Biblio
A new approach to micro-Doppler signal analysis is presented in this article. Novel chirp rate estimators in the time-frequency domain were used for this purpose, which provided the chirp rate of micro-Doppler signatures, allowing the classification of objects in the urban environment. As an example verifying the method, a signal from a high-resolution radar with a linear frequency modulated continuous wave (FMCW) recording an echo reflected from a pedestrian was used to validate the proposed algorithms for chirp rate estimation. The obtained results are plotted on saturated accelerograms, giving an additional parameter dedicated for target classification in security systems utilizing radar sensors for target detection.
Most Web sites rely on resources hosted by third parties such as CDNs. Third parties may be compromised or coerced into misbehaving, e.g. delivering a malicious script or stylesheet. Unexpected changes to resources hosted by third parties can be detected with the Subresource Integrity (SRI) mechanism. The focus of SRI is on scripts and stylesheets. Web fonts cannot be secured with that mechanism under all circumstances. The first contribution of this paper is to evaluates the potential for attacks using malicious fonts. With an instrumented browser we find that (1) more than 95% of the top 50,000 Web sites of the Tranco top list rely on resources hosted by third parties and that (2) only a small fraction employs SRI. Moreover, we find that more than 60% of the sites in our sample use fonts hosted by third parties, most of which are being served by Google. The second contribution of the paper is a proof of concept of a malicious font as well as a tool for automatically generating such a font, which targets security-conscious users who are used to verifying cryptographic fingerprints. Software vendors publish such fingerprints along with their software packages to allow users to verify their integrity. Due to incomplete SRI support for Web fonts, a third party could force a browser to load our malicious font. The font targets a particular cryptographic fingerprint and renders it as a desired different fingerprint. This allows attackers to fool users into believing that they download a genuine software package although they are actually downloading a maliciously modified version. Finally, we propose countermeasures that could be deployed to protect the integrity of Web fonts.
It is important to provide strong security for IoT devices with limited security related resources. We introduce a new dynamic security agent management framework, which dynamically chooses the best security agent to support security functions depending on the applications' security requirements of IoT devices in the system. This framework is designed to overcome the challenges including high computation costs, multiple security protocol compatibility, and efficient energy management in IoT system.
Cross-modal hashing, which searches nearest neighbors across different modalities in the Hamming space, has become a popular technique to overcome the storage and computation barrier in multimedia retrieval recently. Although dozens of cross-modal hashing algorithms are proposed to yield compact binary code representation, applying exhaustive search in a large-scale dataset is impractical for the real-time purpose, and the Hamming distance computation suffers inaccurate results. In this paper, we propose a novel index scheme over binary hash codes in cross-modal retrieval. The proposed indexing scheme exploits a few binary bits of the hash code as the index code. Based on the index code representation, we construct an inverted index structure to accelerate the retrieval efficiency and train a neural network to improve the indexing accuracy. Experiments are performed on two benchmark datasets for retrieval across image and text modalities, where hash codes are generated by three cross-modal hashing methods. Results show the proposed method effectively boosts the performance over the benchmark datasets and hash methods.
This research conducted a security evaluation website with Penetration Testing terms. This Penetration testing is performed using the Man-In-The-Middle Attack method. This method is still widely used by hackers who are not responsible for performing Sniffing, which used for tapping from a targeted computer that aims to search for sensitive data. This research uses some penetration testing techniques, namely SQL Injection, XSS (Cross-site Scripting), and Brute Force Attack. Penetration testing in this study was conducted to determine the security hole (vulnerability), so the company will know about their weakness in their system. The result is 85% success for the penetration testing that finds the vulnerability on the website.
The proposed combination of statistical methods has proved efficient for authorship attribution. The complex analysis method based on the proposed combination of statistical methods has made it possible to minimize the number of phoneme groups by which the authorial differentiation of texts has been done.
The amount of connected devices in the industrial environment is growing continuously, due to the ongoing demands of new features like predictive maintenance. New business models require more data, collected by IIoT edge node sensors based on inexpensive and low performance Microcontroller Units (MCUs). A negative side effect of this rise of interconnections is the increased attack surface, enabled by a larger network with more network services. Attaching badly documented and cheap devices to industrial networks often without permission of the administrator even further increases the security risk. A decent method to monitor the network and detect “unwanted” devices is network scanning. Typically, this scanning procedure is executed by a computer or server in each sub-network. In this paper, we introduce network scanning and mapping as a building block to scan directly from the Industrial Internet of Things (IIoT) edge node devices. This module scans the network in a pseudo-random periodic manner to discover devices and detect changes in the network structure. Furthermore, we validate our approach in an industrial testbed to show the feasibility of this approach.
With the rapid progression of Information and Communication Technology (ICT) and especially of Internet of Things (IoT), the conventional electrical grid is transformed into a new intelligent paradigm, known as Smart Grid (SG). SG provides significant benefits both for utility companies and energy consumers such as the two-way communication (both electricity and information), distributed generation, remote monitoring, self-healing and pervasive control. However, at the same time, this dependence introduces new security challenges, since SG inherits the vulnerabilities of multiple heterogeneous, co-existing legacy and smart technologies, such as IoT and Industrial Control Systems (ICS). An effective countermeasure against the various cyberthreats in SG is the Intrusion Detection System (IDS), informing the operator timely about the possible cyberattacks and anomalies. In this paper, we provide an anomaly-based IDS especially designed for SG utilising operational data from a real power plant. In particular, many machine learning and deep learning models were deployed, introducing novel parameters and feature representations in a comparative study. The evaluation analysis demonstrated the efficacy of the proposed IDS and the improvement due to the suggested complex data representation.
In this study we propose a novel method for drone surveillance that can simultaneously analyze time-frequency responses in all pixels of a high-frame-rate video. The propellers of flying drones rotate at hundreds of Hz and their principal vibration frequency components are much higher than those of their background objects. To separate the pixels around a drone's propellers from its background, we utilize these time-series features for vibration source localization with pixel-level short-time Fourier transform (STFT). We verify the relationship between the number of taps in the STFT computation and the performance of our algorithm, including the execution time and the localization accuracy, by conducting experiments under various conditions, such as degraded appearance, weather, and defocused blur. The robustness of the proposed algorithm is also verified by localizing a flying multi-copter in real-time in an outdoor scenario.
With the development of computer technology and the popularization of network, network brings great convenience to colleagues and risks to people from all walks of life all over the world. The data in the network world is growing explosively. Various kinds of intrusions are emerging in an endless stream. The means of network intrusion are becoming more and more complex. The intrusions occur at any time and the security threats become more and more serious. Defense alone cannot meet the needs of system security. It is also necessary to monitor the behavior of users in the network at any time and detect new intrusions that may occur at any time. This will not only make people's normal network needs cannot be guaranteed, but also face great network risks. So that people not only rely on defensive means to protect network security, this paper explores block chain network intrusion detection system. Firstly, the characteristics of block chain are briefly introduced, and the challenges of block chain network intrusion security and privacy are proposed. Secondly, the intrusion detection system of WLAN is designed experimentally. Finally, the conclusion analysis of block chain network intrusion detection system is discussed.
With the development of location technology, location-based services greatly facilitate people's life . However, due to the location information contains a large amount of user sensitive informations, the servicer in location-based services published location data also be subject to the risk of privacy disclosure. In particular, it is more easy to lead to privacy leaks without considering the attacker's semantic background knowledge while the publish sparse location data. So, we proposed semantic k-anonymity privacy protection method to against above problem in this paper. In this method, we first proposed multi-user compressing sensing method to reconstruct the missing location data . To balance the availability and privacy requirment of anonymity set, We use semantic translation and multi-view fusion to selected non-sensitive data to join anonymous set. Experiment results on two real world datasets demonstrate that our solution improve the quality of privacy protection to against semantic attacks.
Style transfer is an emerging trend in the fields of deep learning's applications, especially in images and audio data this is proven very useful and sometimes the results are astonishing. Gradually styles of textual data are also being changed in many novel works. This paper focuses on the transfer of the sentimental vibe of a sentence. Given a positive clause, the negative version of that clause or sentence is generated keeping the context same. The opposite is also done with negative sentences. Previously this was a very tough job because the go-to techniques for such tasks such as Recurrent Neural Networks (RNNs) [1] and Long Short-Term Memories(LSTMs) [2] can't perform well with it. But since newer technologies like Generative Adversarial Network(GAN) and Variational AutoEncoder(VAE) are emerging, this work seem to become more and more possible and effective. In this paper, Multi-Genarative Variational Auto-Encoder is employed to transfer sentiment values. Inspite of working with a small dataset, this model proves to be promising.
We present a unified communication architecture for security requirements in the industrial internet of things. Formulating security requirements in the language of OPC UA provides a unified method to communicate and compare security requirements within a heavily heterogeneous landscape of machines in the field. Our machine-readable data model provides a fully automatable approach for security requirement communication within the rapidly evolving fourth industrial revolution, which is characterized by high-grade interconnection of industrial infrastructures and self-configuring production systems. Capturing security requirements in an OPC UA compliant and unified data model for industrial control systems enables strong use cases within modern production plants and future supply chains. We implement our data model as well as an OPC UA server that operates on this model to show the feasibility of our approach. Further, we deploy and evaluate our framework within a reference project realized by 14 industrial partners and 7 research facilities within Germany.
A cyber attack is a malicious and deliberate attempt by an individual or organization to breach the integrity, confidentiality, and/or availability of data or services of an information system of another individual or organization. Being able to attribute a cyber attack is a crucial question for security but this question is also known to be a difficult problem. The main reason why there is currently no solution that automatically identifies the initiator of an attack is that attackers usually use proxies, i.e. an intermediate node that relays a host over the network. In this paper, we propose to formalize the problem of identifying the initiator of a cyber attack. We show that if the attack scenario used by the attacker is known, then we are able to resolve the cyber attribution problem. Indeed, we propose a model to formalize these attack scenarios, that we call attack patterns, and give an efficient algorithm to search for attack pattern on a communication history. Finally, we experimentally show the relevance of our approach.
While the IT industry is embracing the cloud-native technologies, migrating from monolithic architecture to service-oriented architecture is not a trivial process. It involves a lot of dissection and abstraction. The layer of abstraction designed for simplifying the development quickly becomes the barrier of visibility and the source of misconfigurations. The complexity may give microservices a larger attack surface compared to monolithic applications. This talk presents a microservices threat modeling that uncovers the attack vectors hidden in each abstraction layer. Scenarios of security breaches in microservices platforms are discussed, followed by the countermeasures to close these attack vectors. Finally, a decision-making process for architecting secure microservices is presented.
The advances in natural language processing and the wide use of social networks have boosted the proliferation of chatbots. These are software services typically embedded within a social network, and which can be addressed using conversation through natural language. Many chatbots exist with different purposes, e.g., to book all kind of services, to automate software engineering tasks, or for customer support. In previous work, we proposed the use of chatbots for domain-specific modelling within social networks. In this short paper, we report on the needs for flexible modelling required by modelling using conversation. In particular, we propose a process of meta-model relaxation to make modelling more flexible, followed by correction steps to make the model conforming to its meta-model. The paper shows how this process is integrated within our conversational modelling framework, and illustrates the approach with an example.
The globalization of the semiconductor supply chain introduces ever-increasing security and privacy risks. Two major concerns are IP theft through reverse engineering and malicious modification of the design. The latter concern in part relies on successful reverse engineering of the design as well. IC camouflaging and logic locking are two of the techniques under research that can thwart reverse engineering by end-users or foundries. However, developing low overhead locking/camouflaging schemes that can resist the ever-evolving state-of-the-art attacks has been a challenge for several years. This article provides a comprehensive review of the state of the art with respect to locking/camouflaging techniques. We start by defining a systematic threat model for these techniques and discuss how various real-world scenarios relate to each threat model. We then discuss the evolution of generic algorithmic attacks under each threat model eventually leading to the strongest existing attacks. The article then systematizes defences and along the way discusses attacks that are more specific to certain kinds of locking/camouflaging. The article then concludes by discussing open problems and future directions.
Biometric techniques can help make vehicles safer to drive, authenticate users, and provide personalized in-car experiences. However, it is unclear to what extent users are willing to trade their personal biometric data for such benefits. In this early work, we conducted an open card sorting study (N=11) to better understand how well users perceive their physical, behavioral and physiological features can personally identify them. Findings showed that on average participants clustered features into six groups, and helped us revise ambiguous cards and better understand users' clustering. These findings provide the basis for a follow up online closed card sorting study to more fully understand perceived identification accuracy of (in-vehicle) biometric sensing. By uncovering this at a larger scale, we can then further study the privacy and user experience trade-off in (automated) vehicles.