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Ozmen, Muslum Ozgur, Yavuz, Attila A..  2017.  Low-Cost Standard Public Key Cryptography Services for Wireless IoT Systems. Proceedings of the 2017 Workshop on Internet of Things Security and Privacy. :65–70.

Internet of Things (IoT) is an integral part of application domains such as smart-home and digital healthcare. Various standard public key cryptography techniques (e.g., key exchange, public key encryption, signature) are available to provide fundamental security services for IoTs. However, despite their pervasiveness and well-proven security, they also have been shown to be highly energy costly for embedded devices. Hence, it is a critical task to improve the energy efficiency of standard cryptographic services, while preserving their desirable properties simultaneously. In this paper, we exploit synergies among various cryptographic primitives with algorithmic optimizations to substantially reduce the energy consumption of standard cryptographic techniques on embedded devices. Our contributions are: (i) We harness special precomputation techniques, which have not been considered for some important cryptographic standards to boost the performance of key exchange, integrated encryption, and hybrid constructions. (ii) We provide self-certification for these techniques to push their performance to the edge. (iii) We implemented our techniques and their counterparts on 8-bit AVR ATmega 2560 and evaluated their performance. We used microECC library and made the implementations on NIST-recommended secp192 curve, due to its standardization. Our experiments confirmed significant improvements on the battery life (up to 7x) while preserving the desirable properties of standard techniques. Moreover, to the best of our knowledge, we provide the first open-source framework including such set of optimizations on low-end devices.

Ozkan, N., Tarhan, A. K., Gören, B., Filiz, İ, Özer, E..  2020.  Harmonizing IT Frameworks and Agile Methods: Challenges and Solutions for the case of COBIT and Scrum. 2020 15th Conference on Computer Science and Information Systems (FedCSIS). :709—719.
Information Technology (IT) is a complex domain. In order to properly manage IT related processes, several frameworks including ITIL (Information Technologies Infrastructure Library), COBIT (Control OBjectives for Information and related Technologies), IT Service CMMI (IT Service Capability Maturity Model) and many others have emerged in recent decades. Meanwhile, the prevalence of Agile methods has increased, posing the coexistence of Agile approach with different IT frameworks already adopted in organizations. More specifically, the pursuit of being agile in the area of digitalization pushes organizations to go for agile transformation while preserving full compliance to IT frameworks for the sake of their survival. The necessity for this coexistence, however, brings its own challenges and solutions for harmonizing the requirements of both parties. In this paper, we focus on harmonizing the requirements of COBIT and Scrum in a same organization, which is especially challenging when a full compliance to COBIT is expected. Therefore, this study aims to identifying the challenges of and possible solutions for the coexistence of Scrum and COBIT (version 4.1 in this case) in an organization, by considering two case studies: one from the literature and the case of Akbank delivered in this study. Thus, it extends the corresponding previous case study from two points: adds one more case study to enrich the results from the previous case study and provides more opportunity to make generalization by considering two independent cases.
Ozgur Kafali, Nirav Ajmeri, Munindar P. Singh.  2017.  Kont: Computing Tradeoffs in Normative Multiagent Systems. 31st Conference on Artificial Intelligence (AAAI).
Özer, E., İskefiyeli, M..  2017.  Detection of DDoS attack via deep packet analysis in real time systems. 2017 International Conference on Computer Science and Engineering (UBMK). :1137–1140.

One of the biggest problems of today's internet technologies is cyber attacks. In this paper whether DDoS attacks will be determined by deep packet inspection. Initially packets are captured by listening of network traffic. Packet filtering was achieved at desired number and type. These packets are recorded to database to be analyzed, daily values and average values are compared by known attack patterns and will be determined whether a DDoS attack attempts in real time systems.

Ozeer, Umar, Etchevers, Xavier, Letondeur, Loïc, Ottogalli, Fran\c cois-Gaël, Salaün, Gwen, Vincent, Jean-Marc.  2018.  Resilience of Stateful IoT Applications in a Dynamic Fog Environment. Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. :332-341.

Fog computing provides computing, storage and communication resources at the edge of the network, near the physical world. Subsequently, end devices nearing the physical world can have interesting properties such as short delays, responsiveness, optimized communications and privacy. However, these end devices have low stability and are prone to failures. There is consequently a need for failure management protocols for IoT applications in the Fog. The design of such solutions is complex due to the specificities of the environment, i.e., (i) dynamic infrastructure where entities join and leave without synchronization, (ii) high heterogeneity in terms of functions, communication models, network, processing and storage capabilities, and, (iii) cyber-physical interactions which introduce non-deterministic and physical world's space and time dependent events. This paper presents a fault tolerance approach taking into account these three characteristics of the Fog-IoT environment. Fault tolerance is achieved by saving the state of the application in an uncoordinated way. When a failure is detected, notifications are propagated to limit the impact of failures and dynamically reconfigure the application. Data stored during the state saving process are used for recovery, taking into account consistency with respect to the physical world. The approach was validated through practical experiments on a smart home platform.

Ozdemir, M. A., Elagoz, B., Soy, A. Alaybeyoglu, Akan, A..  2020.  Deep Learning Based Facial Emotion Recognition System. 2020 Medical Technologies Congress (TIPTEKNO). :1—4.

In this study, it was aimed to recognize the emotional state from facial images using the deep learning method. In the study, which was approved by the ethics committee, a custom data set was created using videos taken from 20 male and 20 female participants while simulating 7 different facial expressions (happy, sad, surprised, angry, disgusted, scared, and neutral). Firstly, obtained videos were divided into image frames, and then face images were segmented using the Haar library from image frames. The size of the custom data set obtained after the image preprocessing is more than 25 thousand images. The proposed convolutional neural network (CNN) architecture which is mimics of LeNet architecture has been trained with this custom dataset. According to the proposed CNN architecture experiment results, the training loss was found as 0.0115, the training accuracy was found as 99.62%, the validation loss was 0.0109, and the validation accuracy was 99.71%.

Oyekanlu, E..  2018.  Distributed Osmotic Computing Approach to Implementation of Explainable Predictive Deep Learning at Industrial IoT Network Edges with Real-Time Adaptive Wavelet Graphs. 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). :179–188.
Challenges associated with developing analytics solutions at the edge of large scale Industrial Internet of Things (IIoT) networks close to where data is being generated in most cases involves developing analytics solutions from ground up. However, this approach increases IoT development costs and system complexities, delay time to market, and ultimately lowers competitive advantages associated with delivering next-generation IoT designs. To overcome these challenges, existing, widely available, hardware can be utilized to successfully participate in distributed edge computing for IIoT systems. In this paper, an osmotic computing approach is used to illustrate how distributed osmotic computing and existing low-cost hardware may be utilized to solve complex, compute-intensive Explainable Artificial Intelligence (XAI) deep learning problem from the edge, through the fog, to the network cloud layer of IIoT systems. At the edge layer, the C28x digital signal processor (DSP), an existing low-cost, embedded, real-time DSP that has very wide deployment and integration in several IoT industries is used as a case study for constructing real-time graph-based Coiflet wavelets that could be used for several analytic applications including deep learning pre-processing applications at the edge and fog layers of IIoT networks. Our implementation is the first known application of the fixed-point C28x DSP to construct Coiflet wavelets. Coiflet Wavelets are constructed in the form of an osmotic microservice, using embedded low-level machine language to program the C28x at the network edge. With the graph-based approach, it is shown that an entire Coiflet wavelet distribution could be generated from only one wavelet stored in the C28x based edge device, and this could lead to significant savings in memory at the edge of IoT networks. Pearson correlation coefficient is used to select an edge generated Coiflet wavelet and the selected wavelet is used at the fog layer for pre-processing and denoising IIoT data to improve data quality for fog layer based deep learning application. Parameters for implementing deep learning at the fog layer using LSTM networks have been determined in the cloud. For XAI, communication network noise is shown to have significant impact on results of predictive deep learning at IIoT network fog layer.
Oya, Simon, Troncoso, Carmela, Pèrez-Gonzàlez, Fernando.  2019.  Rethinking Location Privacy for Unknown Mobility Behaviors. 2019 IEEE European Symposium on Security and Privacy (EuroS P). :416–431.
Location Privacy-Preserving Mechanisms (LPPMs) in the literature largely consider that users' data available for training wholly characterizes their mobility patterns. Thus, they hardwire this information in their designs and evaluate their privacy properties with these same data. In this paper, we aim to understand the impact of this decision on the level of privacy these LPPMs may offer in real life when the users' mobility data may be different from the data used in the design phase. Our results show that, in many cases, training data does not capture users' behavior accurately and, thus, the level of privacy provided by the LPPM is often overestimated. To address this gap between theory and practice, we propose to use blank-slate models for LPPM design. Contrary to the hardwired approach, that assumes known users' behavior, blank-slate models learn the users' behavior from the queries to the service provider. We leverage this blank-slate approach to develop a new family of LPPMs, that we call Profile Estimation-Based LPPMs. Using real data, we empirically show that our proposal outperforms optimal state-of-the-art mechanisms designed on sporadic hardwired models. On non-sporadic location privacy scenarios, our method is only better if the usage of the location privacy service is not continuous. It is our hope that eliminating the need to bootstrap the mechanisms with training data and ensuring that the mechanisms are lightweight and easy to compute help fostering the integration of location privacy protections in deployed systems.
Oweis, N.E., Owais, S.S., Alrababa, M.A., Alansari, M., Oweis, W.G..  2014.  A survey of Internet security risk over social networks. Computer Science and Information Technology (CSIT), 2014 6th International Conference on. :1-4.

The Communities vary from country to country. There are civil societies and rural communities, which also differ in terms of geography climate and economy. This shows that the use of social networks vary from region to region depending on the demographics of the communities. So, in this paper, we researched the most important problems of the Social Network, as well as the risk which is based on the human elements. We raised the problems of social networks in the transformation of societies to another affected by the global economy. The social networking integration needs to strengthen social ties that lead to the existence of these problems. For this we focused on the Internet security risks over the social networks. And study on Risk Management, and then look at resolving various problems that occur from the use of social networks.

Overgaard, Jacob E. F., Hertel, Jens Christian, Pejtersen, Jens, Knott, Arnold.  2018.  Application Specific Integrated Gate-Drive Circuit for Driving Self-Oscillating Gallium Nitride Logic-Level Power Transistors. 2018 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC). :1—6.
Wide bandgap power semiconductors are key enablers for increasing the power density of switch-mode power supplies. However, they require new gate drive technologies. This paper examines and characterizes a fabricated gate-driver in a class-E resonant inverter. The gate-driver's total area of 1.2mm2 includes two high-voltage transistors for gate-driving, integrated complementary metal-oxide-semiconductor (CMOS) gate-drivers, high-speed floating level-shifter and reset circuitry. A prototype printed circuit board (PCB) was designed to assess the implications of an electrostatic discharge (ESD) diode, its parasitic capacitance and package bondwire connections. The parasitic capacitance was estimated using its discharge time from an initial voltage and the capacitance is 56.7 pF. Both bondwires and the diode's parasitic capacitance is neglegible. The gate-driver's functional behaviour is validated using a parallel LC resonant tank resembling a self-oscillating gate-drive. Measurements and simulations show the ESD diode clamps the output voltage to a minimum of -2V.
Overbye, T. J., Mao, Z., Shetye, K. S., Weber, J. D..  2017.  An interactive, extensible environment for power system simulation on the PMU time frame with a cyber security application. 2017 IEEE Texas Power and Energy Conference (TPEC). :1–6.

Power system simulation environments with appropriate time-fidelity are needed to enable rapid testing of new smart grid technologies and for coupled simulations of the underlying cyber infrastructure. This paper presents such an environment which operates with power system models in the PMU time frame, including data visualization and interactive control action capabilities. The flexible and extensible capabilities are demonstrated by interfacing with a cyber infrastructure simulation.

Ouyang, Deqiang, Shao, Jie, Zhang, Yonghui, Yang, Yang, Shen, Heng Tao.  2018.  Video-Based Person Re-Identification via Self-Paced Learning and Deep Reinforcement Learning Framework. Proceedings of the 26th ACM International Conference on Multimedia. :1562–1570.

Person re-identification is an important task in video surveillance, focusing on finding the same person across different cameras. However, most existing methods of video-based person re-identification still have some limitations (e.g., the lack of effective deep learning framework, the robustness of the model, and the same treatment for all video frames) which make them unable to achieve better recognition performance. In this paper, we propose a novel self-paced learning algorithm for video-based person re-identification, which could gradually learn from simple to complex samples for a mature and stable model. Self-paced learning is employed to enhance video-based person re-identification based on deep neural network, so that deep neural network and self-paced learning are unified into one frame. Then, based on the trained self-paced learning, we propose to employ deep reinforcement learning to discard misleading and confounding frames and find the most representative frames from video pairs. With the advantage of deep reinforcement learning, our method can learn strategies to select the optimal frame groups. Experiments show that the proposed framework outperforms the existing methods on the iLIDS-VID, PRID-2011 and MARS datasets.

Oujezsky, Vaclav, Chapcak, David, Horvath, Tomas, Munster, Petr.  2019.  Security Testing Of Active Optical Network Devices. 2019 42nd International Conference on Telecommunications and Signal Processing (TSP). :9—13.

This article presents results and overview of conducted testing of active optical network devices. The base for the testing is originating in Kali Linux and penetration testing generally. The goal of tests is to either confirm or disprove a vulnerability of devices used in the tested polygon. The first part deals with general overview and topology of testing devices, the next part is dedicated to active and passive exploration and exploits. The last part provides a summary of the results.

OUIAZZANE, Said, ADDOU, Malika, BARRAMOU, Fatimazahra.  2019.  A Multi-Agent Model for Network Intrusion Detection. 2019 1st International Conference on Smart Systems and Data Science (ICSSD). :1–5.
The objective of this paper is to propose a distributed intrusion detection model based on a multi agent system. Mutli Agent Systems (MAS) are very suitable for intrusion detection systems as they meet the characteristics required by the networks and Big Data issues. The MAS agents cooperate and communicate with each other to ensure the effective detection of network intrusions without the intervention of an expert as used to be in the classical intrusion detection systems relying on signature matching to detect known attacks. The proposed model helped to detect known and unknown attacks within big computer infrastructure by responding to the network requirements in terms of distribution, autonomy, responsiveness and communication. The proposed model is capable of achieving a good and a real time intrusion detection using multi-agents paradigm and Hadoop Distributed File System (HDFS).
Ouiazzane, S., Addou, M., Barramou, F..  2020.  Toward a Network Intrusion Detection System for Geographic Data. 2020 IEEE International conference of Moroccan Geomatics (Morgeo). :1—7.

The objective of this paper is to propose a model of a distributed intrusion detection system based on the multi-agent paradigm and the distributed file system (HDFS). Multi-agent systems (MAS) are very suitable to intrusion detection systems as they can address the issue of geographic data security in terms of autonomy, distribution and performance. The proposed system is based on a set of autonomous agents that cooperate and collaborate with each other to effectively detect intrusions and suspicious activities that may impact geographic information systems. Our system allows the detection of known and unknown computer attacks without any human intervention (Security Experts) unlike traditional intrusion detection systems that rely on knowledge bases as a mechanism to detect known attacks. The proposed model allows a real time detection of known and unknown attacks within large networks hosting geographic data.

Ouffoué, G., Ortiz, A. M., Cavalli, A. R., Mallouli, W., Domingo-Ferrer, J., Sánchez, D., Zaidi, F..  2016.  Intrusion Detection and Attack Tolerance for Cloud Environments: The CLARUS Approach. 2016 IEEE 36th International Conference on Distributed Computing Systems Workshops (ICDCSW). :61–66.

The cloud has become an established and widespread paradigm. This success is due to the gain of flexibility and savings provided by this technology. However, the main obstacle to full cloud adoption is security. The cloud, as many other systems taking advantage of the Internet, is also facing threats that compromise data confidentiality and availability. In addition, new cloud-specific attacks have emerged and current intrusion detection and prevention mechanisms are not enough to protect the complex infrastructure of the cloud from these vulnerabilities. Furthermore, one of the promises of the cloud is the Quality of Service (QoS) by continuous delivery, which must be ensured even in case of intrusion. This work presents an overview of the main cloud vulnerabilities, along with the solutions proposed in the context of the H2020 CLARUS project in terms of monitoring techniques for intrusion detection and prevention, including attack-tolerance mechanisms.

Ouffoué, G., Zaidi, F., Cavalli, A. R., Lallali, M..  2017.  Model-Based Attack Tolerance. 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA). :68–73.

Software-based systems are nowadays complex and highly distributed. In contrast, existing intrusion detection mechanisms are not always suitable for protecting these systems against new and sophisticated attacks that increasingly appear. In this paper, we present a new generic approach that combines monitoring and formal methods in order to ensure attack-tolerance at a high level of abstraction. Our experiments on an authentication Web application show that this method is effective and realistic to tolerate a variety of attacks.

Ouchani, Samir, Khebbeb, Khaled, Hafsi, Meriem.  2020.  Towards Enhancing Security and Resilience in CPS: A Coq-Maude based Approach. 2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA). :1—6.
Cyber-Physical Systems (CPS) have gained considerable interest in the last decade from both industry and academia. Such systems have proven particularly complex and provide considerable challenges to master their design and ensure their functionalities. In this paper, we intend to tackle some of these challenges related to the security and the resilience of CPS at the design level. We initiate a CPS modeling approach to specify such systems structure and behaviors, analyze their inherent properties and to overcome threats in terms of security and correctness. In this initiative, we consider a CPS as a network of entities that communicate through physical and logical channels, and which purpose is to achieve a set of tasks expressed as an ordered tree. Our modeling approach proposes a combination of the Coq theorem prover and the Maude rewriting system to ensure the soundness and correctness of CPS design. The introduced solution is illustrated through an automobile manufacturing case study.
Ouali, C., Dumouchel, P., Gupta, V..  2017.  Robust video fingerprints using positions of salient regions. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :3041–3045.
This paper describes a video fingerprinting system that is highly robust to audio and video transformations. The proposed system adapts a robust audio fingerprint extraction approach to video fingerprinting. The audio fingerprinting system converts the spectrogram into binary images, and then encodes the positions of salient regions selected from each binary image. Visual features are extracted in a similar way from the video images. We propose two visual fingerprint generation methods where fingerprints encode the positions of salient regions of greyscale video images. Salient regions of the first method are selected based on the intensity values of the image, while the second method identifies the regions that represent the highest variations between two successive images. The similarity between two fingerprints is defined as the intersection between their elements. The search algorithm is speeded up by an efficient implementation on a Graphics Processing Unit (GPU). We evaluate the performance of the proposed video system on TRECVID 2009 and 2010 datasets, and we show that this system achieves promising results and outperforms other state-of-the-art video copy detection methods for queries that do not includes geometric transformations. In addition, we show the effectiveness of this system for a challenging audio+video copy detection task.
Ouaknine, Joel, Sousa-Pinto, Joao, Worrell, James.  2017.  On the Polytope Escape Problem for Continuous Linear Dynamical Systems. Proceedings of the 20th International Conference on Hybrid Systems: Computation and Control. :11–17.
The Polytope Escape Problem for continuous linear dynamical systems consists of deciding, given an affine function f:Rd -\textbackslashtextgreater Rd and a convex polytope P⊆ Rd, both with rational descriptions, whether there exists an initial point x0 in P such that the trajectory of the unique solution to the differential equation: ·x(t)=f(x(t)) x 0= x0 is entirely contained in P. We show that this problem is reducible in polynomial time to the decision version of linear programming with real algebraic coefficients. The latter is a special case of the decision problem for the existential theory of real closed fields, which is known to lie between NP and PSPACE. Our algorithm makes use of spectral techniques and relies, among others, on tools from Diophantine approximation.
Ouaknine, Joel, Pouly, Amaury, Sousa-Pinto, Joao, Worrell, James.  2016.  Solvability of Matrix-Exponential Equations. Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science. :798–806.

We consider a continuous analogue of (Babai et al. 1996)'s and (Cai et al. 2000)'s problem of solving multiplicative matrix equations. Given k + 1 square matrices A1, ..., Ak, C, all of the same dimension, whose entries are real algebraic, we examine the problem of deciding whether there exist non-negative reals t1, ..., tk such that We show that this problem is undecidable in general, but decidable under the assumption that the matrices A1, ..., Ak commute. Our results have applications to reachability problems for linear hybrid automata. Our decidability proof relies on a number of theorems from algebraic and transcendental number theory, most notably those of Baker, Kronecker, Lindemann, and Masser, as well as some useful geometric and linear-algebraic results, including the Minkowski-Weyl theorem and a new (to the best of our knowledge) result about the uniqueness of strictly upper triangular matrix logarithms of upper unitriangular matrices. On the other hand, our undecidability result is shown by reduction from Hilbert's Tenth Problem.

Ouaissa, Mariya, Rhattoy, A., Lahmer, M..  2017.  Group Access Authentication of Machine to Machine Communications in LTE Networks. Proceedings of the Second International Conference on Internet of Things, Data and Cloud Computing. :50:1–50:5.
Today Machine to Machine (M2M) communications are very expanded in many application areas. M2M devices are likely to be small and able to operate for long periods and transmit data through wireless links, it is also defined as machine type communication (MTC) in Release 10 of the 3GPP "3rd Generation Partnership Project". Recently, most research has focused on congestion control, sensing information and control technologies and resource management, etc, but there are not many studies on the security aspects. Indeed, M2M communications and equipments may be exposed to different types of attacks (physical attacks on equipment and recovery of sensitive data, configurations attacks to compromise the software, attacks on the communications protocol, etc). In this article we introduce security into the M2M architecture and discuss the most important question of security, which is the group access authentication by modifying existing authentication protocols, such as group authentication and key agreement protocol used to resolve the group access authentication for M2M.
Ou, Yifan, Deng, Bin, Liu, Xuan, Zhou, Ke.  2019.  Local Outlier Factor Based False Data Detection in Power Systems. 2019 IEEE Sustainable Power and Energy Conference (iSPEC). :2003—2007.
The rapid developments of smart grids provide multiple benefits to the delivery of electric power, but at the same time makes the power grids under the threat of cyber attackers. The transmitted data could be deliberately modified without triggering the alarm of bad data detection procedure. In order to ensure the stable operation of the power systems, it is extremely significant to develop effective abnormal detection algorithms against injected false data. In this paper, we introduce the density-based LOF algorithm to detect the false data and dummy data. The simulation results show that the traditional density-clustering based LOF algorithm can effectively identify FDA, but the detection performance on DDA is not satisfactory. Therefore, we propose the improved LOF algorithm to detect DDA by setting reasonable density threshold.
Ou, Chung-Ming.  2019.  Host-based Intrusion Detection Systems Inspired by Machine Learning of Agent-Based Artificial Immune Systems. 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA). :1–5.

An adaptable agent-based IDS (AAIDS) inspired by the danger theory of artificial immune system is proposed. The learning mechanism of AAIDS is designed by emulating how dendritic cells (DC) in immune systems detect and classify danger signals. AG agent, DC agent and TC agent coordinate together and respond to system calls directly rather than analyze network packets. Simulations show AAIDS can determine several critical scenarios of the system behaviors where packet analysis is impractical.