Mert Nakıp

• Doctor of Philosophy
• Research Assistant at Polish Academy of Sciences

We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings. The rTPNN model significantly differs from the existing methods due to recurrent sensor data processing employed in its architecture. rTPNN performs trend prediction and level p...

Internet connected IoT devices have often been particularly vulnerable to Botnet attacks of the Mirai family in recent years. Thus we develop an attack detection scheme for Mirai Botnets, using the Auto-Associative Dense Random Neural Network that has recently been successful for other attacks such as the SYN attack. The resulting method is trained...

Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper...

This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online Machine Learning (ML) 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...

Electronic Nose (E-Nose) systems, widely applied across diverse fields, have revolutionized quality control, disease diagnostics, and environmental management through their odor detection and analysis capabilities. The decision and analysis of E-Nose systems often enabled by Machine Learning (ML) models that are trained offline using existing datas...

This 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 usi...

In the software rich environment of 6G, systems will be surrounded by edge devices that support distributed software systems which are critical to operations. Such systems may also be subject to frequent updates or uploads of individual software components. Trust in such systems will therefore depend on our ability to rapidly ensure that such softw...

This paper presents several novel algorithms for real-time cyberattack detection using the Auto-Associative Deep Random Neural Network. Some of these algorithms require offline learning, while others allow the algorithm to learn during its normal operation while it is also testing the flow of incoming traffic to detect possible attacks. Most of the...

Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper...

Cyberattacks are increasingly threatening networked systems, often with the emergence of new types of unknown (zero-day) attacks and the rise of vulnerable devices. While Machine Learning (ML)-based Intrusion Detection Systems (IDSs) have been shown to be extremely promising in detecting these attacks, the need to learn large amounts of labelled da...

The IoT's vulnerability to network attacks has motivated the design of intrusion detection schemes (IDS) using Machine Learning (ML), with a low computational cost for online detection but intensive offline learning. Such IDS can have high attack detection accuracy and are easily installed on servers that communicate with IoT devices. However, they...

This paper presents several novel algorithms for real-time cyberattack detection using the Auto-Associative Deep Random Neural Network, which were developed in the HORIZON 2020 IoTAC Project. Some of these algorithms require offline learning, while others require the algorithm to learn during its normal operation while it is also testing the flow o...

This paper proposes a method to assess the security of an n device, or IP address, IoT network by simultaneously identifying all the compromised IoT devices and IP addresses. It uses a specific Random Neural Network (RNN) architecture composed of two mutually interconnected sub-networks that complement each other in a recurrent structure, called...

Predictive solution techniques have been developed recently in order to solve the Massive Access Problem of the Internet of Things (IoT). These techniques forecast the traffic generation patterns of individual IoT devices in the coverage area of an IoT gateway and schedule the MAC-layer resources at the gateway in advance based on these forecasts....

A novel online Compromised Device Identification System (CDIS) is presented to identify IoT devices and/or IP addresses that are compromised by a Botnet attack, within a set of sources and destinations that transmit packets. The method uses specific metrics that are selected for this purpose and which are easily extracted from network traffic, and...

IoT networks handle incoming packets from large numbers of IoT Devices (IoTDs) to IoT Gateways. This can lead to the IoT Massive Access Problem that causes buffer overflow, large end-to-end delays and missed deadlines. This paper analyzes a novel traffic shaping method named the Quasi-Deterministic Traffic Policy (QDTP) that mitigates this problem...

The 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. On...

Most of the existing Medium Access Control (MAC) layer protocols for the Internet of Things (IoT) model the traffic generated by each IoT device via random arrivals such as those in a Poisson process. Under this model, since it is implied that IoT device traffic cannot be predicted, only reactive MAC-layer protocols in which the network responds to...

The COVID-19 pandemic has affected almost all countries from 2020 to 2022. During this period,numerous attempts have been made to predict the number of cases and other future trends of the pandemic.However, they fail to reliably predict the medium and long term evolution of the key featuresof the COVID-19 pandemic. This paper explains the possible...

Recognition of user interaction, in particular engagement detection, became highly crucial for online working and learning environments, especially during the COVID-19 outbreak. Such recognition and detection systems significantly improve the user experience and efficiency by providing valuable feedback. In this paper, we propose a novel Engagement...

The 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. On...

Recognition of user interaction, in particular engagement detection, became highly crucial for online working and learning environments, especially during the COVID-19 outbreak. Such recognition and detection systems significantly improve the user experience and efficiency by providing valuable feedback. In this paper, we propose a novel Engagement...

Critical everyday activities handled by modern IoT Systems imply that security is of major concern both for the end-users and the industry. Securing the IoT System Architecture is commonly used to strengthen its resilience to malicious attacks. However, the security of software running on the IoT must be considered as well, since the exploitation o...

The 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 pe...

In recent years, IoT devices have often been the target of Mirai Botnet attacks. This paper develops an intrusion detection method based on Auto-Associated Dense Random Neural Network with incremental online learning, targeting the detection of Mirai Botnet attacks. The proposed method is trained only on benign IoT traffic while the IoT network is...

Distributing the peak load and alleviating grid stress by considering hourly electricity prices are some of the main research problems for current smart grid systems. This paper deals with the scheduling problem of home appliances' operating hours in smart grids, which aims to achieve minimum cost in user-defined operation intervals. To this end, s...

We develop an algorithm called "Dynamic Positioning Interval based on Reciprocal Forecasting Error (DPIRFE)" for energy-efficient mobile Internet of Things (IoT) Indoor Positioning (IP). In contrast with existing IP algorithms, DPIRFE forecasts the future trajectory of a mobile IoT device by using machine learning and dynamically adjusts the positi...

In recent years, IoT devices have often been the target of Mi-rai Botnet attacks. This paper develops an intrusion detection method based on Auto-Associated Dense Random Neural Network with incremental online learning, targeting the detection of Mirai Botnet attacks. The proposed method is trained only on benign IoT traffic while the IoT network is...

This paper gives an explanation for the failure of machine learning models for the prediction of the cases and the other future trends of Covid-19 pandemic. The paper shows that simple Linear Regression models provide high prediction accuracy values reliably but only for a 2-weeks period and that relatively complex machine learning models, which ha...

A significant challenge of IoT networks is to offer Quality of Service (QoS) and meet deadline requirements when packets from a massive number of IoT devices are forwarded to an IoT gateway. Many IoT devices tend to report their data to their wired or wireless network gateways at closely correlated instants of time, leading to congestion known as t...

A significant challenge of IoT networks is to offer Quality of Service (QoS) and meet deadline requirements when packets from a massive number of IoT devices are forwarded to an IoT gateway. Many IoT devices tend to report their data to their wired or wireless network gateways at closely correlated instants of time, leading to congestion known as t...

We develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter-based, wrapper-based and embedded feature selection methods, our architecture enables the automatic selection of features dynamically based on feature importa...

We 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 Regr...

A challenge of IoT networks is to offer Quality of Service (QoS) and meet deadline requirements when packets from numerous IoT devices must be forwarded. Thus this paper introduces the Randomization of flow Generation Times (RGT) that smooths incoming IoT traffic so that QoS improves and packet loss is avoided. When the “Earliest Deadline First” (E...

We develop a novel methodology that discovers the relationship between the forecasting error and the performance of the application that utilizes the forecasts. In our methodology, an Artificial Neural Network (ANN) learns this relationship while the forecasting error is kept inside a subspace of the entire space of forecasting errors during traini...

In this paper, in order to realize a prototype of an autonomous vehicle, we present a framework that consists of convolutional neural networks and image processing methods. The study is comprised of two main parts as software and hardware. In the hardware part, a small-sized smart video car kit is used as the prototype of the autonomous car. This p...

In 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 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 sch...

During the COVID-19 pandemic, a massive number of attempts on the predictions of the number of cases and the other future trends of this pandemic have been made. However, they fail to predict, in a reliable way, the medium and long term evolution of fundamental features of COVID-19 outbreak within acceptable accuracy. This paper gives an explanatio...

The Massive Access Problem of the Internet of Things (IoT) is the problem of enabling the wireless access of a massive number of IoT devices to the wired infrastructure. In this paper, we describe a Multi-Scale Algorithm (MSA) for joint forecasting-scheduling at a dedicated IoT Gateway to solve the Massive Access Problem at the Medium Access Contro...

We present a joint forecasting-scheduling (JFS) system, to be implemented at an IoT Gateway, in order to alleviate the Massive Access Problem of the Internet of Things. The existing proposals to solve the Massive Access Problem model the traffic generation pattern of each IoT device via random arrivals. In contrast, our JFS system forecasts the tra...

In this paper, we propose a Trend Predictive Neural Network (TPNN) model, which uses the sensor data and the trend of that data in order to classify the fire situation. We implemented TPNN for data of multi-sensor fire detector with 6 sensors to detect 7 inputs. We test the performance of the TPNN model by using the multi-sensor dataset, which is c...

This 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 implementat...

We present a comparative study of Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP), 1-Dimensional Convolutional Neural Network (1-D CNN), and Long-Short Term Memory (LSTM) models on the problem of forecasting the traffic generation patterns of individual Internet of Things (IoT) devices in Machine-to-Machine (M2M) comm...

Nowadays, most of the fire detection systems are based on smoke detection. Detecting smoke is very simple method to be used, but it is very popular. Although detecting smoke is been using in almost every system, it has weaknesses. As main weakness, sensitivity to pollination is great deal for these systems. Pollination causes false positive alarm,...