Browsing by Author "Tarlan, Ozan"

Now showing 1 - 3 of 3

Metadata only
DiBLIoT: A distributed blacklisting protocol for iot device classification using the hashgraph consensus algorithm
(IEEE Computer Society, 2022) Tarlan, Ozan; Şafak, I.; Çakmakçı, Kübra Kalkan; Computer Science; ÇAKMAKCİ, Kübra Kalkan; Tarlan, Ozan
Industrial applications require highly reliable, secure, low-power and low-delay communications. However, wireless communication links in the industrial environment suffer from various channel impairments which can compromise the above requirements. This paper presents a new reliable blacklisting protocol for ensuring the Internet of Things (IoT) network security and mitigating the effects of interference caused by multipath Rayleigh fading using a distributed approach. The proposed blacklisting protocol is simulated over a distributed IoT network setup where flat Rayleigh fading disrupts Message Queuing Telemetry Transport (MQTT) communications. Distributed servers jointly decide in real-Time whether to blacklist a device after individually performing anomaly detection and submitting their results to the hashgraph network. The IoT devices are classified by a device fingerprinting method using various machine learning (ML) algorithms that are trained with real-Time packet capture data. The proposed blacklisting protocol is shown to increase the accuracy of blacklisting malignant devices from 42% to 82% as the number of servers increases from one to five for mixed attacks. It also achieves higher accuracies ranging between 47.2%-97.6% versus 47.4%-90.7% compared to the related work for Denial of Service (DoS) attacks. The proposed protocol is particularly suitable for the Industrial IoT (IIoT) in mitigating the effects of harsh communication environments in manufacturing facilities.
Metadata only
A distributed blacklisting protocol for iot device classification using the hashgraph consensus algorithm
Tarlan, Ozan; Çakmakçı, Kübra Kalkan; Çakmakçı, Kübra Kalkan; Sözer, Hasan; Alagöz, F.; Department of Computer Science; Tarlan, Ozan
Industrial applications require highly reliable, secure, low-power and low-delay communications. However, wireless communication links in the industrial environment suffer from various channel impairments which can compromise these requirements. This thesis presents a new reliable blacklisting protocol for ensuring the Internet of Things (IoT) network security and mitigating the effects of interference caused by multipath Rayleigh fading using a distributed approach. The proposed blacklisting protocol is simulated over a distributed IoT network setup where flat Rayleigh fading disrupts Message Queuing Telemetry Transport (MQTT) communications. Distributed servers jointly decide in real-time whether to blacklist a device after individually performing anomaly detection and submitting their results to the hashgraph network. The IoT devices are classified by a device fingerprinting method using various machine learning (ML) algorithms that are trained with real-time packet capture data. The proposed blacklisting protocol is shown to increase the accuracy of blacklisting malignant devices from 42% to 82% as the number of servers increases from one to five for mixed attacks. It also achieves higher accuracies ranging between 47.2%-97.6% versus 47.4%-90.7% compared to the related work for Denial of Service (DoS) attacks. For this simulation with multiple servers, a novel solution for reducing the PER in a Rayleigh fading environment by searching optimal distributed server locations with a hybrid approach is also provided. The proposed protocol and the server location optimization algorithm are particularly suitable for the Industrial IoT (IIoT) in mitigating the effects of harsh communication environments in manufacturing facilities.
Metadata only
Towards test automation for certification tests in the banking domain
(IEEE, 2023) Elakas, A.; Tarlan, Ozan; Safak, I.; Çakmakçı, Kübra Kalkan; Sözer, Hasan; Computer Science; SÖZER, Hasan; ÇAKMAKCİ, Kübra Kalkan; Tarlan, Ozan
Software systems in the banking domain are business-critical applications that provide financial services. These systems are subject to rigorous certification tests, which are performed manually, and take weeks to complete. In this paper, we suggest that automation of the certificate tests are possible and it will save a considerable amount of time. A certification testing operation which can take a few weeks can be reduced to a few seconds. Firstly, we review the existing test activities to identify the ones that can be automated and introduce a prototype tool for automating some of the tests used for certification. We focus on rules that are verified by analyzing the banking infrastructure. Our tool takes the network topology of the banking infrastructure as input and verifies a subset of these rules. The tool can be extended with additional rules in order to reduce the effort for certification tests. In addition to this tool, we introduce softwaredefined network-based tests to automatically verify compliance with the rules by checking the firewall constraints and host connections. In particular, we focus on a security certification standard named Payment Card Industry Data Security Standard. This certification aims to reduce the risk of data breaches in cardholder data by ensuring industry standard practices for payment card transactions. Our tool offers effort reduction in auditing through automation. It supports continuous auditing and network security enhancement processes.