Biblio

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2020-03-30
2020-01-27
2017-03-20
Dormann, Will.  Submitted.  Google Authentication Risks on iOS. Proceedings of the 1st International Workshop on Mobile Development. :3–5.

The Google Identity Platform is a system that allows a user to sign in to applications and other services by using a Google account. Google Sign-In is one such method for providing one’s identity to the Google Identity Platform. Google Sign-In is available for Android applications and iOS applications, as well as for websites and other devices. Users of Google Sign-In find that it integrates well with the Android platform, but iOS users (iPhone, iPad, etc.) do not have the same experience. The user experience when logging in to a Google account on an iOS application can not only be more tedious than the Android experience, but it also conditions users to engage in behaviors that put the information in their Google accounts at risk.

2017-04-11
Christopher Theisen, Brendan Murphy, Kim Herzig, Laurie Williams.  Submitted.  Risk-Based Attack Surface Approximation: How Much Data is Enough? International Conference on Software Engineering (ICSE) Software Engineering in Practice (SEIP) 2017.

Proactive security reviews and test efforts are a necessary component of the software development lifecycle. Resource limitations often preclude reviewing the entire code
base. Making informed decisions on what code to review can improve a team’s ability to find and remove vulnerabilities. Risk-based attack surface approximation (RASA) is a technique that uses crash dump stack traces to predict what code may contain exploitable vulnerabilities. The goal of this research is to help software development teams prioritize security efforts by the efficient development of a risk-based attack surface approximation. We explore the use of RASA using Mozilla Firefox and Microsoft Windows stack traces from crash dumps. We create RASA at the file level for Firefox, in which the 15.8% of the files that were part of the approximation contained 73.6% of the vulnerabilities seen for the product. We also explore the effect of random sampling of crashes on the approximation, as it may be impractical for organizations to store and process every crash received. We find that 10-fold random sampling of crashes at a rate of 10% resulted in 3% less vulnerabilities identified than using the entire set of stack traces for Mozilla Firefox. Sampling crashes in Windows 8.1 at a rate of 40% resulted in insignificant differences in vulnerability and file coverage as compared to a rate of 100%.

2020-04-06
Fouchal, Hacène, Ninet, Alain.  2020.  Partial Signature for Cooperative Intelligent Transport Systems. 2020 International Conference on Computing, Networking and Communications (ICNC). :586–590.
On C-ITS (Cooperative Intelligent Transport Systems) vehicles send and receive sensitive messages informing about events on roads (accidents, traffic jams, etc,..). The authentication of these messages is highly recommended in order to increase the users confidence about this system. This authentication ensures that only messages coming from trusted vehicles are accepted by receivers. An adapted PKI (Public Key Infrastructure) for C-ITS provides certificates for each vehicle. The certificate will be used to sign messages. This principle is used within deployed C-ITS solutions over the world. This solution is easy to implement but has one major flaw: each message needs to be sent with its signature and its certificate. The size of the message to send becomes high. In the meantime, for many C-ITS use cases, each message is sent many times for robustness reasons. The communication channel could be overloaded. In this paper, we propose to split the signature into some equal parts. When a message has to be sent, it will be sent with one of these parts. A receiver will save the received message with its actual part. For each reception, it will collect the remaining signature parts until all the signature parts are received. Our solution is implemented in a C-ITS architecture working through Bluetooth protocol using the advertising model. The solution is applicable for vehicle speeds reaching 130 km/h. We have proved, through a set of real experimentations, that our solution is possible.
2020-03-30
Thida, Aye, Shwe, Thanda.  2020.  Process Provenance-based Trust Management in Collaborative Fog Environment. 2020 IEEE Conference on Computer Applications(ICCA). :1–5.
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.
2020-03-23
Choi, Jungyong, Shin, WoonSeob, Kim, Jonghyun, Kim, Ki-Hyung.  2020.  Random Seed Generation For IoT Key Generation and Key Management System Using Blockchain. 2020 International Conference on Information Networking (ICOIN). :663–665.
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.
2020-04-06
Chu, YeonSung, Kim, Jae Min, Lee, YoonJick, Shim, SungHoon, Huh, Junho.  2020.  SS-DPKI: Self-Signed Certificate Based Decentralized Public Key Infrastructure for Secure Communication. 2020 IEEE International Conference on Consumer Electronics (ICCE). :1–6.
Currently, the most commonly used scheme for identity authentication on the Internet is based on asymmetric cryptography and the use of a centralized model. The centralized model needs a Certificate Authority (CA) as a trusted third party and a trust chain of CA. However, CA-based PKI is weak in the single point of failure and certificate transparency. Our system, called SS-DPKI, propose a public and decentralized PKI system model. We describe a detailed scheme as well as application to use decentralized PKI based secure communication. Our proposal prevents storage overhead on the data size of transactions and provide reasonable certificate verification time.
2020-04-24
Kim, Chang-Woo, Jang, Gang-Heyon, Shin, Kyung-Hun, Jeong, Sang-Sub, You, Dae-Joon, Choi, Jang-Young.  2020.  Electromagnetic Design and Dynamic Characteristics of Permanent Magnet Linear Oscillating Machines Considering Instantaneous Inductance According to Mover Position. IEEE Transactions on Applied Superconductivity. 30:1—5.
Interior permanent magnet (IPM)-type linear oscillating actuators (LOAs) have a higher output power density than typical LOAs. Their mover consists of a permanent magnet (PM) and an iron core, however, this configuration generates significant side forces. The device can malfunction due to eccentricity in the electromagnetic behavior. Thus, here an electromagnetic design was developed to minimize this side force. In addition, dynamic analysis was performed considering the mechanical systems of LOAs. To perform a more accurate analysis, instantaneous inductance was considered according to the mover's position.
2020-04-17
Gorbenko, Anatoliy, Romanovsky, Alexander, Tarasyuk, Olga, Biloborodov, Oleksandr.  2020.  From Analyzing Operating System Vulnerabilities to Designing Multiversion Intrusion-Tolerant Architectures. IEEE Transactions on Reliability. 69:22—39.
This paper analyzes security problems of modern computer systems caused by vulnerabilities in their operating systems (OSs). Our scrutiny of widely used enterprise OSs focuses on their vulnerabilities by examining the statistical data available on how vulnerabilities in these systems are disclosed and eliminated, and by assessing their criticality. This is done by using statistics from both the National Vulnerabilities Database and the Common Vulnerabilities and Exposures System. The specific technical areas the paper covers are the quantitative assessment of forever-day vulnerabilities, estimation of days-of-grey-risk, the analysis of the vulnerabilities severity and their distributions by attack vector and impact on security properties. In addition, the study aims to explore those vulnerabilities that have been found across a diverse range of OSs. This leads us to analyzing how different intrusion-tolerant architectures deploying the OS diversity impact availability, integrity, and confidentiality.
2020-05-08
Bolla, R., Carrega, A., Repetto, M..  2019.  An abstraction layer for cybersecurity context. 2019 International Conference on Computing, Networking and Communications (ICNC). :214—218.
The growing complexity and diversification of cyber-attacks are largely reflected in the increasing sophistication of security appliances, which are often too cumbersome to be run in virtual services and IoT devices. Hence, the design of cyber-security frameworks is today looking at more cooperative models, which collect security-related data from a large set of heterogeneous sources for centralized analysis and correlation.In this paper, we outline a flexible abstraction layer for access to security context. It is conceived to program and gather data from lightweight inspection and enforcement hooks deployed in cloud applications and IoT devices. We also provide a preliminary description of its implementation, by reviewing the main software components and their role.
2020-05-22
Abdelhadi, Ameer M.S., Bouganis, Christos-Savvas, Constantinides, George A..  2019.  Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization. 2019 International Conference on Field-Programmable Technology (ICFPT). :90—98.
A fundamental recurring task in many machine learning applications is the search for the Nearest Neighbor in high dimensional metric spaces. Towards answering queries in large scale problems, state-of-the-art methods employ Approximate Nearest Neighbors (ANN) search, a search that returns the nearest neighbor with high probability, as well as techniques that compress the dataset. Product-Quantization (PQ) based ANN search methods have demonstrated state-of-the-art performance in several problems, including classification, regression and information retrieval. The dataset is encoded into a Cartesian product of multiple low-dimensional codebooks, enabling faster search and higher compression. Being intrinsically parallel, PQ-based ANN search approaches are amendable for hardware acceleration. This paper proposes a novel Hierarchical PQ (HPQ) based ANN search method as well as an FPGA-tailored architecture for its implementation that outperforms current state of the art systems. HPQ gradually refines the search space, reducing the number of data compares and enabling a pipelined search. The mapping of the architecture on a Stratix 10 FPGA device demonstrates over ×250 speedups over current state-of-the-art systems, opening the space for addressing larger datasets and/or improving the query times of current systems.
2020-05-15
Fan, Renshi, Du, Gaoming, Xu, Pengfei, Li, Zhenmin, Song, Yukun, Zhang, Duoli.  2019.  An Adaptive Routing Scheme Based on Q-learning and Real-time Traffic Monitoring for Network-on-Chip. 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :244—248.
In the Network on Chip (NoC), performance optimization has always been a research focus. Compared with the static routing scheme, dynamical routing schemes can better reduce the data of packet transmission latency under network congestion. In this paper, we propose a dynamical Q-learning routing approach with real-time monitoring of NoC. Firstly, we design a real-time monitoring scheme and the corresponding circuits to record the status of traffic congestion for NoC. Secondly, we propose a novel method of Q-learning. This method finds an optimal path based on the lowest traffic congestion. Finally, we dynamically redistribute network tasks to increase the packet transmission speed and balance the traffic load. Compared with the C-XY routing and DyXY routing, our method achieved improvement in terms of 25.6%-49.5% and 22.9%-43.8%.
2020-02-17
Chalise, Batu K..  2019.  ADMM-based Beamforming Optimization for Physical Layer Security in a Full-duplex Relay System. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :4734–4738.
Although beamforming optimization problems in full-duplex communication systems can be optimally solved with the semidefinite relaxation (SDR) approach, its computational complexity increases rapidly when the problem size increases. In order to circumvent this issue, in this paper, we propose an alternating direction of multiplier method (ADMM) which minimizes the augmented Lagrangian of the dual of the SDR and handles the inequality constraints with the use of slack variables. The proposed ADMM is then applied for optimizing the relay beamformer to maximize the secrecy rate. Simulation results show that the proposed ADMM performs as good as the SDR approach.
2020-03-27
Abedin, Zain Ul, Guan, Zhitao, Arif, Asad Ullah, Anwar, Usman.  2019.  An Advance Cryptographic Solutions in Cloud Computing Security. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). :1–6.
Cryptographically cloud computing may be an innovative safe cloud computing design. Cloud computing may be a huge size dispersed computing model that ambitious by the economy of the level. It integrates a group of inattentive virtualized animatedly scalable and managed possessions like computing control storage space platform and services. External end users will approach to resources over the net victimization fatal particularly mobile terminals, Cloud's architecture structures are advances in on-demand new trends. That are the belongings are animatedly assigned to a user per his request and hand over when the task is finished. So, this paper projected biometric coding to boost the confidentiality in Cloud computing for biometric knowledge. Also, this paper mentioned virtualization for Cloud computing also as statistics coding. Indeed, this paper overviewed the safety weaknesses of Cloud computing and the way biometric coding will improve the confidentiality in Cloud computing atmosphere. Excluding this confidentiality is increased in Cloud computing by victimization biometric coding for biometric knowledge. The novel approach of biometric coding is to reinforce the biometric knowledge confidentiality in Cloud computing. Implementation of identification mechanism can take the security of information and access management in the cloud to a higher level. This section discusses, however, a projected statistics system with relation to alternative recognition systems to date is a lot of advantageous and result oriented as a result of it does not work on presumptions: it's distinctive and provides quick and contact less authentication. Thus, this paper reviews the new discipline techniques accustomed to defend methodology encrypted info in passing remote cloud storage.
2020-01-27
Matyukhina, Alina, Stakhanova, Natalia, Dalla Preda, Mila, Perley, Celine.  2019.  Adversarial Authorship Attribution in Open-Source Projects. Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy. :291–302.
Open-source software is open to anyone by design, whether it is a community of developers, hackers or malicious users. Authors of open-source software typically hide their identity through nicknames and avatars. However, they have no protection against authorship attribution techniques that are able to create software author profiles just by analyzing software characteristics. In this paper we present an author imitation attack that allows to deceive current authorship attribution systems and mimic a coding style of a target developer. Withing this context we explore the potential of the existing attribution techniques to be deceived. Our results show that we are able to imitate the coding style of the developers based on the data collected from the popular source code repository, GitHub. To subvert author imitation attack, we propose a novel author obfuscation approach that allows us to hide the coding style of the author. Unlike existing obfuscation tools, this new obfuscation technique uses transformations that preserve code readability. We assess the effectiveness of our attacks on several datasets produced by actual developers from GitHub, and participants of the GoogleCodeJam competition. Throughout our experiments we show that the author hiding can be achieved by making sensible transformations which significantly reduce the likelihood of identifying the author's style to 0% by current authorship attribution systems.
2020-04-24
Tuttle, Michael, Wicker, Braden, Poshtan, Majid, Callenes, Joseph.  2019.  Algorithmic Approaches to Characterizing Power Flow Cyber-Attack Vulnerabilities. 2019 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT). :1—5.
As power grid control systems become increasingly automated and distributed, security has become a significant design concern. Systems increasingly expose new avenues, at a variety of levels, for attackers to exploit and enable widespread disruptions and/or surveillance. Much prior work has explored the implications of attack models focused on false data injection at the front-end of the control system (i.e. during state estimation) [1]. Instead, in this paper we focus on characterizing the inherent cyber-attack vulnerabilities with power flow. Power flow (and power flow constraints) are at the core of many applications critical to operation of power grids (e.g. state estimation, economic dispatch, contingency analysis, etc.). We propose two algorithmic approaches for characterizing the vulnerability of buses within power grids to cyber-attacks. Specifically, we focus on measuring the instability of power flow to attacks which manifest as either voltage or power related errors. Our results show that attacks manifesting as voltage errors are an order of magnitude more likely to cause instability than attacks manifesting as power related errors (and 5x more likely for state estimation as compared to power flow).
2020-02-17
de Andrade Bragagnolle, Thiago, Pereira Nogueira, Marcelo, de Oliveira Santos, Melissa, do Prado, Afonso José, Ferreira, André Alves, de Mello Fagotto, Eric Alberto, Aldaya, Ivan, Abbade, Marcelo Luís Francisco.  2019.  All-Optical Spectral Shuffling of Signals Traveling through Different Optical Routes. 2019 21st International Conference on Transparent Optical Networks (ICTON). :1–4.
A recent proposed physical layer encryption technique uses an all-optical setup based on spatial light modulators to split two or more wavelength division multiplexed (WDM) signals in several spectral slices and to shuffle these slices. As a result, eavesdroppers aimed to recover information from a single target signal need to handle all the signals involved in the shuffling process. In this work, computer simulations are used to analyse the case where the shuffled signals propagate through different optical routes. From a security point of view, this is an interesting possibility because it obliges eavesdroppers to tap different optical fibres/ cables. On the other hand, each shuffled signal experiences different physical impairments and the deleterious consequences of these effects must be carefully investigated. Our results indicate that, in a metropolitan area network environment, penalties caused by attenuation and dispersion differences may be easily compensated with digital signal processing algorithms that are presently deployed.
2020-03-23
Hyunki-Kim, Jinhyeok-Oh, Changuk-Jang, Okyeon-Yi, Juhong-Han, Hansaem-Wi, Chanil-Park.  2019.  Analysis of the Noise Source Entropy Used in OpenSSL’s Random Number Generation Mechanism. 2019 International Conference on Information and Communication Technology Convergence (ICTC). :59–62.
OpenSSL is an open source library that implements the Secure Socket Layer (SSL), a security protocol used by the TCP/IP layer. All cryptographic systems require random number generation for many reasons, such as cryptographic key generation and protocol challenge/response, OpenSSL is also the same. OpenSSL can be run on a variety of operating systems. especially when generating random numbers on Unix-like operating systems, it can use /dev /(u)random [6], as a seed to add randomness. In this paper, we analyze the process provided by OpenSSL when random number generation is required. We also provide considerations for application developers and OpenSSL users to use /dev/urandom and real-time clock (nanoseconds of timespec structure) as a seed to generate cryptographic random numbers in the Unix family.
2020-03-02
Arifeen, Md Murshedul, Islam, Al Amin, Rahman, Md Mustafizur, Taher, Kazi Abu, Islam, Md.Maynul, Kaiser, M Shamim.  2019.  ANFIS based Trust Management Model to Enhance Location Privacy in Underwater Wireless Sensor Networks. 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE). :1–6.
Trust management is a promising alternative solution to different complex security algorithms for Underwater Wireless Sensor Networks (UWSN) applications due to its several resource constraint behaviour. In this work, we have proposed a trust management model to improve location privacy of the UWSN. Adaptive Neuro Fuzzy Inference System (ANFIS) has been exploited to evaluate trustworthiness of a sensor node. Also Markov Decision Process (MDP) has been considered. At each state of the MDP, a sensor node evaluates trust behaviour of forwarding node utilizing the FIS learning rules and selects a trusted node. Simulation has been conducted in MATLAB and simulation results show that the detection accuracy of trustworthiness is 91.2% which is greater than Knowledge Discovery and Data Mining (KDD) 99 intrusion detection based dataset. So, in our model 91.2% trustworthiness is necessary to be a trusted node otherwise it will be treated as a malicious or compromised node. Our proposed model can successfully eliminate the possibility of occurring any compromised or malicious node in the network.
2020-04-13
M.R., Anala, Makker, Malika, Ashok, Aakanksha.  2019.  Anomaly Detection in Surveillance Videos. 2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW). :93–98.
Every public or private area today is preferred to be under surveillance to ensure high levels of security. Since the surveillance happens round the clock, data gathered as a result is huge and requires a lot of manual work to go through every second of the recorded videos. This paper presents a system which can detect anomalous behaviors and alarm the user on the type of anomalous behavior. Since there are a myriad of anomalies, the classification of anomalies had to be narrowed down. There are certain anomalies which are generally seen and have a huge impact on public safety, such as explosions, road accidents, assault, shooting, etc. To narrow down the variations, this system can detect explosion, road accidents, shooting, and fighting and even output the frame of their occurrence. The model has been trained with videos belonging to these classes. The dataset used is UCF Crime dataset. Learning patterns from videos requires the learning of both spatial and temporal features. Convolutional Neural Networks (CNN) extract spatial features and Long Short-Term Memory (LSTM) networks learn the sequences. The classification, using an CNN-LSTM model achieves an accuracy of 85%.
2020-05-08
Chaudhary, Anshika, Mittal, Himangi, Arora, Anuja.  2019.  Anomaly Detection using Graph Neural Networks. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). :346—350.
Conventional methods for anomaly detection include techniques based on clustering, proximity or classification. With the rapidly growing social networks, outliers or anomalies find ingenious ways to obscure themselves in the network and making the conventional techniques inefficient. In this paper, we utilize the ability of Deep Learning over topological characteristics of a social network to detect anomalies in email network and twitter network. We present a model, Graph Neural Network, which is applied on social connection graphs to detect anomalies. The combinations of various social network statistical measures are taken into account to study the graph structure and functioning of the anomalous nodes by employing deep neural networks on it. The hidden layer of the neural network plays an important role in finding the impact of statistical measure combination in anomaly detection.
Fu, Tian, Lu, Yiqin, Zhen, Wang.  2019.  APT Attack Situation Assessment Model Based on optimized BP Neural Network. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :2108—2111.
In this paper, it first analyzed the characteristics of Advanced Persistent Threat (APT). according to APT attack, this paper established an BP neural network optimized by improved adaptive genetic algorithm to predict the security risk of nodes in the network. and calculated the path of APT attacks with the maximum possible attack. Finally, experiments verify the effectiveness and correctness of the algorithm by simulating attacks. Experiments show that this model can effectively evaluate the security situation in the network, For the defenders to adopt effective measures defend against APT attacks, thus improving the security of the network.
2020-04-10
Repetto, M., Carrega, A., Lamanna, G..  2019.  An architecture to manage security services for cloud applications. 2019 4th International Conference on Computing, Communications and Security (ICCCS). :1—8.
The uptake of virtualization and cloud technologies has pushed novel development and operation models for the software, bringing more agility and automation. Unfortunately, cyber-security paradigms have not evolved at the same pace and are not yet able to effectively tackle the progressive disappearing of a sharp security perimeter. In this paper, we describe a novel cyber-security architecture for cloud-based distributed applications and network services. We propose a security orchestrator that controls pervasive, lightweight, and programmable security hooks embedded in the virtual functions that compose the cloud application, pursuing better visibility and more automation in this domain. Our approach improves existing management practice for service orchestration, by decoupling the management of the business logic from that of security. We also describe the current implementation stage for a programmable monitoring, inspection, and enforcement framework, which represents the ground technology for the realization of the whole architecture.
2019-12-30
Kee, Ruitao, Sie, Jovan, Wong, Rhys, Yap, Chern Nam.  2019.  Arithmetic Circuit Homomorphic Encryption and Multiprocessing Enhancements. 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1–5.
This is a feasibility study on homomorphic encryption using the TFHE library [1] in daily computing using cloud services. A basic set of arithmetic operations namely - addition, subtraction, multiplication and division were created from the logic gates provide. This research peeks into the impact of logic gates on these operations such as latency of the gates and the operation itself. Multiprocessing enhancement were done for multiplication operation using MPI and OpenMP to reduce latency.