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Ayoob, Mustafa, Adi, Wael, Prevelakis, Vassilis.  2017.  Using Ciphers for Failure-Recovery in ITS Systems. Proceedings of the 12th International Conference on Availability, Reliability and Security. :98:1–98:7.
Combining Error-Correction Coding ECC and cryptography was proposed in the recent decade making use of bit-quality parameters to improve the error correction capability. Most of such techniques combine authentication crypto-functions jointly with ECC codes to improve system reliability, while fewer proposals involve ciphering functions with ECC to improve reliability. In this work, we propose practical and pragmatic low-cost approaches for making use of existing ciphering functions for reliability improvement. The presented techniques show that ciphering functions (as deterministic, non-linear bijective functions) can serve to achieve error correction enhancement and hence allow error recovery and scalable security trade-offs with or without additional ECC components. We demonstrate two best-effort error-correcting strategies. It is further shown, that the targeted reliability improvement is scalable to attain practical usability. The first proposed technique is pure-cipher-based error correction procedure deploying hard decision, best-effort operations to improve the system-survivability without changing system configuration. The second strategy is making use of ECC in combination with the ciphering function to enhance system-survivability. The correction procedures are based on simple experimental search-and-modify the corrupted ciphertext until predefined criteria become valid. This procedure may, however, turn out to become equivalent to a successful integrity/authenticity attack that may reduce the system security level, however in a scalable and predictable non-significant fashion.
Smith, A. M., Mayo, J. R., Kammler, V., Armstrong, R. C., Vorobeychik, Y..  2017.  Using computational game theory to guide verification and security in hardware designs. 2017 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). :110–115.

Verifying that hardware design implementations adhere to specifications is a time intensive and sometimes intractable problem due to the massive size of the system's state space. Formal methods techniques can be used to prove certain tractable specification properties; however, they are expensive, and often require subject matter experts to develop and solve. Nonetheless, hardware verification is a critical process to ensure security and safety properties are met, and encapsulates problems associated with trust and reliability. For complex designs where coverage of the entire state space is unattainable, prioritizing regions most vulnerable to security or reliability threats would allow efficient allocation of valuable verification resources. Stackelberg security games model interactions between a defender, whose goal is to assign resources to protect a set of targets, and an attacker, who aims to inflict maximum damage on the targets after first observing the defender's strategy. In equilibrium, the defender has an optimal security deployment strategy, given the attacker's best response. We apply this Stackelberg security framework to synthesized hardware implementations using the design's network structure and logic to inform defender valuations and verification costs. The defender's strategy in equilibrium is thus interpreted as a prioritization of the allocation of verification resources in the presence of an adversary. We demonstrate this technique on several open-source synthesized hardware designs.

Lin, W., Lin, H., Wang, P., Wu, B., Tsai, J..  2018.  Using Convolutional Neural Networks to Network Intrusion Detection for Cyber Threats. 2018 IEEE International Conference on Applied System Invention (ICASI). :1107-1110.

In practice, Defenders need a more efficient network detection approach which has the advantages of quick-responding learning capability of new network behavioural features for network intrusion detection purpose. In many applications the capability of Deep Learning techniques has been confirmed to outperform classic approaches. Accordingly, this study focused on network intrusion detection using convolutional neural networks (CNNs) based on LeNet-5 to classify the network threats. The experiment results show that the prediction accuracy of intrusion detection goes up to 99.65% with samples more than 10,000. The overall accuracy rate is 97.53%.

Viet, Hung Nguyen, Van, Quan Nguyen, Trang, Linh Le Thi, Nathan, Shone.  2018.  Using Deep Learning Model for Network Scanning Detection. Proceedings of the 4th International Conference on Frontiers of Educational Technologies. :117-121.

In recent years, new and devastating cyber attacks amplify the need for robust cybersecurity practices. Preventing novel cyber attacks requires the invention of Intrusion Detection Systems (IDSs), which can identify previously unseen attacks. Many researchers have attempted to produce anomaly - based IDSs, however they are not yet able to detect malicious network traffic consistently enough to warrant implementation in real networks. Obviously, it remains a challenge for the security community to produce IDSs that are suitable for implementation in the real world. In this paper, we propose a new approach using a Deep Belief Network with a combination of supervised and unsupervised machine learning methods for port scanning attacks detection - the task of probing enterprise networks or Internet wide services, searching for vulnerabilities or ways to infiltrate IT assets. Our proposed approach will be tested with network security datasets and compared with previously existing methods.

Al-Emadi, S., Al-Mohannadi, A., Al-Senaid, F..  2020.  Using Deep Learning Techniques for Network Intrusion Detection. 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT). :171—176.
In recent years, there has been a significant increase in network intrusion attacks which raises a great concern from the privacy and security aspects. Due to the advancement of the technology, cyber-security attacks are becoming very complex such that the current detection systems are not sufficient enough to address this issue. Therefore, an implementation of an intelligent and effective network intrusion detection system would be crucial to solve this problem. In this paper, we use deep learning techniques, namely, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to design an intelligent detection system which is able to detect different network intrusions. Additionally, we evaluate the performance of the proposed solution using different evaluation matrices and we present a comparison between the results of our proposed solution to find the best model for the network intrusion detection system.
Dylan Wang, Melody Moh, Teng-Sheng Moh.  2020.  Using Deep Learning to Solve Google reCAPTCHA v2’s Image Challenges.

The most popular CAPTCHA service in use today is Google reCAPTCHA v2, whose main offering is an image-based CAPTCHA challenge. This paper looks into the security measures used in reCAPTCHA v2's image challenges and proposes a deep learning-based solution that can be used to automatically solve them. The proposed method is tested with both a custom object- detection deep learning model as well as Google's own Cloud Vision API, in conjunction with human mimicking mouse movements to bypass the challenges. The paper also suggests some potential defense measures to increase overall security and other additional attack directions for reCAPTCHA v2.

Li, Huhua, Zhan, Dongyang, Liu, Tianrui, Ye, Lin.  2019.  Using Deep-Learning-Based Memory Analysis for Malware Detection in Cloud. 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems Workshops (MASSW). :1–6.
Malware is one of the biggest threats in cloud computing. Malware running inside virtual machines or containers could steal critical information or continue to attack other cloud nodes. To detect malware in cloud, especially zero-day malware, signature-and machine-learning-based approaches are proposed to analyze the execution binary. However, malicious binary files may not permanently be stored in the file system of virtual machine or container, periodically scanner may not find the target files. Dynamic analysis approach usually introduce run-time overhead to virtual machines, which is not widely used in cloud. To solve these problems, we propose a memory analysis approach to detect malware, employing the deep learning technology. The system analyzes the memory image periodically during malware execution, which will not introduce run-time overhead. We first extract the memory snapshot from running virtual machines or containers. Then, the snapshot is converted to a grayscale image. Finally, we employ CNN to detect malware. In the learning phase, malicious and benign software are trained. In the testing phase, we test our system with real-world malwares.
Shepherd, Morgan M., Klein, Gary.  2012.  Using Deterrence to Mitigate Employee Internet Abuse. 2012 45th Hawaii International Conference on System Sciences. :5261–5266.
This study looks at the question of how to reduce/eliminate employee Internet Abuse. Companies have used acceptable use policies (AUP) and technology in an attempt to mitigate employees' personal use of company resources. Research shows that AUPs do not do a good job at this but that technology does. Research also shows that while technology can be used to greatly restrict personal use of the internet in the workplace, employee satisfaction with the workplace suffers when this is done. In this research experiment we used technology not to restrict employee use of company resources for personal use, but to make the employees more aware of the current Acceptable Use Policy, and measured the decrease in employee internet abuse. The results show that this method can result in a drop from 27 to 21 percent personal use of the company networks.
Wang, P., Lin, W. H., Chao, W. J., Chao, K. M., Lo, C. C..  2015.  Using Dynamic Taint Approach for Malware Threat. 2015 IEEE 12th International Conference on e-Business Engineering. :408–416.

Most existing approaches focus on examining the values are dangerous for information flow within inter-suspicious modules of cloud applications (apps) in a host by using malware threat analysis, rather than the risk posed by suspicious apps were connected to the cloud computing server. Accordingly, this paper proposes a taint propagation analysis model incorporating a weighted spanning tree analysis scheme to track data with taint marking using several taint checking tools. In the proposed model, Android programs perform dynamic taint propagation to analyse the spread of and risks posed by suspicious apps were connected to the cloud computing server. In determining the risk of taint propagation, risk and defence capability are used for each taint path for assisting a defender in recognising the attack results against network threats caused by malware infection and estimate the losses of associated taint sources. Finally, a case of threat analysis of a typical cyber security attack is presented to demonstrate the proposed approach. Our approach verified the details of an attack sequence for malware infection by incorporating a finite state machine (FSM) to appropriately reflect the real situations at various configuration settings and safeguard deployment. The experimental results proved that the threat analysis model allows a defender to convert the spread of taint propagation to loss and practically estimate the risk of a specific threat by using behavioural analysis with real malware infection.

Hernández, S., Lu, P. L., Granz, S., Krivosik, P., Huang, P. W., Eppler, W., Rausch, T., Gage, E..  2017.  Using Ensemble Waveform Analysis to Compare Heat Assisted Magnetic Recording Characteristics of Modeled and Measured Signals. IEEE Transactions on Magnetics. 53:1–6.

Ensemble waveform analysis is used to calculate signal to noise ratio (SNR) and other recording characteristics from micromagnetically modeled heat assisted magnetic recording waveforms and waveforms measured at both drive and spin-stand level. Using windowing functions provides the breakdown between transition and remanence SNRs. In addition, channel bit density (CBD) can be extracted from the ensemble waveforms using the di-bit extraction method. Trends in both transition SNR, remanence SNR, and CBD as a function of ambient temperature at constant track width showed good agreement between model and measurement. Both model and drive-level measurement show degradation in SNR at higher ambient temperatures, which may be due to changes in the down-track profile at the track edges compared with track center. CBD as a function of cross-track position is also calculated for both modeling and spin-stand measurements. The CBD widening at high cross-track offset, which is observed at both measurement and model, was directly related to the radius of curvature of the written transitions observed in the model and the thermal profiles used.

Husari, G., Niu, X., Chu, B., Al-Shaer, E..  2018.  Using Entropy and Mutual Information to Extract Threat Actions from Cyber Threat Intelligence. 2018 IEEE International Conference on Intelligence and Security Informatics (ISI). :1–6.
With the rapid growth of the cyber attacks, cyber threat intelligence (CTI) sharing becomes essential for providing advance threat notice and enabling timely response to cyber attacks. Our goal in this paper is to develop an approach to extract low-level cyber threat actions from publicly available CTI sources in an automated manner to enable timely defense decision making. Specifically, we innovatively and successfully used the metrics of entropy and mutual information from Information Theory to analyze the text in the cybersecurity domain. Combined with some basic NLP techniques, our framework, called ActionMiner has achieved higher precision and recall than the state-of-the-art Stanford typed dependency parser, which usually works well in general English but not cybersecurity texts.
Oliveira, Raquel, Dupuy-Chessa, Sophie, Calvary, Gaëlle, Dadolle, Daniele.  2016.  Using Formal Models to Cross Check an Implementation. Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems. :126–137.

Interactive systems are developed according to requirements, which may be, for instance, documentation, prototypes, diagrams, etc. The informal nature of system requirements may be a source of problems: it may be the case that a system does not implement the requirements as expected, thus, a way to validate whether an implementation follows the requirements is needed. We propose a novel approach to validating a system using formal models of the system. In this approach, a set of traces generated from the execution of the real interactive system is searched over the state space of the formal model. The scalability of the approach is demonstrated by an application to an industrial system in the nuclear plant domain. The combination of trace analysis and formal methods provides feedback that can bring improvements to both the real interactive system and the formal model.

Xie, J., Zhang, M., Ma, Y..  2019.  Using Format Migration and Preservation Metadata to Support Digital Preservation of Scientific Data. 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS). :1—6.

With the development of e-Science and data intensive scientific discovery, it needs to ensure scientific data available for the long-term, with the goal that the valuable scientific data should be discovered and re-used for downstream investigations, either alone, or in combination with newly generated data. As such, the preservation of scientific data enables that not only might experiment be reproducible and verifiable, but also new questions can be raised by other scientists to promote research and innovation. In this paper, we focus on the two main problems of digital preservation that are format migration and preservation metadata. Format migration includes both format verification and object transformation. The system architecture of format migration and preservation metadata is presented, mapping rules of object transformation are analyzed, data fixity and integrity and authenticity, digital signature and so on are discussed and an example is shown in detail.

Hinh, Robert, Shin, Sangmi, Taylor, Julia.  2016.  Using frame semantics in authorship attribution. :004093–004098.

Authorship attribution is a stylometric technique that associates text to authors based on the type of writing styles. Researchers have looked for ways to analyze the context of these texts, in some cases with limited results. Most of the approaches view information at the syntactic and physical levels and tend to ignore information from the semantic levels. In this paper, we present a technique that incorporates the use of semantic frames as a method for authorship attribution. We hypothesize that it provides a deeper view into the semantic level of texts, which is an influencing factor in a writer's style. We use a variety of online resources in a pipeline fashion to extract information about frames within the text. The results show that our “bag of frames” approach can be used successfully for stylometry.
 

Hinh, Robert, Shin, Sangmi, Taylor, Julia.  2016.  Using frame semantics in authorship attribution. :004093–004098.

Authorship attribution is a stylometric technique that associates text to authors based on the type of writing styles. Researchers have looked for ways to analyze the context of these texts, in some cases with limited results. Most of the approaches view information at the syntactic and physical levels and tend to ignore information from the semantic levels. In this paper, we present a technique that incorporates the use of semantic frames as a method for authorship attribution. We hypothesize that it provides a deeper view into the semantic level of texts, which is an influencing factor in a writer's style. We use a variety of online resources in a pipeline fashion to extract information about frames within the text. The results show that our “bag of frames” approach can be used successfully for stylometry.

Adams, M., Bhargava, V. K..  2017.  Using friendly jamming to improve route security and quality in ad hoc networks. 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE). :1–6.

Friendly jamming is a physical layer security technique that utilizes extra available nodes to jam any eavesdroppers. This paper considers the use of additional available nodes as friendly jammers in order to improve the security performance of a route through a wireless area network. One of the unresolved technical challenges is the combining of security metrics with typical service quality metrics. In this context, this paper considers the problem of routing through a D2D network while jointly minimizing the secrecy outage probability (SOP) and connection outage probability (COP), using friendly jamming to improve the SOP of each link. The jamming powers are determined to place nulls at friendly receivers while maximizing the power to eavesdroppers. Then the route metrics are derived, and the problem is framed as a convex optimization problem. We also consider that not all network users equally value SOP and COP, and so introduce an auxiliary variable to tune the optimization between the two metrics.

Feil, Sebastian, Kretzer, Martin, Werder, Karl, Maedche, Alexander.  2016.  Using Gamification to Tackle the Cold-Start Problem in Recommender Systems. Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion. :253–256.

The cold start problem in recommender systems refers to the inability of making reliable recommendations if a critical mass of items has not yet been rated. To bypass this problem existing research focused on developing more reliable prediction models for situations in which only few items ratings exist. However, most of these approaches depend on adjusting the algorithm that determines a recommendation. We present a complimentary approach that does not require any adjustments to the recommendation algorithm. We draw on motivation theory and reward users for rating items. In particular, we instantiate different gamification patterns and examine their effect on the average user’s number of provided report ratings. Our results confirm the positive effect of instantiating gamification patterns on the number of received report ratings.

Lewis, Matt.  2018.  Using Graph Databases to Assess the Security of Thingernets Based on the Thingabilities and Thingertivity of Things. Living in the Internet of Things: Cybersecurity of the IoT - 2018. :1-9.

Security within the IoT is currently below par. Common security issues include IoT device vendors not following security best practices and/or omitting crucial security controls and features within their devices, lack of defined and mandated IoT security standards, default IoT device configurations, missing secure update mechanisms to rectify security flaws discovered in IoT devices and the overall unintended consequence of complexity - the attack surface of networks comprising IoT devices can increase exponentially with the addition of each new device. In this paper we set out an approach using graphs and graph databases to understand IoT network complexity and the impact that different devices and their profiles have on the overall security of the underlying network and its associated data.

Lavrova, Daria, Zegzhda, Dmitry, Yarmak, Anastasiia.  2019.  Using GRU neural network for cyber-attack detection in automated process control systems. 2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom). :1—3.
This paper provides an approach to the detection of information security breaches in automated process control systems (APCS), which consists in forecasting multivariate time series formed from the values of the operating parameters of the end system devices. Using an experimental model of water treatment, a comparison was made of the forecasting results for the parameters characterizing the operation of the entire model, and for the parameters characterizing the flow of individual subprocesses implemented by the model. For forecasting, GRU-neural network training was performed.
Yu, M., Halak, B., Zwolinski, M..  2019.  Using Hardware Performance Counters to Detect Control Hijacking Attacks. 2019 IEEE 4th International Verification and Security Workshop (IVSW). :1–6.

Code reuse techniques can circumvent existing security measures. For example, attacks such as Return Oriented Programming (ROP) use fragments of the existing code base to create an attack. Since this code is already in the system, the Data Execution Prevention methods cannot prevent the execution of this reorganised code. Existing software-based Control Flow Integrity can prevent this attack, but the overhead is enormous. Most of the improved methods utilise reduced granularity in exchange for a small performance overhead. Hardware-based detection also faces the same performance overhead and accuracy issues. Benefit from HPC's large-area loading on modern CPU chips, we propose a detection method based on the monitoring of hardware performance counters, which is a lightweight system-level detection for malicious code execution to solve the restrictions of other software and hardware security measures, and is not as complicated as Control Flow Integrity.

B. Yang, E. Martiri.  2015.  "Using Honey Templates to Augment Hash Based Biometric Template Protection". 2015 IEEE 39th Annual Computer Software and Applications Conference. 3:312-316.

Hash based biometric template protection schemes (BTPS), such as fuzzy commitment, fuzzy vault, and secure sketch, address the privacy leakage concern on the plain biometric template storage in a database through using cryptographic hash calculation for template verification. However, cryptographic hashes have only computational security whose being cracked shall leak the biometric feature in these BTPS; and furthermore, existing BTPS are rarely able to detect during a verification process whether a probe template has been leaked from the database or not (i.e., being used by an imposter or a genuine user). In this paper we tailor the "honeywords" idea, which was proposed to detect the hashed password cracking, to enable the detectability of biometric template database leakage. However, unlike passwords, biometric features encoded in a template cannot be renewed after being cracked and thus not straightforwardly able to be protected by the honeyword idea. To enable the honeyword idea on biometrics, diversifiability (and thus renewability) is required on the biometric features. We propose to use BTPS for his purpose in this paper and present a machine learning based protected template generation protocol to ensure the best anonymity of the generated sugar template (from a user's genuine biometric feature) among other honey ones (from synthesized biometric features).

Zhang, Caixia, Bai, Gang.  2018.  Using Hybrid Features of QR Code to Locate and Track in Augmented Reality. Proceedings of the 2018 International Conference on Information Science and System. :273–279.
Augmented Reality (AR) is a technique which seamlessly integrate virtual 3D models into the image of the real scenario in real time. Using the QR code as the identification mark, an algorithm is proposed to extract the virtual straight line of QR code and to locate and track the camera based on the hybrid features, thus it avoids the possibility of failure when locating and tracking only by feature points. The experimental results show that the method of combining straight lines with feature points is better than that of using only straight lines or feature points. Further, an AR (Augmented Reality) system is developed.
Chakraborti, Asit, Amin, Syed Obaid, Azgin, Aytac, Misra, Satyajayant, Ravindran, Ravishankar.  2018.  Using ICN Slicing Framework to Build an IoT Edge Network. Proceedings of the 5th ACM Conference on Information-Centric Networking. :214–215.
We demonstrate 5G network slicing as a unique deployment opportunity for information centric networking (ICN), by using a generic service orchestration framework that operates on commodity compute, storage, and bandwidth resource pools to realize ICN service slices. In this demo, we specifically propose a service slice for the IoT Edge network. ICN has often been considered pertinent for IoT use due to its benefits like simpler stacks on resource constrained devices, in-network caching, and in-built data provenance. We use a lightweight ICN stack on IoT devices connected with sensors and actuators to build a network, where clients can set realistic policies using their legacy hand-held devices. We employ name based authentication protocols between the service end-points and IoT devices to allow secure onboarding. The IoT slice co-exists with other service slices that cater to different classes of applications (e.g., bandwidth intensive applications, such as video conferencing) allowing resource management flexibility. Our design creates orchestrated service Edge functions to which the clients connect, and these can in turn utilize in-network stateless functions to perform tasks, such as decision making and analytics using the available compute resources efficiently.
Khatri, P..  2014.  Using identity and trust with key management for achieving security in Ad hoc Networks. Advance Computing Conference (IACC), 2014 IEEE International. :271-275.

Communication in Mobile Ad hoc network is done over a shared wireless channel with no Central Authority (CA) to monitor. Responsibility of maintaining the integrity and secrecy of data, nodes in the network are held responsible. To attain the goal of trusted communication in MANET (Mobile Ad hoc Network) lot of approaches using key management has been implemented. This work proposes a composite identity and trust based model (CIDT) which depends on public key, physical identity, and trust of a node which helps in secure data transfer over wireless channels. CIDT is a modified DSR routing protocol for achieving security. Trust Factor of a node along with its key pair and identity is used to authenticate a node in the network. Experience based trust factor (TF) of a node is used to decide the authenticity of a node. A valid certificate is generated for authentic node to carry out the communication in the network. Proposed method works well for self certification scheme of a node in the network.

Khatri, P..  2014.  Using identity and trust with key management for achieving security in Ad hoc Networks. Advance Computing Conference (IACC), 2014 IEEE International. :271-275.

Communication in Mobile Ad hoc network is done over a shared wireless channel with no Central Authority (CA) to monitor. Responsibility of maintaining the integrity and secrecy of data, nodes in the network are held responsible. To attain the goal of trusted communication in MANET (Mobile Ad hoc Network) lot of approaches using key management has been implemented. This work proposes a composite identity and trust based model (CIDT) which depends on public key, physical identity, and trust of a node which helps in secure data transfer over wireless channels. CIDT is a modified DSR routing protocol for achieving security. Trust Factor of a node along with its key pair and identity is used to authenticate a node in the network. Experience based trust factor (TF) of a node is used to decide the authenticity of a node. A valid certificate is generated for authentic node to carry out the communication in the network. Proposed method works well for self certification scheme of a node in the network.