Biblio

Many organizations are still reluctant to move sensitive data to the cloud. Moreover, data protection regulations have established considerable punishments for violations of privacy and security requirements. Privacy, however, is a concept that is difficult to measure and to demonstrate. While many privacy design strategies, tactics and patterns have been proposed for privacy-preserving system design, it is difficult to evaluate an existing system with regards to whether these strategies have or have not appropriately been implemented. In this paper we propose indicators for a system's non-compliance with privacy design strategies, called privacy smells. To that end we first identify concrete metrics that measure certain aspects of existing privacy design strategies. We then define smells based on these metrics and discuss their limitations and usefulness. We identify these indicators on two levels of a cloud system: the data flow level and the access control level. Using a cloud system built in Microsoft Azure we show how the metrics can be measured technically and discuss the differences to other cloud providers, namely Amazon Web Services and Google Cloud Platform. We argue that while it is difficult to evaluate the privacy-awareness in a cloud system overall, certain privacy aspects in cloud systems can be mapped to useful metrics that can indicate underlying privacy problems. With this approach we aim at enabling cloud users and auditors to detect deep-rooted privacy problems in cloud systems.

Online privacy policies are lengthy and hard to comprehend. To address this problem, researchers have utilized machine learning (ML) to devise tools that automatically summarize online privacy policies for web users. One such tool is our free and publicly available browser extension, PrivacyCheck. In this paper, we enhance PrivacyCheck by adding a competitor analysis component-a part of PrivacyCheck that recommends other organizations in the same market sector with better privacy policies. We also monitored the usage patterns of about a thousand actual PrivacyCheck users, the first work to track the usage and traffic of an ML-based privacy analysis tool. Results show: (1) there is a good number of privacy policy URLs checked repeatedly by the user base; (2) the users are particularly interested in privacy policies of software services; and (3) PrivacyCheck increased the number of times a user consults privacy policies by 80%. Our work demonstrates the potential of ML-based privacy analysis tools and also sheds light on how these tools are used in practice to give users actionable knowledge they can use to pro-actively protect their privacy.

Smart meters (SMs) play a pivotal rule in the smart grid by being able to report the electricity usage of consumers to the utility provider (UP) almost in real-time. However, this could leak sensitive information about the consumers to the UP or a third-party. Recent works have leveraged the availability of energy storage devices, e.g., a rechargeable battery (RB), in order to provide privacy to the consumers with minimal additional energy cost. In this paper, a privacy-cost management unit (PCMU) is proposed based on a model-free deep reinforcement learning algorithm, called deep double Q-learning (DDQL). Empirical results evaluated on actual SMs data are presented to compare DDQL with the state-of-the-art, i.e., classical Q-learning (CQL). Additionally, the performance of the method is investigated for two concrete cases where attackers aim to infer the actual demand load and the occupancy status of dwellings. Finally, an abstract information-theoretic characterization is provided.

With the increasing popularity and adoption of IoT technology, fog computing has been used as an advancement to cloud computing. Although trust management issues in cloud have been addressed, there are still very few studies in a fog area. Trust is needed for collaborating among fog nodes and trust can further improve the reliability by assisting in selecting the fog nodes to collaborate. To address this issue, we present a provenance based trust mechanism that traces the behavior of the process among fog nodes. Our approach adopts the completion rate and failure rate as the process provenance in trust scores of computing workload, especially obvious measures of trustworthiness. Simulation results demonstrate that the proposed system can effectively be used for collaboration in a fog environment.

Bluetooth technologies have widespread applications in personal area networks, device-to-device communications and forming ad hoc networks. Studying Bluetooth devices security is a challenging task as they lack support for monitor mode available with other wireless networks (e.g. 802.11 WiFi). In addition, the frequency-hoping spread spectrum technique used in its operation necessitates special hardware and software to study its operation. This investigation examines methods for analyzing Bluetooth devices' security and presents a proof-of-concept DoS attack on the Link Manager Protocol (LMP) layer using the InternalBlue framework. Through this study, we demonstrate a method to study Bluetooth device security using existing tools without requiring specialized hardware. Consequently, the methods proposed in the paper can be used to study Bluetooth security in many applications.

Sometimes a security-critical decision must be made using information provided by peers. Think of routing messages, user reports, sensor data, navigational information, blockchain updates. Attackers manifest as peers that strategically report fake information. Trust models use the provided information, and attempt to suggest the correct decision. A model that appears accurate by empirical evaluation of attacks may still be susceptible to manipulation. For a security-critical decision, it is important to take the entire attack space into account. Therefore, we define the property of robustness: the probability of deciding correctly, regardless of what information attackers provide. We introduce the notion of realisations of honesty, which allow us to bypass reasoning about specific feedback. We present two schemes that are optimally robust under the right assumptions. The “majority-rule” principle is a special case of the other scheme which is more general, named “most plausible realisations”.

In this work, novel metaheuristic algorithms are proposed to address the network coding (NC)-based routing and spectrum allocation (RSA) problem in elastic optical networks, aiming to increase the level of security against eavesdropping attacks for the network's confidential connections. A modified simulated annealing, a genetic algorithm, as well as a combination of the two techniques are examined in terms of confidentiality and spectrum utilization. Performance results demonstrate that using metaheuristic techniques can improve the performance of NC-based RSA algorithms and thus can be utilized in real-world network scenarios.

Advanced Persistent Threats (APTs) are stealthy, sophisticated, long-term, multi-stage attacks that threaten the security of sensitive information. Dynamic Information Flow Tracking (DIFT) has been proposed as a promising mechanism to detect and prevent various cyber attacks in computer systems. DIFT tracks suspicious information flows in the system and generates security analysis when anomalous behavior is detected. The number of information flows in a system is typically large and the amount of resources (such as memory, processing power and storage) required for analyzing different flows at different system locations varies. Hence, efficient use of resources is essential to maintain an acceptable level of system performance when using DIFT. On the other hand, the quickest detection of APTs is crucial as APTs are persistent and the damage caused to the system is more when the attacker spends more time in the system. We address the problem of detecting APTs and model the trade-off between resource efficiency and quickest detection of APTs. We propose a game model that captures the interaction of APT and a DIFT-based defender as a two-player, multi-stage, zero-sum, Stackelberg semi-Markov game. Our game considers the performance parameters such as false-negatives generated by DIFT and the time required for executing various operations in the system. We propose a two-time scale Q-learning algorithm that converges to a Stackelberg equilibrium under infinite horizon, limiting average payoff criteria. We validate our model and algorithm on a real-word attack dataset obtained using Refinable Attack INvestigation (RAIN) framework.

Recently, the Internet of Things (IoT) is growing rapidly. IoT sensors are attached to various devices, and information is detected, collected and utilized through various wired and wireless communication environments. As the IoT is used in various places, IoT devices face a variety of malicious attacks such as MITM and reverse engineering. To prevent these, encryption is required for device-to-device communication, and keys required for encryption must be properly managed. We propose a scheme to generate seed needed for key generation and a scheme to manage the public key using blockchain.

Security has become a major concern for women, children and even elders in every walk of their life. Women are getting assaulted and molested, children are getting kidnapped, elder citizens are also facing many problems like robbery, etc. In this paper, a smart security solution called smart wearable device system is implemented using the Raspberry Pi3 for enhancing the safety and security of women/children. It works as an alert as well as a security system. It provides a buzzer alert alert to the people who are nearby to the user (wearing the smart device). The system uses Global Positioning System (GPS) to locate the user, sends the location of the user through SMS to the emergency contact and police using the Global System for Mobile Communications (GSM) / General Radio Packet Service (GPRS) technology. The device also captures the image of the assault and surroundings of the user or victim using USB Web Camera interfaced to the device and sends it as an E-mail alert to the emergency contact soon after the user presses the panic button present on Smart wearable device system.

We propose thrombolysis using a magnetic nanoparticle microswarm with tissue plasminogen activator (tPA) under ultrasound imaging. The microswarm is generated in blood using an oscillating magnetic field and can be navigated with locomotion along both the long and short axis. By modulating the input field, the aspect ratio of the microswarm can be reversibly tuned, showing the ability to adapt to different confined environments. Simulation results indicate that both in-plane and out-of-plane fluid convection are induced around the microswarm, which can be further enhanced by tuning the aspect ratio of the microswarm. Under ultrasound imaging, the microswarm is navigated in a microchannel towards a blood clot and deformed to obtain optimal lysis. Experimental results show that the lysis rate reaches -0.1725 ± 0.0612 mm3/min in the 37°C blood environment under the influence of the microswarm-induced fluid convection and tPA. The lysis rate is enhanced 2.5-fold compared to that without the microswarm (-0.0681 ± 0.0263 mm3/min). Our method provides a new strategy to increase the efficiency of thrombolysis by applying microswarm-induced fluid convection, indicating that swarming micro/nanorobots have the potential to act as effective tools towards targeted therapy.

The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing. Available communication resources (frequency-time blocks and transmit powers) and computational resources (processor usage) are allocated to stochastic arrivals of network slice requests. Each request arrives with priority (weight), throughput, computational resource, and latency (deadline) requirements, and if feasible, it is served with available communication and computational resources allocated over its requested duration. As each decision of resource allocation makes some of the resources temporarily unavailable for future, the myopic solution that can optimize only the current resource allocation becomes ineffective for network slicing. Therefore, a Q-learning solution is presented to maximize the network utility in terms of the total weight of granted network slicing requests over a time horizon subject to communication and computational constraints. Results show that reinforcement learning provides major improvements in the 5G network utility relative to myopic, random, and first come first served solutions. While reinforcement learning sustains scalable performance as the number of served users increases, it can also be effectively used to assign resources to network slices when 5G needs to share the spectrum with incumbent users that may dynamically occupy some of the frequency-time blocks.

An increasing number of industries around the world are adopting advance manufacturing technologies for product design, among which additive manufacturing (AM) is gaining attention among aerospace, defense, automotive and health care domains. Products with complicated designs demanding lesser weight, improved performance and conformance are manufactured by companies using AM technologies. Some noticeable examples of ducting, airflow system and vent products in the aerospace domain can be seen being made out of AM techniques. One of the benefits being mentioned is the significant reduction in the number of components going into a finished product, thereby impacting the supply chain as well. However, one of the challenges in AM process is to reduce the process variation which affects the reliability of the product. To realize the true benefits of additively manufactured products, it is imperative to ensure that the reliability of AM products is similar or better than traditionally manufactured products. Current state of art for assessing reliability of traditionally manufactured products is mature. However, the reliability assessment framework for products manufactured by advanced technologies are being studied upon. In this direction, this paper highlights a structured reliability assessment framework for additive manufactured products, which will help in identifying, analyzing and mitigating reliability risks as part of product development life cycle.

The advanced complex electronic systems increasingly demand safer and more secure hardware parts. Correspondingly, fault injection became a major verification milestone for both safety- and security-critical applications. However, fault injection campaigns for gate-level designs suffer from huge execution times. Therefore, designers need to apply early design evaluation techniques to reduce the execution time of fault injection campaigns. In this work, we propose a method to represent gate-level Single-Event Transient (SET) faults by multiple Single-Event Upset (SEU) faults at the Register-Transfer Level. Introduced approach is to identify true and false logic paths for each SET in the flip-flops' fan-in logic cones to obtain more accurate sets of flip-flops for multiple SEUs injections at RTL. Experimental results demonstrate the feasibility of the proposed method to successfully reduce the fault space and also its advantage with respect to state of the art. It was shown that the approach is able to reduce the fault space, and therefore the fault-injection effort, by up to tens to hundreds of times.

In order to accurately extract the data from unstructured Chinese text, this paper proposes a rule-based method based on natural language processing and regular expression. This method makes use of the language expression rules of the data in the text and other related knowledge to form the feature word lists and rule template to match the text. Experimental results show that the accuracy of the designed algorithm is 94.09%.

Using artificial intelligence technology to help network security has become a major trend. At present, major countries in the world have successively invested R & D force in the attack and defense of automatic network based on artificial intelligence. The U.S. Navy, the U.S. air force, and the DOD strategic capabilities office have invested heavily in the development of artificial intelligence network defense systems. DARPA launched the network security challenge (CGC) to promote the development of automatic attack system based on artificial intelligence. In the 2016 Defcon final, mayhem (the champion of CGC in 2014), an automatic attack team, participated in the competition with 14 human teams and once defeated two human teams, indicating that the automatic attack method generated by artificial intelligence system can scan system defects and find loopholes faster and more effectively than human beings. Japan's defense ministry also announced recently that in order to strengthen the ability to respond to network attacks, it will introduce artificial intelligence technology into the information communication network defense system of Japan's self defense force. It can be predicted that the deepening application of artificial intelligence in the field of network attack and defense may bring about revolutionary changes and increase the imbalance of the strategic strength of cyberspace in various countries. Therefore, it is necessary to systematically investigate the current situation of network attack and defense based on artificial intelligence at home and abroad, comprehensively analyze the development trend of relevant technologies at home and abroad, deeply analyze the development outline and specification of artificial intelligence attack and defense around the world, and refine the application status and future prospects of artificial intelligence attack and defense, so as to promote the development of artificial intelligence attack and Defense Technology in China and protect the core interests of cyberspace, of great significance

With the continuous popularization of smart terminals, Android and IOS systems are the most mainstream mobile operating systems in the market, and their application types and application numbers are constantly increasing. As an open system, the security issues of Android application emerge in endlessly, such as the reverse decompilation of installation package, malicious code injection, application piracy, interface hijacking, SMS hijacking and input monitoring. These security issues will also appear on mobile applications in the power industry, which will not only result in the embezzlement of applied knowledge copyrights but also lead to serious leakage of users' information and even economic losses. It may even result in the remote malicious control of key facilities, which will cause serious social issues. Under the background of the development of smart grid information construction, also with the application and promotion of power services in mobile terminals, information security protection for mobile terminal applications and interactions with the internal system of the power grid has also become an important research direction. While analyzing the risks faced by mobile applications, this article also enumerates and analyzes the necessary measures for risk resolution.

Traditional downlink (DL)-uplink (UL) coupled cell association scheme is suboptimal solution for user association as most of the users are associated to a high powered macro base station (MBS) compared to low powered small base station (SBS) in heterogeneous network. This brings challenges like multiple interference issues, imbalanced user traffic load which leads to a degraded throughput in HetNet. In this paper, we investigate DL-UL decoupled cell association scheme to address these challenges and formulate a sum-rate maximization problem in terms of admission control, cell association and power allocation for MBS only, coupled and decoupled HetNet. The formulated optimization problem falls into a class of mixed integer non linear programming (MINLP) problem which is NP-hard and requires an exhaustive search to find the optimal solution. However, computational complexity of the exhaustive search increases exponentially with the increase in number of users. Therefore, an outer approximation algorithm (OAA), with less complexity, is proposed as a solution to find near optimal solution. Extensive simulations work have been done to evaluate proposed algorithm. Results show effectiveness of proposed novel decoupled cell association scheme over traditional coupled cell association scheme in terms of users associated/attached, mitigating interference, traffic offloading to address traffic imbalances and sum-rate maximization.

Explainable artificial intelligence (XAI) methods rely on access to model architecture and parameters that is not always feasible for most users, practitioners, and regulators. Inspired by cognitive psychology, we present a case for response times (RTs) as a technique for XAI. RTs are observable without access to the model. Moreover, dynamic inference models performing conditional computation generate variable RTs for visual learning tasks depending on hierarchical representations. We show that MSDNet, a conditional computation model with early-exit architecture, exhibits slower RT for images with more complex features in the ObjectNet test set, as well as the human phenomenon of scene grammar, where object recognition depends on intrascene object-object relationships. These results cast light on MSDNet's feature space without opening the black box and illustrate the promise of RT methods for XAI.

Mobile Forensics (MF) field uses prescribed scientific approaches with a focus on recovering Potential Digital Evidence (PDE) from mobile devices leveraging forensic techniques. Consequently, increased proliferation, mobile-based services, and the need for new requirements have led to the development of the MF field, which has in the recent past become an area of importance. In this article, the authors take a step to conduct a review on Mobile Forensics Investigation Process Models (MFIPMs) as a step towards uncovering the MF transitions as well as identifying open and future challenges. Based on the study conducted in this article, a review of the literature revealed that there are a few MFIPMs that are designed for solving certain mobile scenarios, with a variety of concepts, investigation processes, activities, and tasks. A total of 100 MFIPMs were reviewed, to present an inclusive and up-to-date background of MFIPMs. Also, this study proposes a Harmonized Mobile Forensic Investigation Process Model (HMFIPM) for the MF field to unify and structure whole redundant investigation processes of the MF field. The paper also goes the extra mile to discuss the state of the art of mobile forensic tools, open and future challenges from a generic standpoint. The results of this study find direct relevance to forensic practitioners and researchers who could leverage the comprehensiveness of the developed processes for investigation.

The chapter presents a review of proactive Moving Target Defense (MTD) paradigm and investigates the feasibility and potential of specific MTD approaches for the resource‐constrained Internet of Things (IoT) applications. The aim is not only to provide taxonomy of various MTD approaches but also to advocate MTD techniques in the dynamic network domain in conjunction with the emerging Software Defined Networking (SDN) for more effective proactive IoT defense. The Internet of Battlefield Things (IoBT) and Industrial IoT (IIoT), which subject to more attacks, are identified as two critical IoT domains that can reap from the SDN‐based MTD approaches. Finally, the chapter also discusses potential future research challenges of the MTD approaches in the IoT domain.

One of the emerging methodologies nowadays in the field of cryptography based on human DNA sequences. As the research says that even a limited quantity of DNA can store gigantic measure of information likewise DNA can process and transmit the information, such potential of DNA give rise to the idea of DNA cryptography. A synopsis of the research carried out in DNA based security presented in this paper. Included deliberation contain encryption algorithms based on random DNA, chaotic systems, polymerase chain reaction, coupled map lattices, and other common encryption algorithms. Purpose of algorithms are specific or general as some of them are only designed to encrypt the images or more specific images like medical images or text data and others designed to use it as general for images and text data. We discussed divergent techniques that proposed earlier based on random sample DNA, medical image encryption, image encryption, and cryptanalysis done on various algorithms. With the help of this paper, one can understand the existing algorithms and can design a DNA based encryption algorithm.

Compressive Sensing (CS) is a promising technology for the acquisition of signals. The number of measurements is reduced by using CS which is needed to obtain the signals in some basis that are compressible or sparse. The compressible or sparse nature of the signals can be obtained by transforming the signals in some domain. Depending on the signals sparsity signals are sampled below the Nyquist sampling criteria by using CS. An optimization problem needs to be solved for the recovery of the original signal. Very few studies have been reported about the reconstruction of the signals. Therefore, in this paper, the reconstruction algorithms are elaborated systematically for sparse signal recovery in CS. The discussion of various reconstruction algorithms in made in this paper will help the readers in order to understand these algorithms efficiently.

Peer-to-peer (P2P) systems such as BitTorrent and Bitcoin are susceptible to serious attacks from byzantine nodes that join as peers. Due to well-known impossibility results for designing P2P primitives in unrestricted byzantine settings, research has explored many adversarial models with additional assumptions, ranging from mild (such as pre-established PKI) to strong (such as the existence of common random coins). One such widely-studied model is the general-omission model, which yields simple protocols with good efficiency, but has been considered impractical or unrealizable since it artificially limits the adversary only to omitting messages.In this work, we study the setting of a synchronous network wherein peer nodes have CPUs equipped with a recent trusted computing mechanism called Intel SGX. In this model, we observe that the byzantine adversary reduces to the adversary in the general-omission model. As a first result, we show that by leveraging SGX features, we eliminate any source of advantage for a byzantine adversary beyond that gained by omitting messages, making the general-omission model realizable. Our evaluation of 1000 nodes running on 40 DeterLab machines confirms theoretical efficiency claim.

Computing capabilities such as real time data, unlimited connection, data from sensors, environmental analysis, automated decisions (machine learning) are demanded by many areas like industry for example decision making, machine learning, by research and military, for example GPS, sensor data collection. The possibility to make these features compatible with each domain that demands them is known as ubiquitous computing. Ubiquitous computing includes network topologies such as wireless sensor networks (WSN) which can help further improving the existing communication, for example the Internet. Also, ubiquitous computing is included in the Internet of Things (IoT) applications. In this article, it is discussed the mobility of WSN and its advantages and innovations, which make possible implementations for smart home and office. Knowing the growing number of mobile users, we place the mobile phone as the key factor of the future ubiquitous wireless networks. With secure computing, communicating, and storage capacities of mobile devices, they can be taken advantage of in terms of architecture in the sense of scalability, energy efficiency, packet delay, etc. Our work targets to present a structure from a ubiquitous computing point of view for researchers who have an interest in ubiquitous computing and want to research on the analysis, to implement a novel method structure for the ubiquitous computing system in military sectors. Also, this paper presents security and privacy issues in ubiquitous sensor networks (USN).