWR sensitivity to dynamic strain excitation at 25°C, 100°C, and 400°C was .439 μV/με, 0.363μV/με, and .136 μV/με, respectively. The experimental outcomes verified that inductive coupled wirelessly interrogated SAWRs can be successfully used for dynamic strain sensing up to 400°C.

Trust is a critical issue in human-robot interactions (HRI) as it is the core of human desire to accept and use a non-human agent. Theory of Mind (ToM) has been defined as the ability to understand the beliefs and intentions of others that may differ from one's own. Evidences in psychology and HRI suggest that trust and ToM are interconnected and interdependent concepts, as the decision to trust another agent must depend on our own representation of this entity's actions, beliefs and intentions. However, very few works take ToM of the robot into consideration while studying trust in HRI. In this paper, we investigated whether the exposure to the ToM abilities of a robot could affect humans' trust towards the robot. To this end, participants played a Price Game with a humanoid robot (Pepper) that was presented having either low-level ToM or high-level ToM. Specifically, the participants were asked to accept the price evaluations on common objects presented by the robot. The willingness of the participants to change their own price judgement of the objects (i.e., accept the price the robot suggested) was used as the main measurement of the trust towards the robot. Our experimental results showed that robots possessing a high-level of ToM abilities were trusted more than the robots presented with low-level ToM skills.

Today's smart-grids have seen a clear rise in new ways of energy generation, transmission, and storage. This has not only introduced a huge degree of variability, but also a continual shift away from traditionally centralized generation and storage to distributed energy resources (DERs). In addition, the distributed sensors, energy generators and storage devices, and networking have led to a huge increase in attack vectors that make the grid vulnerable to a variety of attacks. The interconnection between computational and physical components through a largely open, IP-based communication network enables an attacker to cause physical damage through remote cyber-attacks or attack on software-controlled grid operations via physical- or cyber-attacks. Transactive Energy (TE) is an emerging approach for managing increasing DERs in the smart-grids through economic and control techniques. Transactive Smart-Grids use the TE approach to improve grid reliability and efficiency. However, skepticism remains in their full-scale viability for ensuring grid reliability. In addition, different TE approaches, in specific situations, can lead to very different outcomes in grid operations. In this paper, we present a comprehensive web-based platform for evaluating resilience of smart-grids against a variety of cyber- and physical-attacks and evaluating impact of various TE approaches on grid performance. We also provide several case-studies demonstrating evaluation of TE approaches as well as grid resilience against cyber and physical attacks.

One of the biggest problems in the current society is people's safety. Safety measures and mechanisms are especially important in the case of vulnerable social groups, such as migrants, homeless, and victims of domestic and/or sexual violence. In order to cope with this problem, we witness an increasing number of personal alarm systems in the market, most of them based on panic buttons. Nevertheless, none of them has got widespread acceptance mainly because of limited Human-Computer Interaction. In the context of this work, we developed an innovative mobile application that recognizes an attack through speech and gesture recognition. This paper describes such a system and presents its features, some of them based on the emerging concept of Human-in-the-Loop Cyber-physical Systems and new concepts of Human-Computer Interaction.

With the emergence of biometric technology in various applications, such as access control (e.g. mobile lock/unlock), financial transaction (e.g. Alibaba smile-to-pay) and time attendance, the development of biometric system attracts increasingly interest to the developers. Despite a sound biometric system gains the security assurance and great usability, it is a rather challenging task to develop an effective biometric system. For instance, many public available biometric APIs do not provide sufficient instructions / precise documentations on the usage of biometric APIs. Many developers are struggling in implementing these APIs in various tasks. Moreover, quick update on biometric-based algorithms (e.g. feature extraction and matching) may propagate to APIs, which leads to potential confusion to the system developers. Hence, we conduct an empirical study to the problems that the developers currently encountered while implementing the biometric APIs as well as the issues that need to be addressed when developing biometric systems using these APIs. We manually analyzed a total of 500 biometric API-related posts from various online media such as Stack Overflow and Neurotechnology. We reveal that 1) most of the problems encountered are related to the lack of precise documentation on the biometric APIs; 2) the incompatibility of biometric APIs cross multiple implementation environments.

Industry 4.0 and the Internet of Production can increase efficiency and effectiveness of workflows in manufacturing companies and production networks. Despite ubiquitous automation, people are essential in socio-technical cyber-physical production systems due to unique cognitive capabilities, as final arbitrators, or for ethical and legal reasons. However, the design of interfaces between the human-in-the-loop and production systems poses challenges not yet been sufficiently elaborated in research and practice. We present two behavioural studies in the context of inter-company collaboration that show why considering the human-in-the-loop is crucial: The first study shows that information complexity and individual differences shape the overall decision quality. With increasing information complexity, the decision speed decreases and the decision accuracy descends. Consequently, a fine balance between necessary, abundant, and superfluous information must be found. The second experiment studies human decision making in complex environments using a business simulation. We found that correct decision aids can augment the human-in-the-loop's decision making and that these can increase usability, trust, and proft. Yet, incorrect decision support has the opposite effect. Guidelines for designing socio-technical cyber-physical production systems and a research agenda conclude this article.

Data dropouts in communication network can have a significant impact on wide-area oscillation damping control of a smart power grid with large-scale deployment of distributed and networked phasor measurement units and wind energy resources. Remote feedback signals sent through communication channels encounter data dropout, which is represented by the Gilbert-Elliott model. An observer-driven reduced copy (ORC) approach is presented, which uses the knowledge of the nominal system dynamics during data dropouts to improve the damping performance where conventional feedback would suffer. An expression for the expectation of the bound on the error norm between the actual and the estimated states relating uncertainties in the cyber system due to data dropout and physical system due to change in operating conditions is also derived. The key contribution comes from the analytical derivation of the impact of coupling between the cyber and the physical layer on ORC performance. Monte Carlo simulation is performed to calculate the dispersion of the error bound. Nonlinear time-domain simulations demonstrate that the ORC produces significantly better performance compared to conventional feedback under higher data drop situations.

To experimentally study the characteristics of ultrasonic sensors, a wideband air-coupled ultrasonic transducer, wideband electromagnetic dynamic acoustic transducer (WEMDAT), is designed and fabricated. Characterisation methods, including electrical impedance analysis, laser Doppler vibrometry and pressure-field microphone measurement, are used to examine the performance of the WEMDAT, which have shown that the transducer has a wide bandwidth ranging approximately from 47 kHz to 145 kHz and a good directivity with a beam angle of around 20˚ with no evident side lobes. A 40 kHz commercial flexural ultrasonic transducer (FUT) is then taken as an example to receive ultrasonic waves in a pitch-catch configuration to evaluate the performance of the WEMDAT as an acoustic source. Experiment results have demonstrated that the WEMDAT can maintain the most of the frequency content of a 5 cycle 40 kHz tone burst electric signal and convert it into an ultrasonic wave for studying the dynamic characteristic and the directivity pattern of the ultrasonic receiver. A comparison of the dynamic characteristics between the transmitting and the receiving processes of the same FUT reveals that the FUT has a wider bandwidth when operating as an ultrasonic receiver than operating as a transmitter, which indicates that it is necessary to quantitatively investigate the receiving process of an ultrasonic transducer, demonstrating a huge potential of the WEMDAT serving as a standard acoustic source for ultrasonic sensors for various air-coupled ultrasonic applications.

The need for sensors to deliver, communicate, collect, alert, and share information in various applications has made wireless sensor networks very popular. However, due to its limited resources in terms of computation power, battery life and memory storage of the sensor nodes, it is challenging to add security features to provide the confidentiality, integrity, and availability. Blockchain technology ensures security and avoids the need of any trusted third party. However, applying Blockchain in a resource-constrained wireless sensor network is a challenging task because Blockchain is power, computation, and memory hungry in nature and demands heavy bandwidth due to control overheads. In this paper, a new routing and a private communication Blockchain framework is designed and tested with Constant Bit rate (CBR). The proposed Load Balancing Multi-Hop (LBMH) routing shares and enhances the battery life of the Cluster Heads and reduce control overhead during Block updates, but due to limited storage and energy of the sensor nodes, Blockchain in sensor networks may never become a reality unless computation, storage and battery life are readily available at low cost.

Wireless Sensor Networks (WSNs) are important and becoming more important as we integrate wireless sensor networks and the internet with different things, which has changed our life, and it is affected everywhere in our life like shopping, storage, live monitoring, smart home etc., called Internet of Things (IoT), as any use of the network physical devices that included in electronics, software, sensors, actuators, and connectivity which makes available these things to connect, collect and exchange data, and the most importantly thing is the accuracy of the data that has been collected in the Internet of Things, detecting sensor data with faulty readings is an important issue of secure communication and power consumption. So, requirement of energy-efficiency and integrity of information is mandatory.

The mobile ad-hoc networks comprised of nodes that are communicated through dynamic request and also by static table driven technique. The dynamic route discovery in AODV routing creates an unsecure transmission as well as reception. The reason for insecurity is the route request is given to all the nodes in the network communication. The possibility of the intruder nodes are more in the case of dynamic route request. Wormhole attacks in MANETs are creating challenges in the field of network analysis. In this paper the wormhole scenario is realized using high power transmission. This is implemented using energy model of ns2 simulator. The Apptool simulator identifies the energy level of each node and track the node of high transmission power. The performance curves for throughput, node energy for different encrypted values, packet drop ratio, and end to end delay are plotted.

Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs or patterns) are the first choice of most consumers to authorize the payment. This paper demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, WristSpy, which examines to what extent the user's PIN/pattern during the mobile payment could be revealed from a single wrist-worn wearable device under different passcode input scenarios involving either two hands or a single hand. In particular, WristSpy has the capability to accurately reconstruct fine-grained hand movement trajectories and infer PINs/patterns when mobile and wearable devices are on two hands through building a Euclidean distance-based model and developing a training-free parallel PIN/pattern inference algorithm. When both devices are on the same single hand, a highly challenging case, WristSpy extracts multi-dimensional features by capturing the dynamics of minute hand vibrations and performs machine-learning based classification to identify PIN entries. Extensive experiments with 15 volunteers and 1600 passcode inputs demonstrate that an adversary is able to recover a user's PIN/pattern with up to 92% success rate within 5 tries under various input scenarios.

We propose WristUnlock, a novel technique that uses a wrist wearable to unlock a smartphone in a secure and usable fashion. WristUnlock explores both the physical proximity and secure Bluetooth connection between the smartphone and wrist wearable. There are two modes in WristUnlock with different security and usability features. In the WristRaise mode, the user raises his smartphone in his natural way with the same arm carrying the wrist wearable; the smartphone gets unlocked if the acceleration data on the smartphone and wrist wearable satisfy an anticipated relationship specific to the user himself. In the WristTouch mode, the wrist wearable sends a random number to the smartphone through both the Bluetooth channel and a touch-based physical channel; the smartphone gets unlocked if the numbers received from both channels are equal. We thoroughly analyze the security of WristUnlock and confirm its high efficacy through detailed experiments.

Defender-attacker Stackelberg security games (SSGs) have been applied for solving many real-world security problems. Recent work in SSGs has incorporated a deceptive signaling scheme into the SSG model, where the defender strategically reveals information about her defensive strategy to the attacker, in order to influence the attacker’s decision making for the defender’s own benefit. In this work, we study the problem of signaling in security games against a boundedly rational attacker. 

Every day, university networks are bombarded with attempts to steal the sensitive data of the various disparate domains and organizations they serve. For this reason, universities form teams of information security specialists called a Security Operations Center (SOC) to manage the complex operations involved in monitoring and mitigating such attacks. When a suspicious event is identified, members of the SOC are tasked to understand the nature of the event in order to respond to any damage the attack might have caused. This process is defined by administrative policies which are often very high-level and rarely systematically defined. This impedes the implementation of generalized and automated event response solutions, leading to specific ad hoc solutions based primarily on human intuition and experience as well as immediate administrative priorities. These solutions are often fragile, highly specific, and more difficult to reuse in other scenarios.

Today's smart-grids have seen a clear rise in new ways of energy generation, transmission, and storage. This has not only introduced a huge degree of variability, but also a continual shift away from traditionally centralized generation and storage to distributed energy resources (DERs). In addition, the distributed sensors, energy generators and storage devices, and networking have led to a huge increase in attack vectors that make the grid vulnerable to a variety of attacks. The interconnection between computational and physical components through a largely open, IP-based communication network enables an attacker to cause physical damage through remote cyber-attacks or attack on software-controlled grid operations via physical- or cyber-attacks. Transactive Energy (TE) is an emerging approach for managing increasing DERs in the smart-grids through economic and control techniques. Transactive Smart-Grids use the TE approach to improve grid reliability and efficiency. However, skepticism remains in their full-scale viability for ensuring grid reliability. In addition, different TE approaches, in specific situations, can lead to very different outcomes in grid operations. In this paper, we present a comprehensive web-based platform for evaluating resilience of smart-grids against a variety of cyber- and physical-attacks and evaluating impact of various TE approaches on grid performance. We also provide several case-studies demonstrating evaluation of TE approaches as well as grid resilience against cyber and physical attacks. 

Internet of Things (IoT) is an evolving research area for the last two decades. The integration of the IoT and social networking concept results in developing an interdisciplinary research area called the Social Internet of Things (SIoT). The SIoT is dominant over the traditional IoT because of its structure, implementation, and operational manageability. In the SIoT, devices interact with each other independently to establish a social relationship for collective goals. To establish trustworthy relationships among the devices significantly improves the interaction in the SIoT and mitigates the phenomenon of risk. The problem is to choose a trustworthy node who is most suitable according to the choice parameters of the node. The best-selected node by one node is not necessarily the most suitable node for other nodes, as the trustworthiness of the node is independent for everyone. We employ some theoretical characterization of the soft-set theory to deal with this kind of decision-making problem. In this paper, we developed a weighted based trustworthiness ranking model by using soft set theory to evaluate the trustworthiness in the SIoT. The purpose of the proposed research is to reduce the risk of fraudulent transactions by identifying the most trusted nodes.

Network attacks continue to pose threats to missions in cyber space. To prevent critical missions from getting impacted or minimize the possibility of mission impact, active cyber defense is very important. Mission impact graph is a graphical model that enables mission impact assessment and shows how missions can be possibly impacted by cyber attacks. Although the mission impact graph provides valuable information, it is still very difficult for human analysts to comprehend due to its size and complexity. Especially when given limited resources, human analysts cannot easily decide which security measures to take first with respect to mission assurance. Therefore, this paper proposes to apply a ranking algorithm towards the mission impact graph so that the huge amount of information can be prioritized. The actionable conditions that can be managed by security admins are ranked with numeric values. The rank enables efficient utilization of limited resources and provides guidance for taking security countermeasures.

The rapid growth and globalization of the integrated circuit (IC) industry put the threat of hardware Trojans (HTs) front and center among all security concerns in the IC supply chain. Current Trojan detection approaches always assume HTs are composed of digital circuits. However, recent demonstrations of analog attacks, such as A2 and Rowhammer, invalidate the digital assumption in previous HT detection or testing methods. At the system level, attackers can utilize the analog properties of the underlying circuits such as charge-sharing and capacitive coupling effects to create information leakage paths. These new capacitor-based vulnerabilities are rarely covered in digital testings. To address these stealthy yet harmful threats, we identify a large class of such capacitor-enabled attacks and define them as charge-domain Trojans. We are able to abstract the detailed charge-domain models for these Trojans and expose the circuit-level properties that critically contribute to their information leakage paths. Aided by the abstract models, an information flow tracking (IFT) based solution is developed to detect charge-domain leakage paths and then identify the charge-domain Trojans/vulnerabilities. Our proposed method is validated on an experimental RISC microcontroller design injected with different variants of charge-domain Trojans. We demonstrate that successful detection can be accomplished with an automatic tool which realizes the IFT-based solution.

Today's virtual switches not only support legacy network protocols and standard network management interfaces, but also become adapted to OpenFlow as a prevailing communication protocol. This makes them a core networking component of today's virtualized infrastructures which are able to handle sophisticated networking scenarios in a flexible and software-defined manner. At the same time, these virtual SDN data planes become high-value targets because a compromised switch is hard to detect while it affects all components of a virtualized/SDN-based environment.Most of the well known programmable virtual switches in the market are open source which makes them cost-effective and yet highly configurable options in any network infrastructure deployment. However, this comes at a cost which needs to be addressed. Accordingly, this paper raises an alarm on how attackers may leverage white box analysis of software switch functionalities to lunch effective low profile attacks against it. In particular, we practically present how attackers can systematically take advantage of static and dynamic code analysis techniques to lunch a low rate saturation attack on virtual SDN data plane in a cloud data center.

Network virtualization is a fundamental technology for datacenters and upcoming wireless communications (e.g., 5G). It takes advantage of software-defined networking (SDN) that provides efficient network management by converting networking fabrics into SDN-capable devices. Moreover, white-box switches, which provide flexible and fast packet processing, are broadly deployed in commercial datacenters. A white-box switch requires a specific and restricted packet processing pipeline; however, to date, there has been no SDN-based network hypervisor that can support the pipeline of white-box switches. Therefore, in this paper, we propose WhiteVisor: a network hypervisor which can support the physical network composed of white-box switches. WhiteVisor converts a flow rule from the virtual network into a packet processing pipeline compatible with the white-box switch. We implement the prototype herein and show its feasibility and effectiveness with pipeline conversion and overhead.