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Ezick, James, Henretty, Tom, Baskaran, Muthu, Lethin, Richard, Feo, John, Tuan, Tai-Ching, Coley, Christopher, Leonard, Leslie, Agrawal, Rajeev, Parsons, Ben et al..  2019.  Combining Tensor Decompositions and Graph Analytics to Provide Cyber Situational Awareness at HPC Scale. 2019 IEEE High Performance Extreme Computing Conference (HPEC). :1–7.

This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combines two high-performance packages: ENSIGN for large-scale sparse tensor decompositions and HAGGLE for graph analytics. Tensor decompositions isolate coherent patterns of network behavior in ways that common clustering methods based on distance metrics cannot. Parallelized graph analysis then uses directed queries on a representation that combines the elements of identified patterns with other available information (such as additional log fields, domain knowledge, network topology, whitelists and blacklists, prior feedback, and published alerts) to confirm or reject a threat hypothesis, collect context, and raise alerts. MADHAT was developed using the collaborative HPC Architecture for Cyber Situational Awareness (HACSAW) research environment and evaluated on structured network sensor logs collected from Defense Research and Engineering Network (DREN) sites using HPC resources at the U.S. Army Engineer Research and Development Center DoD Supercomputing Resource Center (ERDC DSRC). To date, MADHAT has analyzed logs with over 650 million entries.

Eziama, Elvin, Ahmed, Saneeha, Ahmed, Sabbir, Awin, Faroq, Tepe, Kemal.  2019.  Detection of Adversary Nodes in Machine-To-Machine Communication Using Machine Learning Based Trust Model. 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). :1—6.

Security challenges present in Machine-to-Machine Communication (M2M-C) and big data paradigm are fundamentally different from conventional network security challenges. In M2M-C paradigms, “Trust” is a vital constituent of security solutions that address security threats and for such solutions,it is important to quantify and evaluate the amount of trust in the information and its source. In this work, we focus on Machine Learning (ML) Based Trust (MLBT) evaluation model for detecting malicious activities in a vehicular Based M2M-C (VBM2M-C) network. In particular, we present an Entropy Based Feature Engineering (EBFE) coupled Extreme Gradient Boosting (XGBoost) model which is optimized with Binary Particle Swarm optimization technique. Based on three performance metrics, i.e., Accuracy Rate (AR), True Positive Rate (TPR), False Positive Rate (FPR), the effectiveness of the proposed method is evaluated in comparison to the state-of-the-art ensemble models, such as XGBoost and Random Forest. The simulation results demonstrates the superiority of the proposed model with approximately 10% improvement in accuracy, TPR and FPR, with reference to the attacker density of 30% compared with the start-of-the-art algorithms.

Eziama, E., Jaimes, L. M. S., James, A., Nwizege, K. S., Balador, A., Tepe, K..  2018.  Machine Learning-Based Recommendation Trust Model for Machine-to-Machine Communication. 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). :1-6.

The Machine Type Communication Devices (MTCDs) are usually based on Internet Protocol (IP), which can cause billions of connected objects to be part of the Internet. The enormous amount of data coming from these devices are quite heterogeneous in nature, which can lead to security issues, such as injection attacks, ballot stuffing, and bad mouthing. Consequently, this work considers machine learning trust evaluation as an effective and accurate option for solving the issues associate with security threats. In this paper, a comparative analysis is carried out with five different machine learning approaches: Naive Bayes (NB), Decision Tree (DT), Linear and Radial Support Vector Machine (SVM), KNearest Neighbor (KNN), and Random Forest (RF). As a critical element of the research, the recommendations consider different Machine-to-Machine (M2M) communication nodes with regard to their ability to identify malicious and honest information. To validate the performances of these models, two trust computation measures were used: Receiver Operating Characteristics (ROCs), Precision and Recall. The malicious data was formulated in Matlab. A scenario was created where 50% of the information were modified to be malicious. The malicious nodes were varied in the ranges of 10%, 20%, 30%, 40%, and the results were carefully analyzed.

Eya, Nnabuike, Alhassan, Haru, AlAbdullah, Ali, Hameed, Khalid, Bin-Melha, Mohammed, Abd-Alhameed, Raed A..  2019.  Improved Group Key Management with Host Mobility Protocol in Wireless Mobile Environment. Proceedings of the International Conference on Information and Communication Technology. :56–64.

Group communication as an efficient communication mechanism, in recent years has become popular. This is due to the increase in group applications and services. Group communication ensures efficient delivery of packets from one source to multiple recipients or many sources to multiple recipients. Group key management in a wireless environment has been an interesting challenge with group communication because of insecure communication channel. The security and integrity of group communication in a wireless environment is a challenge. One of the challenges with group communication is the mobility of group members. Member mobility is a challenge when designing a group key management scheme. There have been several attempts that have been made to design a secure group key management for wireless environment. Not so many successful attempts have towards wireless mobile environments to explicitly address the various challenges with dynamic mobility issue between multiple networks. This research proposes a GKM scheme that tackles mobility in group communication. The protocol is analyzed to assess security and performance requirements. The size of the group variation, the mobility rate variation are carefully observed to determine the impact on the average of rekeying messages generated at every event and also 1-affects-n phenomenon. The results achieved, shows that the proposed protocol outperforms other popular solutions with less number of rekeying messages per event and also less number of affected members per event. Backward and Forward security are preserved for moving members.

EVINA, P. A., AYACHI, F. LABBENE, JAIDI, F., Bouhoula, A..  2019.  Enforcing a Risk Assessment Approach in Access Control Policies Management: Analysis, Correlation Study and Model Enhancement. 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). :1866—1871.
Nowadays, the domain of Information System (IS) security is closely related to that of Risk Management (RM). As an immediate consequence, talking about and tackling the security of IS imply the implementation of a set of mechanisms that aim to reduce or eliminate the risk of IS degradations. Also, the high cadence of IS evolution requires careful consideration of corresponding measures to prevent or mitigate security risks that may cause the degradation of these systems. From this perspective, an access control service is subjected to a number of rules established to ensure the integrity and confidentiality of the handled data. During their lifecycle, the use or manipulation of Access Control Policies (ACP) is accompanied with several defects that are made intentionally or not. For many years, these defects have been the subject of numerous studies either for their detection or for the analysis of the risks incurred by IS to their recurrence and complexity. In our research works, we focus on the analysis and risk assessment of noncompliance anomalies in concrete instances of access control policies. We complete our analysis by studying and assessing the risks associated with the correlation that may exist between different anomalies. Indeed, taking into account possible correlations can make a significant contribution to the reliability of IS. Identifying correlation links between anomalies in concrete instances of ACP contributes in discovering or detecting new scenarios of alterations and attacks. Therefore, once done, this study mainly contributes in the improvement of our risk assessment model.
Evgeny, Pavlenko, Dmitry, Zegzhda, Anna, Shtyrkina.  2019.  Estimating the sustainability of cyber-physical systems based on spectral graph theory. 2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom). :1–5.
Paper proposed an approach to estimating the sustainability of cyber-physical systems based on system state analysis. Authors suggested that sustainability is the system ability to reconfigure for recovering from attacking influences. Proposed a new criterion for cyber-physical systems sustainability assessment based on spectral graph theory. Numerical calculation of the criterion is based on distribution properties of the graph spectrum - the set of eigenvalues of the adjacency matrix corresponding to the graph. Experimental results have shown dependency of change in Δσ, difference between initial value of σstart and final σstop, on working route length, and on graph connectivity was revealed. This parameter is proposed to use as a criterion for CPS sustainability.
Evesti, Antti, Wieser, Christian, Zhao, Tiandu.  2016.  Improved Information Security Situational Awareness by Manifold Visualisation. Proccedings of the 10th European Conference on Software Architecture Workshops. :33:1–33:2.

Security situational awareness is an essential building block in order to estimate security level of systems and to decide how to protect networked systems from cyber attacks. In this extended abstract we envision a model that combines results from security metrics to 3d network visualisation. The purpose is to apply security metrics to gather data from individual hosts. Simultaneously, the whole network is visualised in a 3d format, including network hosts and their connections. The proposed model makes it possible to offer enriched situational awareness for security administrators. This can be achieved by adding information pertaining to individual host into the network level 3d visualisation. Thus, administrator can see connected hosts and how the security of these hosts differs at one glance.

Everspaugh, A., Yan Zhai, Jellinek, R., Ristenpart, T., Swift, M..  2014.  Not-So-Random Numbers in Virtualized Linux and the Whirlwind RNG. Security and Privacy (SP), 2014 IEEE Symposium on. :559-574.

Virtualized environments are widely thought to cause problems for software-based random number generators (RNGs), due to use of virtual machine (VM) snapshots as well as fewer and believed-to-be lower quality entropy sources. Despite this, we are unaware of any published analysis of the security of critical RNGs when running in VMs. We fill this gap, using measurements of Linux's RNG systems (without the aid of hardware RNGs, the most common use case today) on Xen, VMware, and Amazon EC2. Despite CPU cycle counters providing a significant source of entropy, various deficiencies in the design of the Linux RNG makes its first output vulnerable during VM boots and, more critically, makes it suffer from catastrophic reset vulnerabilities. We show cases in which the RNG will output the exact same sequence of bits each time it is resumed from the same snapshot. This can compromise, for example, cryptographic secrets generated after resumption. We explore legacy-compatible countermeasures, as well as a clean-slate solution. The latter is a new RNG called Whirlwind that provides a simpler, more-secure solution for providing system randomness.
 

Everson, Douglas, Cheng, Long.  2020.  Network Attack Surface Simplification for Red and Blue Teams. 2020 IEEE Secure Development (SecDev). :74–80.
Network port scans are a key first step to developing a true understanding of a network-facing attack surface. However in large-scale networks, the data resulting from such scans can be too numerous for Red Teams to process for manual and semiautomatic testing. Indiscriminate port scans can also compromise a Red Team seeking to quickly gain a foothold on a network. A large attack surface can even complicate Blue Team activities like threat hunting. In this paper we provide a cluster analysis methodology designed to group similar hosts to reduce security team workload and Red Team observability. We also measure the Internet-facing network attack surface of 13 organizations by clustering their hosts based on similarity. Through a case study we demonstrate how the output of our clustering technique provides new insight to both Red and Blue Teams, allowing them to quickly identify potential high-interest points on the attack surface.
Evans, David, Calvo, Daniel, Arroyo, Adrian, Manilla, Alejandro, Gómez, David.  2019.  End-to-end security assessment framework for connected vehicles. 2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC). :1–6.
To increase security and to offer user experiences according to the requirements of a hyper-connected world, modern vehicles are integrating complex electronic systems, being transformed into systems of Cyber-Physical Systems (CPS). While a great diversity of heterogeneous hardware and software components must work together and control in real-time crucial functionalities, cybersecurity for the automotive sector is still in its infancy. This paper provides an analysis of the most common vulnerabilities and risks of connected vehicles, using a real example based on industrial and market-ready technologies. Several components have been implemented to inject and simulate multiple attacks, which enable security services and mitigation actions to be developed and validated.
Eun Hee Ko, Klabjan, D..  2014.  Semantic Properties of Customer Sentiment in Tweets. Advanced Information Networking and Applications Workshops (WAINA), 2014 28th International Conference on. :657-663.

An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets semantic patterns in consumers' discussions on social media. Specifically, the purposes of this study are twofold: 1) finding similarity and dissimilarity between two sets of textual documents that include consumers' sentiment polarities, two forms of positive vs. negative opinions and 2) driving actual content from the textual data that has a semantic trend. The considered tweets include consumers' opinions on US retail companies (e.g., Amazon, Walmart). Cosine similarity and K-means clustering methods are used to achieve the former goal, and Latent Dirichlet Allocation (LDA), a popular topic modeling algorithm, is used for the latter purpose. This is the first study which discover semantic properties of textual data in consumption context beyond sentiment analysis. In addition to major findings, we apply LDA (Latent Dirichlet Allocations) to the same data and drew latent topics that represent consumers' positive opinions and negative opinions on social media.

Eugster, P., Marson, G. A., Poettering, B..  2018.  A Cryptographic Look at Multi-party Channels. 2018 IEEE 31st Computer Security Foundations Symposium (CSF). :31–45.
Cryptographic channels aim to enable authenticated and confidential communication over the Internet. The general understanding seems to be that providing security in the sense of authenticated encryption for every (unidirectional) point-to-point link suffices to achieve this goal. As recently shown (in FSE17/ToSC17), however, the security properties of the unidirectional links do not extend, in general, to the bidirectional channel as a whole. Intuitively, the reason for this is that the increased interaction in bidirectional communication can be exploited by an adversary. The same applies, a fortiori, in a multi-party setting where several users operate concurrently and the communication develops in more directions. In the cryptographic literature, however, the targeted goals for group communication in terms of channel security are still unexplored. Applying the methodology of provable security, we fill this gap by defining exact (game-based) authenticity and confidentiality goals for broadcast communication, and showing how to achieve them. Importantly, our security notions also account for the causal dependencies between exchanged messages, thus naturally extending the bidirectional case where causal relationships are automatically captured by preserving the sending order. On the constructive side we propose a modular and yet efficient protocol that, assuming only point-to-point links between users, leverages (non-cryptographic) broadcast and standard cryptographic primitives to a full-fledged broadcast channel that provably meets the security notions we put forth.
Etigowni, Sriharsha, Tian, Dave(Jing), Hernandez, Grant, Zonouz, Saman, Butler, Kevin.  2016.  CPAC: Securing Critical Infrastructure with Cyber-physical Access Control. Proceedings of the 32Nd Annual Conference on Computer Security Applications. :139–152.

Critical infrastructure such as the power grid has become increasingly complex. The addition of computing elements to traditional physical components increases complexity and hampers insight into how elements in the system interact with each other. The result is an infrastructure where operational mistakes, some of which cannot be distinguished from attacks, are more difficult to prevent and have greater potential impact, such as leaking sensitive information to the operator or attacker. In this paper, we present CPAC, a cyber-physical access control solution to manage complexity and mitigate threats in cyber-physical environments, with a focus on the electrical smart grid. CPAC uses information flow analysis based on mathematical models of the physical grid to generate policies enforced through verifiable logic. At the device side, CPAC combines symbolic execution with lightweight dynamic execution monitoring to allow non-intrusive taint analysis on programmable logic controllers in realtime. These components work together to provide a realtime view of all system elements, and allow for more robust and finer-grained protections than any previous solution to securing the grid. We implement a prototype of CPAC using Bachmann PLCs and evaluate several real-world incidents that demonstrate its scalability and effectiveness. The policy checking for a nation-wide grid is less than 150 ms, faster than existing solutions. We additionally show that CPAC can analyze potential component failures for arbitrary component failures, far beyond the capabilities of currently deployed systems. CPAC thus provides a solution to secure the modern smart grid from operator mistakes or insider attacks, maintain operational privacy, and support N - x contingencies.

Ethelbert, O., Moghaddam, F. F., Wieder, P., Yahyapour, R..  2017.  A JSON Token-Based Authentication and Access Management Schema for Cloud SaaS Applications. 2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud). :47–53.

Cloud computing is significantly reshaping the computing industry built around core concepts such as virtualization, processing power, connectivity and elasticity to store and share IT resources via a broad network. It has emerged as the key technology that unleashes the potency of Big Data, Internet of Things, Mobile and Web Applications, and other related technologies; but it also comes with its challenges - such as governance, security, and privacy. This paper is focused on the security and privacy challenges of cloud computing with specific reference to user authentication and access management for cloud SaaS applications. The suggested model uses a framework that harnesses the stateless and secure nature of JWT for client authentication and session management. Furthermore, authorized access to protected cloud SaaS resources have been efficiently managed. Accordingly, a Policy Match Gate (PMG) component and a Policy Activity Monitor (PAM) component have been introduced. In addition, other subcomponents such as a Policy Validation Unit (PVU) and a Policy Proxy DB (PPDB) have also been established for optimized service delivery. A theoretical analysis of the proposed model portrays a system that is secure, lightweight and highly scalable for improved cloud resource security and management.

Eswaraiah, Guruprasad, Subramanian, Lalitha Muthu, Vishwanathan, Roopa.  2019.  Exploring Automation in Proofs of Attribute-based Encryption in the Standard Model. 2019 17th International Conference on Privacy, Security and Trust (PST). :1–5.
Motivated by the complexity of cryptographic proofs, we propose methods to automate the construction and verification of cryptographic proofs in the standard model. Proofs in the standard model (as opposed to the random oracle model) are the gold standard of cryptographic proofs, and most cryptographic protocols strive to achieve them. The burgeoning complexity of cryptographic proofs implies that such proofs are prone to errors, and are hard to write, much less verify. In this paper, we propose techniques to generate automated proofs for attribute-based encryption schemes in the standard model, building upon a prototype tool, AutoG&P due to Barthe et al. In doing so, we significantly expand the scope of AutoG&P to support a rich set of data types such as multi-dimensional arrays, and constructs commonly used in cryptographic protocols such as monotone-access structures, and linear secret-sharing schemes. We also provide support for a extended class of pairing-based assumptions. We demonstrate the usefulness of our extensions by giving automated proofs of the Lewko et al. attribute-based encryption scheme, and the Waters' ciphertext-policy attribute-based encryption scheme.
Esteves-Verissimo, P., Völp, M., Decouchant, J., Rahli, V., Rocha, F..  2017.  Meeting the Challenges of Critical and Extreme Dependability and Security. 2017 IEEE 22nd Pacific Rim International Symposium on Dependable Computing (PRDC). :92–97.

The world is becoming an immense critical information infrastructure, with the fast and increasing entanglement of utilities, telecommunications, Internet, cloud, and the emerging IoT tissue. This may create enormous opportunities, but also brings about similarly extreme security and dependability risks. We predict an increase in very sophisticated targeted attacks, or advanced persistent threats (APT), and claim that this calls for expanding the frontier of security and dependability methods and techniques used in our current CII. Extreme threats require extreme defenses: we propose resilience as a unifying paradigm to endow systems with the capability of dynamically and automatically handling extreme adversary power, and sustaining perpetual and unattended operation. In this position paper, we present this vision and describe our methodology, as well as the assurance arguments we make for the ultra-resilient components and protocols they enable, illustrated with case studies in progress.

Estes, Tanya, Finocchiaro, James, Blair, Jean, Robison, Johnathan, Dalme, Justin, Emana, Michael, Jenkins, Luke, Sobiesk, Edward.  2016.  A Capstone Design Project for Teaching Cybersecurity to Non-technical Users. Proceedings of the 17th Annual Conference on Information Technology Education. :142–147.

This paper presents a multi-year undergraduate computing capstone project that holistically contributes to the development of cybersecurity knowledge and skills in non-computing high school and college students. We describe the student-built Vulnerable Web Server application, which is a system that packages instructional materials and pre-built virtual machines to provide lessons on cybersecurity to non-technical students. The Vulnerable Web Server learning materials have been piloted at several high schools and are now integrated into multiple security lessons in an intermediate, general education information technology course at the United States Military Academy. Our paper interweaves a description of the Vulnerable Web Server materials with the senior capstone design process that allowed it to be built by undergraduate information technology and computer science students, resulting in a valuable capstone learning experience. Throughout the paper, a call is made for greater emphasis on educating the non-technical user.

Essra, A., Sitompul, O. S., Nasution, B. Benyamin, Rahmat, R. F..  2017.  Hierarchical Graph Neuron Scheme in Classifying Intrusion Attack. 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT). :1–6.

Hierarchical Graph Neuron (HGN) is an extension of network-centric algorithm called Graph Neuron (GN), which is used to perform parallel distributed pattern recognition. In this research, HGN scheme is used to classify intrusion attacks in computer networks. Patterns of intrusion attacks are preprocessed in three steps: selecting attributes using information gain attribute evaluation, discretizing the selected attributes using entropy-based discretization supervised method, and selecting the training data using K-Means clustering algorithm. After the preprocessing stage, the HGN scheme is then deployed to classify intrusion attack using the KDD Cup 99 dataset. The results of the classification are measured in terms of accuracy rate, detection rate, false positive rate and true negative rate. The test result shows that the HGN scheme is promising and stable in classifying the intrusion attack patterns with accuracy rate reaches 96.27%, detection rate reaches 99.20%, true negative rate below 15.73%, and false positive rate as low as 0.80%.

Essam, Gehad, Shehata, Heba, Khattab, Tamer, Abualsaud, Khalid, Guizani, Mohsen.  2019.  Novel Hybrid Physical Layer Security Technique in RFID Systems. 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). :1299–1304.
In this paper, we propose a novel PHY layer security technique in radio frequency identification (RFID) backscatter communications system. In order to protect the RFID tag information confidentiality from the eavesdroppers attacks, the proposed technique deploys beam steering (BS) using a one dimensional (1-D) antenna array in the tag side in addition to noise injection from the reader side. The performance analysis and simulation results show that the new technique outperforms the already-existing noise injection security technique and overcomes its design limitations.
Essaid, Meryam, Kim, DaeYong, Maeng, Soo Hoon, Park, Sejin, Ju, Hong Taek.  2019.  A Collaborative DDoS Mitigation Solution Based on Ethereum Smart Contract and RNN-LSTM. 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). :1–6.

Recently Distributed Denial-of-Service (DDoS) are becoming more and more sophisticated, which makes the existing defence systems not capable of tolerating by themselves against wide-ranging attacks. Thus, collaborative protection mitigation has become a needed alternative to extend defence mechanisms. However, the existing coordinated DDoS mitigation approaches either they require a complex configuration or are highly-priced. Blockchain technology offers a solution that reduces the complexity of signalling DDoS system, as well as a platform where many autonomous systems (Ass) can share hardware resources and defence capabilities for an effective DDoS defence. In this work, we also used a Deep learning DDoS detection system; we identify individual DDoS attack class and also define whether the incoming traffic is legitimate or attack. By classifying the attack traffic flow separately, our proposed mitigation technique could deny only the specific traffic causing the attack, instead of blocking all the traffic coming towards the victim(s).

Essa, A., Al-Shoura, T., Nabulsi, A. Al, Al-Ali, A. R., Aloul, F..  2018.  Cyber Physical Sensors System Security: Threats, Vulnerabilities, and Solutions. 2018 2nd International Conference on Smart Grid and Smart Cities (ICSGSC). :62-67.

A Cyber Physical Sensor System (CPSS) consists of a computing platform equipped with wireless access points, sensors, and actuators. In a Cyber Physical System, CPSS constantly collects data from a physical object that is under process and performs local real-time control activities based on the process algorithm. The collected data is then transmitted through the network layer to the enterprise command and control center or to the cloud computing services for further processing and analysis. This paper investigates the CPSS' most common cyber security threats and vulnerabilities and provides countermeasures. Furthermore, the paper addresses how the CPSS are attacked, what are the leading consequences of the attacks, and the possible remedies to prevent them. Detailed case studies are presented to help the readers understand the CPSS threats, vulnerabilities, and possible solutions.

Esquivel-Quiros, Luis Gustavo, Barrantes, Elena Gabriela, Darlington, Fernando Esponda.  2018.  Measuring data privacy preserving and machine learning. 2018 7th International Conference On Software Process Improvement (CIMPS). :85–94.

The increasing publication of large amounts of data, theoretically anonymous, can lead to a number of attacks on the privacy of people. The publication of sensitive data without exposing the data owners is generally not part of the software developers concerns. The regulations for the data privacy-preserving create an appropriate scenario to focus on privacy from the perspective of the use or data exploration that takes place in an organization. The increasing number of sanctions for privacy violations motivates the systematic comparison of three known machine learning algorithms in order to measure the usefulness of the data privacy preserving. The scope of the evaluation is extended by comparing them with a known privacy preservation metric. Different parameter scenarios and privacy levels are used. The use of publicly available implementations, the presentation of the methodology, explanation of the experiments and the analysis allow providing a framework of work on the problem of the preservation of privacy. Problems are shown in the measurement of the usefulness of the data and its relationship with the privacy preserving. The findings motivate the need to create optimized metrics on the privacy preferences of the owners of the data since the risks of predicting sensitive attributes by means of machine learning techniques are not usually eliminated. In addition, it is shown that there may be a hundred percent, but it cannot be measured. As well as ensuring adequate performance of machine learning models that are of interest to the organization that data publisher.

Espinosa, Floren Alexis T., Guerrero III, Guillermo Gohan E., Vea, Larry A..  2016.  Modeling Free-form Handwriting Gesture User Authentication for Android Smartphones. Proceedings of the International Conference on Mobile Software Engineering and Systems. :3–6.

Smartphones nowadays are customized to help users with their daily tasks such as storing important data or making transactions through the internet. With the sensitivity of the data involved, authentication mechanism such as fixed-text password, PIN, or unlock patterns are used to safeguard these data against intruders. However, these mechanisms have the risk from security threats such as cracking or shoulder surfing. To enhance mobile and/or information security, this study aimed to develop a free-form handwriting gesture user authentication for smartphones. It also tried to discover the static and dynamic handwriting features that significantly influence the recognition of a legitimate user. The experiment was then conducted by asking thirty (30) individuals to draw or swipe using their fingertip their desired free-form security pattern ten (10) times. These patterns were then cleaned and processed, and extracted seven (7) static and eleven (11) dynamic handwriting features. By means of Neural Network classifier of the RapidMiner data mining tool, these features were used to develop, validate, and test a model for user authentication. The model showed a very promising recognition rate of 96.67%. The model is further tested through a prototype, and it still gave a very satisfactory result.