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.

2020-05-22
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.
2020-08-03
Chowdhary, Ankur, Sengupta, Sailik, Alshamrani, Adel, Huang, Dijiang, Sabur, Abdulhakim.  2019.  Adaptive MTD Security using Markov Game Modeling. 2019 International Conference on Computing, Networking and Communications (ICNC). :577–581.
Large scale cloud networks consist of distributed networking and computing elements that process critical information and thus security is a key requirement for any environment. Unfortunately, assessing the security state of such networks is a challenging task and the tools used in the past by security experts such as packet filtering, firewall, Intrusion Detection Systems (IDS) etc., provide a reactive security mechanism. In this paper, we introduce a Moving Target Defense (MTD) based proactive security framework for monitoring attacks which lets us identify and reason about multi-stage attacks that target software vulnerabilities present in a cloud network. We formulate the multi-stage attack scenario as a two-player zero-sum Markov Game (between the attacker and the network administrator) on attack graphs. The rewards and transition probabilities are obtained by leveraging the expert knowledge present in the Common Vulnerability Scoring System (CVSS). Our framework identifies an attacker's optimal policy and places countermeasures to ensure that this attack policy is always detected, thus forcing the attacker to use a sub-optimal policy with higher cost.
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.
2020-07-03
Adari, Suman Kalyan, Garcia, Washington, Butler, Kevin.  2019.  Adversarial Video Captioning. 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W). :24—27.
In recent years, developments in the field of computer vision have allowed deep learning-based techniques to surpass human-level performance. However, these advances have also culminated in the advent of adversarial machine learning techniques, capable of launching targeted image captioning attacks that easily fool deep learning models. Although attacks in the image domain are well studied, little work has been done in the video domain. In this paper, we show it is possible to extend prior attacks in the image domain to the video captioning task, without heavily affecting the video's playback quality. We demonstrate our attack against a state-of-the-art video captioning model, by extending a prior image captioning attack known as Show and Fool. To the best of our knowledge, this is the first successful method for targeted attacks against a video captioning model, which is able to inject 'subliminal' perturbations into the video stream, and force the model to output a chosen caption with up to 0.981 cosine similarity, achieving near-perfect similarity to chosen target captions.
2020-08-07
Davenport, Amanda, Shetty, Sachin.  2019.  Air Gapped Wallet Schemes and Private Key Leakage in Permissioned Blockchain Platforms. 2019 IEEE International Conference on Blockchain (Blockchain). :541—545.
In this paper we consider the threat surface and security of air gapped wallet schemes for permissioned blockchains as preparation for a Markov based mathematical model, and quantify the risk associated with private key leakage. We identify existing threats to the wallet scheme and existing work done to both attack and secure the scheme. We provide an overview the proposed model and outline justification for our methods. We follow with next steps in our remaining work and the overarching goals and motivation for our methods.
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.
2020-08-10
Mansour, Ahmad, Malik, Khalid M., Kaso, Niko.  2019.  AMOUN: Lightweight Scalable Multi-recipient Asymmetric Cryptographic Scheme. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). :0838–0846.
Securing multi-party communication is very challenging particularly in dynamic networks. Existing multi-recipient cryptographic schemes pose variety of limitations. These include: requiring trust among all recipients to make an agreement, high computational cost for both encryption and decryption, and additional communication overhead when group membership changes. To overcome these limitations, this paper introduces a novel multi-recipient asymmetric cryptographic scheme, AMOUN. This scheme enables the sender to possibly send different messages in one ciphertext to multiple recipients to better utilize network resources, while ensuring that each recipient only retrieves its own designated message. Security analysis demonstrates that proposed scheme is secure against well-known attacks. Evaluation results demonstrate that lightweight AMOUN outperforms RSA and Multi-RSA in terms of computational cost for both encryption and decryption. For a given prime size, in case of encryption, AMOUN achieves 86% and 98% lower average computational cost than RSA and Multi-RSA, respectively; while for decryption, it shows performance improvement of 98% compared to RSA and Multi-RSA.
2020-07-06
Cerotti, D., Codetta-Raiteri, D., Egidi, L., Franceschinis, G., Portinale, L., Dondossola, G., Terruggia, R..  2019.  Analysis and Detection of Cyber Attack Processes targeting Smart Grids. 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). :1–5.
This paper proposes an approach based on Bayesian Networks to support cyber security analysts in improving the cyber-security posture of the smart grid. We build a system model that exploits real world context information from both Information and Operational Technology environments in the smart grid, and we use it to demonstrate sample predictive and diagnostic analyses. The innovative contribution of this work is in the methodology capability of capturing the many dependencies involved in the assessment of security threats, and of supporting the security analysts in planning defense and detection mechanisms for energy digital infrastructures.
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.
2020-08-10
Zhang, Xinman, He, Tingting, Xu, Xuebin.  2019.  Android-Based Smartphone Authentication System Using Biometric Techniques: A Review. 2019 4th International Conference on Control, Robotics and Cybernetics (CRC). :104–108.
As the technological progress of mobile Internet, smartphone based on Android OS accounts for the vast majority of market share. The traditional encryption technology cannot resolve the dilemma in smartphone information leakage, and the Android-based authentication system in view of biometric recognition emerge to offer more reliable information assurance. In this paper, we summarize several biometrics providing their attributes. Furthermore, we also review the algorithmic framework and performance index acting on authentication techniques. Thus, typical identity authentication systems including their experimental results are concluded and analyzed in the survey. The article is written with an intention to provide an in-depth overview of Android-based biometric verification systems to the readers.
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%.
2020-07-13
Mahmood, Shah.  2019.  The Anti-Data-Mining (ADM) Framework - Better Privacy on Online Social Networks and Beyond. 2019 IEEE International Conference on Big Data (Big Data). :5780–5788.
The unprecedented and enormous growth of cloud computing, especially online social networks, has resulted in numerous incidents of the loss of users' privacy. In this paper, we provide a framework, based on our anti-data-mining (ADM) principle, to enhance users' privacy against adversaries including: online social networks; search engines; financial terminal providers; ad networks; eavesdropping governments; and other parties who can monitor users' content from the point where the content leaves users' computers to within the data centers of these information accumulators. To achieve this goal, our framework proactively uses the principles of suppression of sensitive data and disinformation. Moreover, we use social-bots in a novel way for enhanced privacy and provide users' with plausible deniability for their photos, audio, and video content uploaded online.