Nakip, Mert
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Araş.Gör.
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Main Affiliation
01. Yaşar Üniversitesi
Status
Former Staff
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Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
0
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3GOOD HEALTH AND WELL-BEING
2
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4QUALITY EDUCATION
0
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5GENDER EQUALITY
0
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6CLEAN WATER AND SANITATION
1
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7AFFORDABLE AND CLEAN ENERGY
4
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8DECENT WORK AND ECONOMIC GROWTH
0
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
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10REDUCED INEQUALITIES
0
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11SUSTAINABLE CITIES AND COMMUNITIES
1
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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13CLIMATE ACTION
0
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14LIFE BELOW WATER
1
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15LIFE ON LAND
0
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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17PARTNERSHIPS FOR THE GOALS
1
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Documents
43
Citations
508
h-index
14
Documents
27
Citations
251
Scholarly Output
34
Articles
13
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0/0
Supervised MSc Theses
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Supervised PhD Theses
0
WoS Citation Count
201
Scopus Citation Count
347
Patents
0
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0
WoS Citations per Publication
5.91
Scopus Citations per Publication
10.21
Open Access Source
13
Supervised Theses
0
| Journal | Count |
|---|---|
| IEEE Access | 5 |
| 2021 Innovations in Intelligent Systems and Applications Conference ASYU 2021 | 2 |
| 2020 Innovations in Intelligent Systems and Applications Conference ASYU 2020 | 2 |
| 7th IEEE World Forum on Internet of Things WF-IoT 2021 | 2 |
| 2nd International Symposium on Security in Computer and Information Sciences EuroCybersec 2021 | 2 |
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34 results
Scholarly Output Search Results
Now showing 1 - 10 of 34
Article Citation - WoS: 19Citation - Scopus: 37Online Self-Supervised Deep Learning for Intrusion Detection Systems(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2024) Mert Nakip; Erol Gelenbe; Nakip, Mert; Gelenbe, ErolThis paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework which enables a fully online Deep Learning (DL) based Intrusion Detection System (IDS) that requires no human intervention or prior off-line learning. The proposed framework analyzes and labels incoming traffic packets based only on the decisions of the IDS itself using an Auto-Associative Deep Random Neural Network and on an online estimate of its statistically measured trustworthiness. The SSID framework enables IDS to adapt rapidly to time-varying characteristics of the network traffic and eliminates the need for offline data collection. This approach avoids human errors in data labeling and human labor and computational costs of model training and data collection. The approach is experimentally evaluated on public datasets and compared with well-known machine learning and deep learning models showing that this SSID framework is very useful and advantageous as an accurate and online learning DL-based IDS for IoT systems.Conference Object Citation - Scopus: 4Converting Utility Meters from Analogue to Smart based on Deep Learning Models(Institute of Electrical and Electronics Engineers Inc., 2020) Humberto J.Cabeza Barreto; Ilker Kurtulan; Suleyman Inci; Mert Nakıp; Cüneyt Güzeliş; Barreto, Humberto J Cabeza; Kurtulan, Ilker; Guzelis, Cuneyt; Inci, Suleyman; Nakip, MertIn this paper we proposed a system that automatically interprets the data of the utility meters by analyzing the photo of an analogue meter. In addition it sends the meter data to the consumers and the providers. We based the system on Convolutional Neural Networks (CNN) where we compared the You Only Look Once (YOLO) and a LeNet as CNN models. We collected the data for the training of each CNN model from the demonstration set of the project. Our results show that the YOLO model is reliable and fast. The model has a 99% accuracy for the gas meter and 98% accuracy for the water meter. © 2020 Elsevier B.V. All rights reserved.Article Citation - WoS: 4Citation - Scopus: 7Dynamic Automatic Forecaster Selection via Artificial Neural Network Based Emulation to Enable Massive Access for the Internet of Things(Academic Press, 2022) Mert Nakıp; Erdem Çakan; Volkan Rodoplu; Cüneyt Güzeliş; Çakan, Erdem; Rodoplu, Volkan; Güzeliş, Cüneyt; Nakıp, MertThe Massive Access Problem of the Internet of Things (IoT) occurs at the uplink Medium Access Control (MAC) layer when a massive number of IoT devices seek to transfer their data to an IoT gateway. Although recently proposed predictive access solutions that schedule the uplink traffic based on forecasts of IoT device traffic achieve high network performance these solutions depend heavily on the performance of forecasters. Hence the design and selection of forecasting schemes are key to enabling massive access for such predictive access solutions. To this end in this paper first we develop a framework that emulates the relationship between the IoT device class composition in the coverage area of an IoT gateway and the resulting network performance by virtue of an Artificial Neural Network (ANN). Second based on this framework we develop the Dynamic Automatic Forecaster Selection (DAFS) method which selects the best-performing forecasting scheme for predictive access in particular for Joint Forecasting-Scheduling (JFS) in a manner that adapts dynamically to a changing number of IoT devices in each device class in the coverage area. We evaluate the performance of DAFS via simulations and show that our method is able to achieve at least 80% of the best performance that can be attained for both throughput and energy consumption. Furthermore we demonstrate that DAFS is robust with respect to the selection of architectural parameters and has a reasonable computation time for real-time IoT applications. These results imply that DAFS holds the potential for practical implementation at IoT gateways in order to enable massive access under a dynamically changing composition of IoT devices. © 2022 Elsevier B.V. All rights reserved.Conference Object Citation - Scopus: 1An Associated Random Neural Network Detects Intrusions and Estimates Attack Graphs(IEEE Computer Society, 2024) Mert Nakıp; Erol Gelenbe; Nalip, Mert; Nakip, Mert; Gelenbe, ErolCyberattacks especially Botnet Distributed Denial of Service (DDoS) increasingly target networked systems compromise interconnected nodes by constantly spreading malware. In order to prevent these attacks in their early stages which includes stopping the spread of malware it is vital to identify compromised nodes and successfully predict potential attack paths. To this end this paper proposes a novel system based on an Associated Random Neural Network (ARNN) that simultaneously detects intrusion at the network-level and estimates the network attack graph. In this system ARNN is trained online to minimize problem-specific multi-Task loss so that it identifies compromised network nodes while the neural network connection weights also estimate the attack path. The performance of the method is calculated using the Kitsune attack dataset showing that the method achieves a recall rate above 0.95 in estimating the network attack graph and provides a near-perfect classification of compromised nodes. The ARNN-based system for dynamic and continuous estimation of compromised nodes and network attack graphs can pave the way for enhancing security measures and stopping Botnet DDoS attacks from spreading in networked systems. © 2025 Elsevier B.V. All rights reserved.Conference Object Citation - Scopus: 4Energy-Efficient Indoor Positioning for Mobile Internet of Things Based on Artificial Intelligence(Institute of Electrical and Electronics Engineers Inc., 2021) Alper Saylam; Rifat Orhan Cikmazel; Nur Kelesoglu; Mert Nakıp; Volkan Rodoplu; Saylam, Alper; Cikmazel, Rifat Orhan; Rodoplu, Volkan; Kelesoglu, Nur; Nakip, MertWe develop an energy-efficient indoor positioning system based on Artificial Intelligence (AI). In our system first at the positioning layer a Multi-Layer Perceptron (MLP) estimates the current indoor position of an IoT device based on positioning indicators obtained from the anchors. Second at the forecasting layer a pair of MLPs estimate the future positions of the device based on the past position estimates obtained when the device woke up as well as the forecast positions of the device during the sleep periods. Third the device is awakened to send a positioning beacon at intervals over which a significant displacement is predicted to occur by the forecasting layer. Our results demonstrate that our indoor positioning system saves significant energy via adaptive sleep cycles whose duration is determined by the prediction of a significant displacement. This work establishes a foundation for indoor positioning that utilizes AI-based positioning and trajectory forecasting. © 2022 Elsevier B.V. All rights reserved.Conference Object Citation - Scopus: 3Mitigating the Massive Access Problem in the Internet of Things(Springer Science and Business Media Deutschland GmbH, 2022) Erol Gelenbe; Mert Nakıp; Dariusz Marek; Tadeusz Czachórskí; Czachorski, Tadeusz; Marek, Dariusz; Nakıp, Mert; Gelenbe, Erol; E. Gelenbe , M. Jankovic , D. Kehagias , A. Marton , A. VilmosThe traffic from the large number of IoT devices connected to the IoT is a source of congestion known as the Massive Access Problem (MAP) that results in packet losses delays and missed deadlines for real-time data. This paper reviews the literature on MAP and summarizes recent results on two approaches that have been designed to mitigate MAP. One approach is based on randomizing the packet arrival instants to IoT gateways within a given time interval that is chosen so that packet arrivals do not exceed their deadlines but also so that they do not constitute bulk arrivals. The second approach is a novel traffic shaping policy named the Quasi-Deterministic-Transmission-Policy (QDTP) which has been proved to drastically reduce queue formation at the receiving gateway by delaying packet departures from the IoT devices in a judicious manner. Both analytical and experimental results are summarized that describe the benefits of these techniques. © 2022 Elsevier B.V. All rights reserved.Article Citation - WoS: 12Citation - Scopus: 18Multi-Channel Joint Forecasting-Scheduling for the Internet of Things(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020) Volkan Rodoplu; Mert Nakip; Roozbeh Qorbanian; Deniz Tursel Eliiyi; Rodoplu, Volkan; Qorbanian, Roozbeh; Nakip, Mert; Eliiyi, Deniz TurselWe develop a methodology for Multi-Channel Joint Forecasting-Scheduling (MC-JFS) targeted at solving the Medium Access Control (MAC) layer Massive Access Problem of Machine-to-Machine (M2M) communication in the presence of multiple channels as found in Orthogonal Frequency Division Multiple Access (OFDMA) systems. In contrast with the existing schemes that merely react to current traffic demand Joint Forecasting-Scheduling (JFS) forecasts the traffic generation pattern of each Internet of Things (IoT) device in the coverage area of an IoT Gateway and schedules the uplink transmissions of the IoT devices over multiple channels in advance thus obviating contention collision and handshaking which are found in reactive protocols. In this paper we present the general form of a deterministic scheduling optimization program for MC-JFS that maximizes the total number of bits that are delivered over multiple channels by the delay deadlines of the IoT applications. In order to enable real-time operation of the MC-JFS system first we design a heuristic called Multi-Channel Look Ahead Priority based on Average Load (MC-LAPAL) that solves the general form of the scheduling problem. Second for the special case of identical channels we develop a reduction technique by virtue of which an optimal solution of the scheduling problem is computed in real time. We compare the network performance of our MC-JFS scheme against Multi-Channel Reservation-based Access Barring (MC-RAB) and Multi-Channel Enhanced Reservation-based Access Barring (MC-ERAB) both of which serve as benchmark reactive protocols. Our results show that MC-JFS outperforms both MC-RAB and MC-ERAB with respect to uplink cross-layer throughput and transmit energy consumption and that MC-LAPAL provides high performance as an MC-JFS heuristic. Furthermore we show that the computation time of MC-LAPAL scales approximately linearly with the number of IoT devices. This work serves as a foundation for building scalable JFS schemes at IoT Gateways in the near future.Conference Object Citation - WoS: 3Citation - Scopus: 5Development of a Multi-Sensor Fire Detector Based On Machine Learning Models(IEEE, 2019) Mert Nakip; Cuneyt Guzelis; Guzelis, Cuneyt; Nakip, MertThis paper proposes a method to reduce false positive fire alarms by fusing data from different sensors using a specific machine learning model. We design an electronic circuit with 6 sensors to detect 7 physical sensory inputs. We experimentally collect dataset for training and testing of machine learning models which are used for the implementation of fusing and classifying sensor data. An algorithm which employs the trained machine learning model for the classification of sensor data and then the thresholding is designed. Machine learning models are selected based on the results of comparisons among multi-layer perceptron support vector machine and radial basis function network. We use classification accuracy percentage false negative error and false positive error as measures for comparison. Multi-layer perceptron is observed as the best model according to its 96.875% classification accuracy.Article Citation - WoS: 6Citation - Scopus: 6Fire detection and risk assessment via Support Vector Regression with Flattening-Samples Based Augmented Regularization(Elsevier Ltd, 2024) Mert Nakıp; Nur Kelesoglu; Cüneyt Güzeliş; Guzelis, Cueneyt; Kelesoglu, Nur; Nakip, MertWe propose a Hybrid Support Vector Regression (SVR) with Flattening-Samples Based Augmented Regularization (Hybrid FSR-SVR) architecture for multi-sensor fire detection and forest fire risk assessment. The Hybrid FSR-SVR is a lightweight architecture built upon the novel Flattening-Samples Based Augmented Regularization (FSR) approach and temporal trends of environmental variables. The FSR approach augments l2 norm based smoothing term into an l1-l2 combination facilitating the integration of l1 regularization into the SVR method thereby enhancing generalization with minimal computational load. We evaluate the performance of Hybrid FSR-SVR using two distinct datasets covering indoor and forest fires benchmarking against 15 machine learning models including state-of-the-art techniques such as Recurrent Trend Predictive Neural Network (rTPNN) Long-Short Term Memory (LSTM) Multi-Layer Perceptron (MLP) Gated Recurrent Unit (GRU) and Gradient Boosting. Our findings demonstrate that Hybrid FSR-SVR effectively assesses the risk of forest fire enabling early preventive measures. Notably it achieves a remarkable accuracy of 0.95 for forest fire detection and ranks third with 0.88 accuracy for indoor fire detection. Importantly it exhibits computation times significantly lower – by 1 to 2 orders of magnitude – than the majority of compared techniques. The superior generalization ability of Hybrid FSR-SVR facilitated by flattening-samples based augmented regularization allows for high detection performance even with smaller training sets. © 2024 Elsevier B.V. All rights reserved.Conference Object Citation - Scopus: 12Multi-Layer Perceptron Decomposition Architecture for Mobile IoT Indoor Positioning(Institute of Electrical and Electronics Engineers Inc., 2021) Erdem Çakan; Aral Sahin; Mert Nakıp; Volkan Rodoplu; Cakan, Erdem; Sahin, Aral; Rodoplu, Volkan; Nakip, MertWe develop a Multi-Layer Perceptron (MLP) Decomposition architecture for mobile Internet Things (IoT) indoor positioning. We demonstrate the performance of our architecture on an indoor system that utilizes ultra-wideband (UWB) positioning. Our architecture outperforms the following benchmark processing techniques on the same data: MLP Linear Regression Ridge Regression Support Vector Regression and the Least Squares Method for indoor positioning. The results show that our architecture can significantly advance the positioning accuracy of indoor positioning systems and enable indoor applications such as navigation proximity marketing asset tracking collision avoidance and social distancing. © 2021 Elsevier B.V. All rights reserved.
