Biblio

Cryptographic (crypto) algorithms are the essential ingredients of all secure systems: crypto hash functions and encryption algorithms, for example, can guarantee properties such as integrity and confidentiality. Developers, however, can misuse the application programming interfaces (API) of such algorithms by using constant keys and weak passwords. This paper presents CRYLOGGER, the first open-source tool to detect crypto misuses dynamically. CRYLOGGER logs the parameters that are passed to the crypto APIs during the execution and checks their legitimacy offline by using a list of crypto rules. We compared CRYLOGGER with CryptoGuard, one of the most effective static tools to detect crypto misuses. We show that our tool complements the results of CryptoGuard, making the case for combining static and dynamic approaches. We analyzed 1780 popular Android apps downloaded from the Google Play Store to show that CRYLOGGER can detect crypto misuses on thousands of apps dynamically and automatically. We reverse-engineered 28 Android apps and confirmed the issues flagged by CRYLOGGER. We also disclosed the most critical vulnerabilities to app developers and collected their feedback.

With the continuous development of Internet technology, people pay more and more attention to private security. In particular, third-party tracking is a major factor affecting privacy security. So far, the most effective way to prevent third-party tracking is to create a blacklist. However, blacklist generation and maintenance need to be carried out manually which is inefficient and difficult to maintain. In order to generate blacklists more quickly and accurately in this era of big data, this paper proposes a machine learning system MFTrackerDetector against third-party tracking. The system is based on the theory of structural hole and only detects third-party trackers. The system consists of two subsystems, DMTrackerDetector and DFTrackerDetector. DMTrackerDetector is a JavaScript-based subsystem and DFTrackerDetector is a Flash-based subsystem. Because tracking code and non-tracking code often call different APIs, DMTrackerDetector builds a classifier using all the APIs in JavaScript as features and extracts the API features in JavaScript through dynamic analysis. Unlike static analysis method, the dynamic analysis method can effectively avoid code obfuscation. DMTrackerDetector eventually generates a JavaScript-based third-party tracker list named Jlist. DFTrackerDetector constructs a classifier using all the APIs in ActionScript as features and extracts the API features in the flash script through static analysis. DFTrackerDetector finally generates a Flash-based third-party tracker list named Flist. DFTrackerDetector achieved 92.98% accuracy in the Flash test set and DMTrackerDetector achieved 90.79% accuracy in the JavaScript test set. MFTrackerDetector eventually generates a list of third-party trackers, which is a combination of Jlist and Flist.

Smart substation and Vehicular Ad-Hoc Network (VANET) are two important applications of aggregate signature scheme. Due to the large number of data collection equipment in substation, it needs security authentication and integrity protection to transmit data. Similarly, in VANET, due to limited resources, it has the needs of privacy protection and improving computing efficiency. Aggregate signature scheme can satisfy the above these needs and realize one-time verification of signature for multi-terminal data collection which can improve the performance. Aggregate signature scheme is an important technology to solve network security problem. Recently, many aggregate signature schemes are proposed which can be applied in smart grid or VANET. In this paper, we present two security analyses on two aggregate signature schemes proposed recently. By analysis, it shows that the two aggregate signature schemes do not satisfy the security property of unforgeability. A malicious user can forge a signature on any message. We also present some improved methods to solve these security problems with better performance. From security analysis to improvement of aggregate signature scheme, it is very suitable to be an instance to exhibit the students on designing of security aggregate signature scheme for network security education or course.

Demand response (DR) is one of the most economical methods for peak demand reduction, renewable energy integration and ancillary service support. Residential electrical energy consumption takes approximately 33% of the total electricity usage and hence has great potentials in DR applications. However, residential DR encounters various challenges such as small individual magnitude, stochastic consuming patterns and privacy issues. In this study, we propose a stochastic optimal mechanism to tackle these issues and try to reveal the benefits from residential DR implementation. Stochastic residential load (SRL) models, a generation cost prediction (GCP) model and a stochastic optimal load aggregation (SOLA) model are developed. A set of uniformly distributed scalers is introduced into the SOLA model to efficiently avoid the peak demand rebound problem in DR applications. The SOLA model is further transformed into a deterministic LP model. Time-of-Use (TOU) tariff is adopted as the price structure because of its similarity and popularity. Case studies show that the proposed mechanism can significantly reduce the peak-to-average power ratio (PAPR) of the load profile as well as the electrical energy cost. Furthermore, the impacts of consumers' participation levels in the DR program are investigated. Simulation results show that the 50% participation level appears as the best case in terms system stability. With the participation level of 80%, consumers' electrical energy cost is minimized. The proposed mechanism can be used by a residential load aggregator (LA) or a utility to plan a DR program, predict its impacts, and aggregate residential loads to minimize the electrical energy cost.

As an essential part to intelligently fine-grained scheduling, planning and maintenance in smart grid and energy internet, short-term load forecasting makes great progress recently owing to the big data collected from smart meters and the leap forward in machine learning technologies. However, the centralized computing topology of classical electric information system, where individual electricity consumption data are frequently transmitted to the cloud center for load forecasting, tends to violate electric consumers' privacy as well as to increase the pressure on network bandwidth. To tackle the tricky issues, we propose a privacy-preserving framework based on the edge-fog-cloud continuum for smart grid. Specifically, 1) we gravitate the training of load forecasting models and forecasting workloads to distributed smart meters so that consumers' raw data are handled locally, and only the forecasting outputs that have been protected are reported to the cloud center via fog nodes; 2) we protect the local forecasting models that imply electricity features from model extraction attacks by model randomization; 3) we exploit a shuffle scheme among smart meters to protect the data ownership privacy, and utilize a re-encryption scheme to guarantee the forecasting data privacy. Finally, through comprehensive simulation and analysis, we validate our proposed privacy-preserving framework in terms of privacy protection, and computation and communication efficiency.

This study presents an open standards-based information system supporting democratisation and consumer empowerment through flexibility activation. This study describes a functional technical reference infrastructure: a secure, standard-based and viable communication backbone for flexibility activation. The infrastructure allows connection, registering, activation and reporting for different types of granular consumer flexibility. The flexibility sources can be directly controllable set points of chargers and stationary batteries, as well as controllable loads. The proposed communication system sees all these flexibility provisions as distributed energy resources in a wider sense, and the architecture allows consumer-level integration of different energy systems. This makes new flexibility sources fully available to the balancing responsible entities in a viable and realistically implementable manner. The proposed reference architecture, as implemented in the FLEXCoop project, relies on established open standards as it is based on the Open Automated Demand Response (OpenADR) and OAuth2/OpenID standards and the corresponding IEC 62746-10 standard, and it covers interfacing towards other relevant standards. The security and access implications are addressed by the OpenID security layer built on top of the OAuth2 and integrated with the OpenADR standard. To address the data protection and privacy aspects, the architecture is designed on the least knowledge principle.

Smart meters (SMs) can pose privacy threats for consumers, an issue that has received significant attention in recent years. This paper studies the impact of Side Information (SI) on the performance of possible attacks to real-time privacy-preserving algorithms for SMs. In particular, we consider a deep adversarial learning framework, in which the desired releaser, which is a Recurrent Neural Network (RNN), is trained by fighting against an adversary network until convergence. To define the objective for training, two different approaches are considered: the Causal Adversarial Learning (CAL) and the Directed Information (DI)-based learning. The main difference between these approaches relies on how the privacy term is measured during the training process. The releaser in the CAL method, disposing of supervision from the actual values of the private variables and feedback from the adversary performance, tries to minimize the adversary log-likelihood. On the other hand, the releaser in the DI approach completely relies on the feedback received from the adversary and is optimized to maximize its uncertainty. The performance of these two algorithms is evaluated empirically using real-world SMs data, considering an attacker with access to SI (e.g., the day of the week) that tries to infer the occupancy status from the released SMs data. The results show that, although they perform similarly when the attacker does not exploit the SI, in general, the CAL method is less sensitive to the inclusion of SI. However, in both cases, privacy levels are significantly affected, particularly when multiple sources of SI are included.

In this century, the demand for energy is increasing daily, and the need for energy resources has become urgent and inevitable. New ways of generating energy, such as renewable resources that depend on many sources, including the sun and wind energy will contribute to the future of humankind largely and effectively. These renewable sources are facing major challenges that cannot be ignored which also require more researches on appropriate solutions . This has led to the emergence of a new type of network user called prosumer, which causes new challenges such as the intermittent nature of renewable. Smart grids have emerged as a solution to integrate these distributed energy sources. It also provides a mechanism to maintain safety and security for power supply networks. The main idea of smart grids is to facilitate local production and consumption By customers and consumers.Distributed ledger technology (DLT) or Block-chain technology has evolved dramatically since 2008 that coincided with the birth of its first application Bitcoin, which is the first cryptocurrency. This innovation led to sparked in the digital revolution, which provides decentralization, security, and democratization of information storage and transfer systems across numerous sectors/industries. Block-chain can be applied for the sake of the durability and safety of energy systems. In this paper, we will propose a new distributed framework that provides protection based on block-chain technology for energy systems to enhance self-defense capability against those cyber-attacks.

Load profiling is to cluster power consumption data to generate load patterns showing typical behaviors of consumers, and thus it has enormous potential applications in smart grid. However, short-interval readings would generate massive smart meter data. Although cloud computing provides an excellent choice to analyze such big data, it also brings significant privacy concerns since the cloud is not fully trustworthy. In this paper, based on a modified vector homomorphic encryption (VHE), we propose a privacy-preserving and outsourced k-means clustering scheme (PPOk M) for secure load profiling over encrypted meter data. In particular, we design a similarity-measuring method that effectively and non-interactively performs encrypted distance metrics. Besides, we present an integrity verification technique to detect the sloppy cloud server, which intends to stop iterations early to save computational cost. In addition, extensive experiments and analysis show that PPOk M achieves high accuracy and performance while preserving convergence and privacy.

Smart Meters (SM) are IoT end devices used to collect user utility consumption with limited processing power on the edge of the smart grid (SG). While SMs have great applications in providing data analysis to the utility provider and consumers, private user information can be inferred from SMs readings. For preserving user privacy, a number of methods were developed that use perturbation by adding noise to alter user load and hide consumer data. Most methods limit the amount of perturbation noise using differential privacy to preserve the benefits of data analysis. However, additive noise perturbation may have an undesirable effect on billing. Additionally, users may desire to select complete privacy without giving consent to having their data analyzed. We present a virtual battery model that uses perturbation with additive noise obtained from a virtual chargeable battery. The level of noise can be set to make user data differentially private preserving statistics or break differential privacy discarding the benefits of data analysis for more privacy. Our model uses fog aggregation with authentication and encryption that employs lightweight cryptographic primitives. We use Diffie-Hellman key exchange for symmetrical encryption of transferred data and a two-way challenge-response method for authentication.

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.

The desired features for the smart grid include dynamic billing capabilities along with consumer privacy protection. Existing aggregation-based privacy frameworks have limitations such as centralized designs prone to single points of failure and/or a high computational overload on the smart meters due to in-network aggregation or complex algorithmic operations. Additionally, these existing schemes do not consider how dynamic billing can be implemented while consumer privacy is preserved. In this paper, a cyber-resilient framework that enables dynamic billing while focusing on consumer privacy preservation is proposed. The distributed design provides a framework for spatio-temporal aggregation and keeps the process lightweight for the smart meters. The comparative analysis of our proposed work with existing work shows a significant improvement in terms of the spatial aggregation overhead, overhead on smart meters and scalability. The paper also discusses the resilience of our framework against privacy attacks.

An adversarial attack is an algorithm that perturbs the input of a machine learning model in an intelligent way in order to change the output of the model. An important property of adversarial attacks is transferability. According to this property, it is possible to generate adversarial perturbations on one model and apply it the input to fool the output of a different model. Our work focuses on studying the transferability of adversarial attacks in sound event classification. We are able to demonstrate differences in transferability properties from those observed in computer vision. We show that dataset normalization techniques such as z-score normalization does not affect the transferability of adversarial attacks and we show that techniques such as knowledge distillation do not increase the transferability of attacks.

In this paper, our aim is to detect illegal spectrum access behaviors. Firstly, we detect whether the channel is busy, and then if it is busy, recognizing whether there are illegal users. To get closer to the actual situation, we consider a more general scenario where multiple users are authorized to work on the same channel under certain interference control strategies, and build it as a ternary hypothesis test model using the generalized multi-hypothesis Neyman-Pearson criterion. Considering the various potential combination of multiple authorized users, the spectrum detection process utilizes a two-step detector. We adopt the Generalized Likelihood Ratio Test (GLRT) and the Rao test to detect illegal spectrum access behaviors. What is more, the Wald test is proposed which has a compromise between computational complexity and performance. The relevant formulas of the three detection schemes are derived. Finally, comprehensive and in-depth simulations are provided to verify the effectiveness of the proposed detection scheme that it has the best detection performance under different authorized sample numbers and different performance constraints. Besides, we illustrate the probability of detection of illegal behaviors under different parameters of illegal behaviors and different sets of AUs' states under the Wald test.

This article investigates the beamforming method selection in artificial-noise-aid (AN-aid) multiuser multiple-input-single-output (MU-MISO) broadcast wiretap systems in slow fading channel environment. We adopt beamforming pre-coding matrix with artificial noise to achieve secure multiuser communication and optimize system performance, and compare the secure transmission performance of two beamforming methods. To overcome the complexity of this model, a novel optimization scheme expressed using semi-closed-form expressions and Monte Carlo method is employed to derive the relationship between transmission parameters and secure transmission performance. This scheme would help us to analyses performance of different beamforming methods.

The following topics are dealt with: diseases; medical signal processing; learning (artificial intelligence); security of data; blood; patient treatment; patient monitoring; bioelectric phenomena; biomedical electrodes; biological tissues.

This paper studies the security issue of a gossip-based distributed projected gradient (DPG) algorithm, when it is applied for solving a decentralized multi-agent optimization. It is known that the gossip-based DPG algorithm is vulnerable to insider attacks because each agent locally estimates its (sub)gradient without any supervision. This work leverages the convolutional neural network (CNN) to perform the detection and localization of the insider attackers. Compared to the previous work, CNN can learn appropriate decision functions from the original state information without preprocessing through artificially designed rules, thereby alleviating the dependence on complex pre-designed models. Simulation results demonstrate that the proposed CNN-based approach can effectively improve the performance of detecting and localizing malicious agents, as compared with the conventional pre-designed score-based model.

In this paper, an E-band MIMO radar with 1 transmit and 4 receive channels is designed. The signal bandwidth is 2GHz at 77GHz, the max power of transmitted signal which is Frequency-modulated continuous-wave (FMCW) is 13dBm. This radar consists of two cascade parts: RF frond-end and digital signal process block. The RF front-end part includes antenna array, millimeter wave transceiver chips, and the digital signal process part includes FPGA, DSP and power supply circuits. It could be used in foreign object detection (FOD), landing assistance of helicopter and security checking.

The use of wireless connectivity has increased exponentially in recent years. Fifth generation (5G) communications will soon be deployed worldwide. Six-generation (6G) communications vision and planning have begun, and the use of 6G communications is expected to begin in the 2030s. The 6G system has higher capacity, higher data rates, lower latency, higher security and better quality of service (QoS) compared to the 5G system. This paper presents a brief overview on the vision and requirements of 6G wireless communications and networks. Finally, some of the challenges in launching the 6G are also explained.

Transmit beamforming and artificial noise (AN) methods have been widely employed to achieve wireless physical layer (PHY) secure transmissions. While most works focus on transmit beamforming optimization, little attention is paid to the design of artificial noise projection matrix (ANPM). In this paper, compared with traditional ANPM obtained by zero-forcing method, which only makes AN power uniform distribution in free space outside legitimate users (LU) locations, we design ANPM to maximize the interference on eavesdroppers without interference on LUs for multicast directional modulation (MCDM) scenario based on frequency diverse array (FDA). Furthermore, we extend our approach to the case of with imperfect locations of Eves. Finally, simulation results show that Eves can be seriously affected by the AN with perfect/imperfect locations, respectively.

Intentional interference such as spoofing is an emerging threat to GPS receivers used in both civilian and defense applications. With the majority of smartphones relying on GPS for positioning and navigation, the vulnerability of these phones to spoofing attacks is an issue of security concern. In this paper, it is demonstrated that is easy to successfully spoof a smartphone using a simplistic spoofing technique. A spoofing signal is generated using open-source signal simulator and transmitted using a low-cost SDR. In view of the tremendously increasing usage of GPS enabled smartphones, it is necessary to develop suitable countermeasures for spoofing. This work carries significance as it would help in understanding the effects of spoofing at various levels of signal processing in the receiver and develop advanced spoofing detection and mitigation techniques.

Formal languages are ubiquitous wherever software systems need to exchange or store data. Unparsing into and parsing from such languages is an error-prone process that has spawned an entire class of security vulnerabilities. There has been ample research into finding vulnerabilities on the parser side, but outside of language specific approaches, few techniques targeting unparser vulnerabilities exist. This work presents a language-agnostic approach for spotting injection vulnerabilities in unparsers. It achieves this by mining unparse trees using dynamic taint analysis to extract language keywords, which are leveraged for guided fuzzing. Vulnerabilities can thus be found without requiring prior knowledge about the formal language, and in fact, the approach is even applicable where no specification thereof exists at all. This empowers security researchers and developers alike to gain deeper understanding of unparser implementations through examination of the unparse trees generated by the approach, as well as enabling them to find new vulnerabilities in poorly-understood software. This work presents a language-agnostic approach for spotting injection vulnerabilities in unparsers. It achieves this by mining unparse trees using dynamic taint analysis to extract language keywords, which are leveraged for guided fuzzing. Vulnerabilities can thus be found without requiring prior knowledge about the formal language, and in fact, the approach is even applicable where no specification thereof exists at all. This empowers security researchers and developers alike to gain deeper understanding of unparser implementations through examination of the unparse trees generated by the approach, as well as enabling them to find new vulnerabilities in poorly-understood software.

Security vetting of Android applications is one of the crucial aspects of the Android ecosystem. Regarding the state of the art tools for this goal, most of them doesn't consider analyzing native codes and only analyze the Java code. However, Android concedes its developers to implement a part or all of their applications using C or C++ code. Thus, applying conservative manners for analyzing Android applications while ignoring native codes would lead to less precision in results. Few works have tried to analyze Android native codes, but only JN-SAF has applied taint analysis using static techniques such as symbolic execution. However, symbolic execution has some problems when is used in large programs. One of these problems is the exponential growth of program paths that would raise the path explosion issue. In this work, we have tried to alleviate this issue by introducing our new tool named CTAN. CTAN applies new symbolic execution methods to angr in a particular way that it can make JN-SAF more efficient and faster. We have introduced compositional taint analysis in CTAN by combining satisfiability modulo theories with symbolic execution. Our experiments show that CTAN is 26 percent faster than its previous work JN-SAF and it also leads to more precision by detecting more data-leakage in large Android native codes.

Technology and the use of online services are very prevalent across much of our everyday lives. As our digital interactions continue to grow, there is a need to improve public awareness of the risks to our personal online privacy and security. Designing for cyber security awareness has never been so important. In this work, we consider people's current impressions towards their privacy and security online. We also explore how abnormal network activity data can be visually conveyed to afford a heightened cyber security awareness. In detail, the paper documents the different effects of visual variables in an edge and node DoS visualisation to depict abnormally high volumes of traffic. The results from two studies show that people are generally becoming more concerned about their privacy and security online. Moreover, we have found that the more focus based visual techniques (i.e. blur) and geometry-based techniques (i.e. jaggedness and sketchiness) afford stronger impressions of uncertainty from abnormally high volumes of network traffic. In terms of security, these impressions and feelings alert in the end-user that something is not quite as it should be and hence develop a heightened cyber security awareness.

Smart cities and societies are driving unprecedented technological and socioeconomic growth in everyday life albeit making us increasingly vulnerable to infinitely and incomprehensibly diverse threats. Short Message Service (SMS) spam is one such threat that can affect mobile security by propagating malware on mobile devices. A security breach could also cause a mobile device to send spam messages. Many works have focused on classifying incoming SMS messages. This paper proposes a tool to detect spam from outgoing SMS messages, although the work can be applied to both incoming and outgoing SMS messages. Specifically, we develop a system that comprises multiple machine learning (ML) based classifiers built by us using three classification methods – Naïve Bayes (NB), Support Vector Machine (SVM), and Naïve Bayes Multinomial (NBM)- and five preprocessing and feature extraction methods. The system is built to allow its execution in cloud, fog or edge layers, and is evaluated using 15 datasets built by 4 widely-used public SMS datasets. The system detects spam SMSs and gives recommendations on the spam filters and classifiers to be used based on user preferences including classification accuracy, True Negatives (TN), and computational resource requirements.