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Kabiri, M. N., Wannous, M..  2017.  An Experimental Evaluation of a Cloud-Based Virtual Computer Laboratory Using Openstack. 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). :667–672.

In previous work, we proposed a solution to facilitate access to computer science related courses and learning materials using cloud computing and mobile technologies. The solution was positively evaluated by the participants, but most of them indicated that it lacks support for laboratory activities. As it is well known that many of computer science subjects (e.g. Computer Networks, Information Security, Systems Administration, etc.) require a suitable and flexible environment where students can access a set of computers and network devices to successfully complete their hands-on activities. To achieve this criteria, we created a cloud-based virtual laboratory based on OpenStack cloud platform to facilitate access to virtual machine both locally and remotely. Cloud-based virtual labs bring a lot of advantages, such as increased manageability, scalability, high availability and flexibility, to name a few. This arrangement has been tested in a case-study exercise with a group of students as part of Computer Networks and System Administration courses at Kabul Polytechnic University in Afghanistan. To measure success, we introduced a level test to be completed by participants prior and after the experiment. As a result, the learners achieved an average of 17.1 % higher scores in the post level test after completing the practical exercises. Lastly, we distributed a questionnaire after the experiment and students provided positive feedback on the effectiveness and usefulness of the proposed solution.

Kabiri, Peyman, Chavoshi, Mahdieh.  2019.  Destructive Attacks Detection and Response System for Physical Devices in Cyber-Physical Systems. 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1–6.

Nowadays, physical health of equipment controlled by Cyber-Physical Systems (CPS) is a significant concern. This paper reports a work, in which, a hardware is placed between Programmable Logic Controller (PLC) and the actuator as a solution. The proposed hardware operates in two conditions, i.e. passive and active. Operation of the proposed solution is based on the repetitive operational profile of the actuators. The normal operational profile of the actuator is fed to the protective hardware and is considered as the normal operating condition. In the normal operating condition, the middleware operates in its passive mode and simply monitors electronic signals passing between PLC and Actuator. In case of any malicious operation, the proposed hardware operates in its active mode and both slowly stops the actuator and sends an alert to SCADA server initiating execution of the actuator's emergency profile. Thus, the proposed hardware gains control over the actuator and prevents any physical damage on the operating devices. Two sample experiments are reported in which, results of implementing the proposed solution are reported and assessed. Results show that once the PLC sends incorrect data to actuator, the proposed hardware detects it as an anomaly. Therefore, it does not allow the PLC to send incorrect and unauthorized data pattern to its actuator. Significance of the paper is in introducing a solution to prevent destruction of physical devices apart from source or purpose of the encountered anomaly and apart from CPS functionality or PLC model and operation.

Kaci, A., Kamwa, I., Dessaint, L.A., Guillon, S..  2014.  Synchrophasor Data Baselining and Mining for Online Monitoring of Dynamic Security Limits. Power Systems, IEEE Transactions on. 29:2681-2695.

When the system is in normal state, actual SCADA measurements of power transfers across critical interfaces are continuously compared with limits determined offline and stored in look-up tables or nomograms in order to assess whether the network is secure or insecure and inform the dispatcher to take preventive action in the latter case. However, synchrophasors could change this paradigm by enabling new features, the phase-angle differences, which are well-known measures of system stress, with the added potential to increase system visibility. The paper develops a systematic approach to baseline the phase-angles versus actual transfer limits across system interfaces and enable synchrophasor-based situational awareness (SBSA). Statistical methods are first used to determine seasonal exceedance levels of angle shifts that can allow real-time scoring and detection of atypical conditions. Next, key buses suitable for SBSA are identified using correlation and partitioning around medoid (PAM) clustering. It is shown that angle shifts of this subset of 15% of the network backbone buses can be effectively used as features in ensemble decision tree-based forecasting of seasonal security margins across critical interfaces.
 

Kaci, A., Kamwa, I., Dessaint, L.-A., Guillon, S..  2014.  Phase angles as predictors of network dynamic security limits and further implications. PES General Meeting | Conference Exposition, 2014 IEEE. :1-6.

In the United States, the number of Phasor Measurement Units (PMU) will increase from 166 networked devices in 2010 to 1043 in 2014. According to the Department of Energy, they are being installed in order to “evaluate and visualize reliability margin (which describes how close the system is to the edge of its stability boundary).” However, there is still a lot of debate in academia and industry around the usefulness of phase angles as unambiguous predictors of dynamic stability. In this paper, using 4-year of actual data from Hydro-Québec EMS, it is shown that phase angles enable satisfactory predictions of power transfer and dynamic security margins across critical interface using random forest models, with both explanation level and R-squares accuracy exceeding 99%. A generalized linear model (GLM) is next implemented to predict phase angles from day-ahead to hour-ahead time frames, using historical phase angles values and load forecast. Combining GLM based angles forecast with random forest mapping of phase angles to power transfers result in a new data-driven approach for dynamic security monitoring.
 

Kacimi, Zineb, Benhlima, Laila.  2017.  XACML Policies into mongoDB for Privacy Access Control. Proceedings of the Mediterranean Symposium on Smart City Application. :9:1–9:5.

Nowadays Big data is considered as one of the major technologies used to manage a huge number of data, but there is little consideration of privacy in big data platforms. Indeed, developers don't focus on implementing security best practices in their programs to protect personal and sensitive data, and organizations can face financial lost because of this noncompliance with applied regulations. In this paper, we propose a solution to insert privacy policies written in XACML (eXtensible Access Control Markup Language) in access control solution to NoSQL database, our solution can be used for NoSQL data store which doesn't t include many access control features, it aims basically to ensure fine grained access control considering purpose as the main parameter, we will focus on access control in document level, and apply this approach to MongoDB which is the most used NoSQL data store.

Kaczmarek, J., Wrobel, M.R..  2014.  Operating system security by integrity checking and recovery using write-protected storage. Information Security, IET. 8:122-131.

An integrity checking and recovery (ICAR) system is presented here, which protects file system integrity and automatically restores modified files. The system enables files cryptographic hashes generation and verification, as well as configuration of security constraints. All of the crucial data, including ICAR system binaries, file backups and hashes database are stored in a physically write-protected storage to eliminate the threat of unauthorised modification. A buffering mechanism was designed and implemented in the system to increase operation performance. Additionally, the system supplies user tools for cryptographic hash generation and security database management. The system is implemented as a kernel extension, compliant with the Linux security model. Experimental evaluation of the system was performed and showed an approximate 10% performance degradation in secured file access compared to regular access.
 

Kadampot, Ishaque Ashar, Tahmasbi, Mehrdad, Bloch, Matthieu R.  2019.  Codes for Covert Communication over Additive White Gaussian Noise Channels. 2019 IEEE International Symposium on Information Theory (ISIT). :977—981.

We propose a coding scheme for covert communication over additive white Gaussian noise channels, which extends a previous construction for discrete memoryless channels. We first show how sparse signaling with On-Off keying fails to achieve the covert capacity but that a modification allowing the use of binary phase-shift keying for "on" symbols recovers the loss. We then construct a modified pulse-position modulation scheme that, combined with multilevel coding, can achieve the covert capacity with low-complexity error-control codes. The main contribution of this work is to reconcile the tension between diffuse and sparse signaling suggested by earlier information-theoretic results.

Kadebu, Prudence, Thada, Vikas, Chiurunge, Panashe.  2018.  Natural Language Processing and Deep Learning Towards Security Requirements Classification. 2018 3rd International Conference on Contemporary Computing and Informatics (IC3I). :135–140.
Security Requirements classification is an important area to the Software Engineering community in order to build software that is secure, robust and able to withstand attacks. This classification facilitates proper analysis of security requirements so that adequate security mechanisms are incorporated in the development process. Machine Learning techniques have been used in Security Requirements classification to aid in the process that lead to ensuring that correct security mechanisms are designed corresponding to the Security Requirements classifications made to eliminate the risk of security being incorporated in the late stages of development. However, these Machine Learning techniques have been found to have problems including, handcrafting of features, overfitting and failure to perform well with high dimensional data. In this paper we explore Natural Language Processing and Deep Learning to determine if this can be applied to Security Requirements classification.
Kadhe, S., Sprintson, A..  2017.  Security for Minimum Storage Regenerating Codes and Locally Repairable Codes. 2017 IEEE International Symposium on Information Theory (ISIT). :1028–1032.

We consider the problem of designing repair efficient distributed storage systems, which are information-theoretically secure against a passive eavesdropper that can gain access to a limited number of storage nodes. We present a framework that enables design of a broad range of secure storage codes through a joint construction of inner and outer codes. As case studies, we focus on two specific families of storage codes: (i) minimum storage regenerating (MSR) codes, and (ii) maximally recoverable (MR) codes, which are a class of locally repairable codes (LRCs). The main idea of this framework is to utilize the existing constructions of storage codes to jointly design an outer coset code and inner storage code. Finally, we present a construction of an outer coset code over small field size to secure locally repairable codes presented by Tamo and Barg for the special case of an eavesdropper that can observe any subset of nodes of maximum possible size.

Kadhim, H., Hatem, M. A..  2019.  Secure Data Packet in MANET Based Chaos-Modified AES Algorithm. 2019 2nd International Conference on Engineering Technology and its Applications (IICETA). :208–213.
Security is one of the more challenging problem for wireless Ad-Hoc networks specially in MANT due their features like dynamic topology, no centralized infrastructure, open architecture, etc. that make its more prone to different attacks. These attacks can be passive or active. The passive attack it hard to detect it in the network because its targets the confidential of data packet by eavesdropping on it. Therefore, the privacy preservation for data packets payload which it transmission over MANET has been a major part of concern. especially for safety-sensitive applications such as, privacy conference meetings, military applications, etc. In this paper it used symmetric cryptography to provide privacy for data packet by proposed modified AES based on five proposed which are: Key generation based on multi chaotic system, new SubByte, new ShiftRows, Add-two-XOR, Add-Shiftcycl.
Kadhim, Hakem Adil, AbdulRashidx, NurAini.  2014.  Maximum-shift string matching algorithms. Computer and Information Sciences (ICCOINS), 2014 International Conference on. :1-6.

The string matching algorithms have broad applications in many areas of computer sciences. These areas include operating systems, information retrieval, editors, Internet searching engines, security applications and biological applications. Two important factors used to evaluate the performance of the sequential string matching algorithms are number of attempts and total number of character comparisons during the matching process. This research proposes to integrate the good properties of three single string matching algorithms, Quick-Search, Zuh-Takaoka and Horspool, to produce hybrid string matching algorithm called Maximum-Shift algorithm. Three datasets are used to test the proposed algorithm, which are, DNA, Protein sequence and English text. The hybrid algorithm, Maximum-Shift, shows efficient results compared to four string matching algorithms, Quick-Search, Horspool, Smith and Berry-Ravindran, in terms of the number of attempts and the total number of character comparisons.
 

Kadhim, Y., Mishra, A..  2019.  Radial Basis Function (RBF) Based on Multistage Autoencoders for Intrusion Detection system (IDS). 2019 1st International Informatics and Software Engineering Conference (UBMYK). :1—4.

In this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to detect IDS attacks with 98.80% accuracy when validated using UNSW-NB15 dataset. The experimental results show the proposed method presents satisfactory results when compared with those obtained in this field.

Kadoguchi, M., Kobayashi, H., Hayashi, S., Otsuka, A., Hashimoto, M..  2020.  Deep Self-Supervised Clustering of the Dark Web for Cyber Threat Intelligence. 2020 IEEE International Conference on Intelligence and Security Informatics (ISI). :1—6.

In recent years, cyberattack techniques have become more and more sophisticated each day. Even if defense measures are taken against cyberattacks, it is difficult to prevent them completely. It can also be said that people can only fight defensively against cyber criminals. To address this situation, it is necessary to predict cyberattacks and take appropriate measures in advance, and the use of intelligence is important to make this possible. In general, many malicious hackers share information and tools that can be used for attacks on the dark web or in the specific communities. Therefore, we assume that a lot of intelligence, including this illegal content exists in cyber space. By using the threat intelligence, detecting attacks in advance and developing active defense is expected these days. However, such intelligence is currently extracted manually. In order to do this more efficiently, we apply machine learning to various forum posts that exist on the dark web, with the aim of extracting forum posts containing threat information. By doing this, we expect that detecting threat information in cyber space in a timely manner will be possible so that the optimal preventive measures will be taken in advance.

KADOGUCHI, Masashi, HAYASHI, Shota, HASHIMOTO, Masaki, OTSUKA, Akira.  2019.  Exploring the Dark Web for Cyber Threat Intelligence Using Machine Leaning. 2019 IEEE International Conference on Intelligence and Security Informatics (ISI). :200–202.

In recent years, cyber attack techniques are increasingly sophisticated, and blocking the attack is more and more difficult, even if a kind of counter measure or another is taken. In order for a successful handling of this situation, it is crucial to have a prediction of cyber attacks, appropriate precautions, and effective utilization of cyber intelligence that enables these actions. Malicious hackers share various kinds of information through particular communities such as the dark web, indicating that a great deal of intelligence exists in cyberspace. This paper focuses on forums on the dark web and proposes an approach to extract forums which include important information or intelligence from huge amounts of forums and identify traits of each forum using methodologies such as machine learning, natural language processing and so on. This approach will allow us to grasp the emerging threats in cyberspace and take appropriate measures against malicious activities.

Kaelbling, L. P., Lozano-Pérez, T..  2017.  Learning composable models of parameterized skills. 2017 IEEE International Conference on Robotics and Automation (ICRA). :886–893.

There has been a great deal of work on learning new robot skills, but very little consideration of how these newly acquired skills can be integrated into an overall intelligent system. A key aspect of such a system is compositionality: newly learned abilities have to be characterized in a form that will allow them to be flexibly combined with existing abilities, affording a (good!) combinatorial explosion in the robot's abilities. In this paper, we focus on learning models of the preconditions and effects of new parameterized skills, in a form that allows those actions to be combined with existing abilities by a generative planning and execution system.

Kafai, M., Eshghi, K., Bhanu, B..  2014.  Discrete Cosine Transform Locality-Sensitive Hashes for Face Retrieval. Multimedia, IEEE Transactions on. 16:1090-1103.

Descriptors such as local binary patterns perform well for face recognition. Searching large databases using such descriptors has been problematic due to the cost of the linear search, and the inadequate performance of existing indexing methods. We present Discrete Cosine Transform (DCT) hashing for creating index structures for face descriptors. Hashes play the role of keywords: an index is created, and queried to find the images most similar to the query image. Common hash suppression is used to improve retrieval efficiency and accuracy. Results are shown on a combination of six publicly available face databases (LFW, FERET, FEI, BioID, Multi-PIE, and RaFD). It is shown that DCT hashing has significantly better retrieval accuracy and it is more efficient compared to other popular state-of-the-art hash algorithms.
 

Kafali, Ö, Jones, J., Petruso, M., Williams, L., Singh, M. P..  2017.  How Good Is a Security Policy against Real Breaches? A HIPAA Case Study 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE). :530–540.

Policy design is an important part of software development. As security breaches increase in variety, designing a security policy that addresses all potential breaches becomes a nontrivial task. A complete security policy would specify rules to prevent breaches. Systematically determining which, if any, policy clause has been violated by a reported breach is a means for identifying gaps in a policy. Our research goal is to help analysts measure the gaps between security policies and reported breaches by developing a systematic process based on semantic reasoning. We propose SEMAVER, a framework for determining coverage of breaches by policies via comparison of individual policy clauses and breach descriptions. We represent a security policy as a set of norms. Norms (commitments, authorizations, and prohibitions) describe expected behaviors of users, and formalize who is accountable to whom and for what. A breach corresponds to a norm violation. We develop a semantic similarity metric for pairwise comparison between the norm that represents a policy clause and the norm that has been violated by a reported breach. We use the US Health Insurance Portability and Accountability Act (HIPAA) as a case study. Our investigation of a subset of the breaches reported by the US Department of Health and Human Services (HHS) reveals the gaps between HIPAA and reported breaches, leading to a coverage of 65%. Additionally, our classification of the 1,577 HHS breaches shows that 44% of the breaches are accidental misuses and 56% are malicious misuses. We find that HIPAA's gaps regarding accidental misuses are significantly larger than its gaps regarding malicious misuses.

Kafash, S. H., Giraldo, J., Murguia, C., Cárdenas, A. A., Ruths, J..  2018.  Constraining Attacker Capabilities Through Actuator Saturation. 2018 Annual American Control Conference (ACC). :986–991.
For LTI control systems, we provide mathematical tools - in terms of Linear Matrix Inequalities - for computing outer ellipsoidal bounds on the reachable sets that attacks can induce in the system when they are subject to the physical limits of the actuators. Next, for a given set of dangerous states, states that (if reached) compromise the integrity or safe operation of the system, we provide tools for designing new artificial limits on the actuators (smaller than their physical bounds) such that the new ellipsoidal bounds (and thus the new reachable sets) are as large as possible (in terms of volume) while guaranteeing that the dangerous states are not reachable. This guarantees that the new bounds cut as little as possible from the original reachable set to minimize the loss of system performance. Computer simulations using a platoon of vehicles are presented to illustrate the performance of our tools.
Kafedziski, Venceslav.  2019.  Compressive Sampling Stepped Frequency Ground Penetrating Radar Using Group Sparsity and Markov Chain Sparsity Model. 2019 14th International Conference on Advanced Technologies, Systems and Services in Telecommunications (℡SIKS). :265–268.
We investigate an implementation of a compressive sampling (CS) stepped frequency ground penetrating radar. Due to the small number of targets, the B-scan is represented as a sparse image. Due to the nature of stepped frequency radar, smaller number of random frequencies can be used to obtain each A-scan (sparse delays). Also, the measurements obtained from different antenna positions can be reduced to a smaller number of random antenna positions. We also use the structure in the B-scan, i.e. the shape of the targets, which can be known, for instance, when detecting land mines. We demonstrate our method using radar data available from the Web from the land mine targets buried in the ground. We use group sparsity, i.e. we assume that the targets have some non-zero (and presumably known) dimension in the cross-range coordinate of the B-scan. For such targets, we also use the Markov chain model for the targets, where we simultaneously estimate the model parameters using the EMturboGAMP algorithm. Both approaches result in improved performance.
Kaftannikov, I. L., Kozlova, A. V., Khlyzov, A. D..  2020.  Prototype of a Li-Fi Communication System for Data Exchange Between Mobile Devices. 2020 Global Smart Industry Conference (GloSIC). :192—198.

This paper deals with the design and development of a Li-Fi (light fidelity) simplex communication system for data exchange between Android mobile devices. Li-Fi is an up-to-date technology in the modern world, since it uses visible light for data exchange, allowing for high-speed communication. The paper includes a brief review of Li-Fi technology, a review of the literature used, and a study of technological methods for implementing such systems, based on scientific sources. We propose the algorithms for data exchange, packet formation, and encryption-decryption. The paper presents the developed mobile application and the transceiver device, the development results, as well as experiments with the developed prototype. The results show that Li-Fi technology is workable and is a good alternative to existing communication methods.

Kaghaz-Garan, S., Umbarkar, A., Doboli, A..  2014.  Joint localization and fingerprinting of sound sources for auditory scene analysis. Robotic and Sensors Environments (ROSE), 2014 IEEE International Symposium on. :49-54.

In the field of scene understanding, researchers have mainly focused on using video/images to extract different elements in a scene. The computational as well as monetary cost associated with such implementations is high. This paper proposes a low-cost system which uses sound-based techniques in order to jointly perform localization as well as fingerprinting of the sound sources. A network of embedded nodes is used to sense the sound inputs. Phase-based sound localization and Support-Vector Machine classification are used to locate and classify elements of the scene, respectively. The fusion of all this data presents a complete “picture” of the scene. The proposed concepts are applied to a vehicular-traffic case study. Experiments show that the system has a fingerprinting accuracy of up to 97.5%, localization error less than 4 degrees and scene prediction accuracy of 100%.

Kahani, Nafiseh, Fallah, Mehran S..  2018.  A Reactive Defense Against Bandwidth Attacks Using Learning Automata. Proceedings of the 13th International Conference on Availability, Reliability and Security. :31:1-31:6.

This paper proposes a new adaptively distributed packet filtering mechanism to mitigate the DDoS attacks targeted at the victim's bandwidth. The mechanism employs IP traceback as a means of distinguishing attacks from legitimate traffic, and continuous action reinforcement learning automata, with an improved learning function, to compute effective filtering probabilities at filtering routers. The solution is evaluated through a number of experiments based on actual Internet data. The results show that the proposed solution achieves a high throughput of surviving legitimate traffic as a result of its high convergence speed, and can save the victim's bandwidth even in case of varying and intense attacks.

Kahla, Mostafa, Azab, Mohamed, Mansour, Ahmed.  2018.  Secure, Resilient, and Self-Configuring Fog Architecture for Untrustworthy IoT Environments. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :49—54.

The extensive increase in the number of IoT devices and the massive data generated and sent to the cloud hinder the cloud abilities to handle it. Further, some IoT devices are latency-sensitive. Such sensitivity makes it harder for far clouds to handle the IoT needs in a timely manner. A new technology named "Fog computing" has emerged as a solution to such problems. Fog computing relies on close by computational devices to handle the conventional cloud load. However, Fog computing introduced additional problems related to the trustworthiness and safety of such devices. Unfortunately, the suggested architectures did not consider such problem. In this paper we present a novel self-configuring fog architecture to support IoT networks with security and trust in mind. We realize the concept of Moving-target defense by mobilizing the applications inside the fog using live migrations. Performance evaluations using a benchmark for mobilized applications showed that the added overhead of live migrations is very small making it deployable in real scenarios. Finally, we presented a mathematical model to estimate the survival probabilities of both static and mobile applications within the fog. Moreover, this work can be extended to other systems such as mobile ad-hoc networks (MANETS) or in vehicular cloud computing (VCC).

Kahvazadeh, Sarang, Masip-Bruin, Xavi, Díaz, Rodrigo, Marín-Tordera, Eva, Jurnet, Alejandro, Garcia, Jordi, Juan, Ana, Simó, Ester.  2019.  Balancing Security Guarantees vs QoS Provisioning in Combined Fog-to-Cloud Systems. 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS). :1–6.

Several efforts are currently active in dealing with scenarios combining fog, cloud computing, out of which a significant proportion is devoted to control, and manage the resulting scenario. Certainly, although many challenging aspects must be considered towards the design of an efficient management solution, it is with no doubt that whatever the solution is, the quality delivered to the users when executing services and the security guarantees provided to the users are two key aspects to be considered in the whole design. Unfortunately, both requirements are often non-convergent, thus making a solution suitably addressing both aspects is a challenging task. In this paper, we propose a decoupled transversal security strategy, referred to as DCF, as a novel architectural oriented policy handling the QoS-Security trade-off, particularly designed to be applied to combined fog-to-cloud systems, and specifically highlighting its impact on the delivered QoS.

Kaiafas, G., Varisteas, G., Lagraa, S., State, R., Nguyen, C. D., Ries, T., Ourdane, M..  2018.  Detecting Malicious Authentication Events Trustfully. NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium. :1-6.

Anomaly detection on security logs is receiving more and more attention. Authentication events are an important component of security logs, and being able to produce trustful and accurate predictions minimizes the effort of cyber-experts to stop false attacks. Observed events are classified into Normal, for legitimate user behavior, and Malicious, for malevolent actions. These classes are consistently excessively imbalanced which makes the classification problem harder; in the commonly used Los Alamos dataset, the malicious class comprises only 0.00033% of the total. This work proposes a novel method to extract advanced composite features, and a supervised learning technique for classifying authentication logs trustfully; the models are Random Forest, LogitBoost, Logistic Regression, and ultimately Majority Voting which leverages the predictions of the previous models and gives the final prediction for each authentication event. We measure the performance of our experiments by using the False Negative Rate and False Positive Rate. In overall we achieve 0 False Negative Rate (i.e. no attack was missed), and on average a False Positive Rate of 0.0019.