M. Ali Aydin

A hybrid intrusion detection system design for computer network security

Intrusions detection systems (IDSs) are systems that try to detect attacks as they occur or after the attacks took place. IDSs collect network traffic information from some point on the network or computer system and then use this information to secure the network. Intrusion detection systems can be misuse-detection or anomaly detection based. Misuse-detection based IDSs can only detect known attacks whereas anomaly detection based IDSs can also detect new attacks by using heuristic methods. In this paper we propose a hybrid IDS by combining the two approaches in one system. The hybrid IDS is obtained by combining packet header anomaly detection (PHAD) and network traffic anomaly detection (NETAD) which are anomaly-based IDSs with the misuse-based IDS Snort which is an open-source project. The hybrid IDS obtained is evaluated using the MIT Lincoln Laboratories network traffic data (IDEVAL) as a testbed. Evaluation compares the number of attacks detected by misuse-based IDS on its own, with the hybrid IDS obtained combining anomaly-based and misuse-based IDSs and shows that the hybrid IDS is a more powerful system.

Half cycling dynamic bandwidth allocation with prediction on EPON

This study is about a prediction approach for our previous dynamic bandwidth allocation algorithm for Ethernet Passive Optical Networks (EPON). Our previous work (hcDBA) is based on half cycle timing for bandwidth allocation. That can be handled as a middle way between online and offline bandwidth allocation schemes. In PONs, prediction is used for bandwidth allocation to grant loaded nodes with early responses. However, due to the versatile nature of data traffic, prediction algorithms have a handicap to provide a better solution in classical approaches. In this study, a novel prediction approach integrated with hcDBA algorithm described. Performance comparison of hcDBA with & without prediction and IPACT algorithm is given. According to the simulation results, prediction on hcDBA seems to give some performance improvements in terms of access-delay.

Traffic characterization study on EPON upstream channel

Metropolitan ring networks are usually used to connect the high speed backbone networks with access networks. Until now, the metropolitan network and the access network are gained much attention of researchers. However they have been investigated in separate ways. There is no work in which the end-to-end performance from end-users of the access network to metropolitan network is evaluated. How to simulate a complete end-to-end network while keeping basic characteristics of access and metro traffic is an emergence problem to evaluate the end-to-end performance. In reality, a complete end-to-end network in which hundreds of Ethernet Passive Optical Network (EPON) are connected to metro ring networks cannot be simulated because of the huge amount of traffic generated from the access network side. In this paper, we aim to obtain trace files of incoming traffic at the entrance of Optical Line Terminal (OLT) by running simulations of an EPON network which implements Interleaved Polling with Adaptive Cycle Time (IPACT). Then, the generated traffic pattern will be characterized in order to find corresponding traffic model among well known traffic models and this model will be used as output traffic of OLTs. After, we can use this traffic model without simulating a complete EPON network. Through various simulations, we observe that the generated traffic that comes to OLT is similar to the traffic obtained with Poisson sources

An Approach for Evaluating Performance of Magnetic-Field Based Indoor Positioning Systems: Neural Network

Indoor Positioning Systems are more and more attractive research area and popular studies. They provide direct access of instant location information of people in large, complex locations such as airports, museums, hospitals, etc. Especially for elders and children, location information can be lifesaving in such complex places. Thanks to the smart technology that can be worn, daily accessories such as wristbands, smart clocks are suitable for this job. In this study, the earth’s magnetic field data is used to find location of devices. Having less noise rather than other type of data, magnetic field data provides high success. In this study, with this data, a positioning model is constructed by using Artificial Neural Network (ANN). Support Vector Machines(SVM) was used to compare the results of the model with the ANN. Also the accuracy of this model is calculated and how the number of hidden layer of neural network affects the accuracy is analyzed. Results show that magnetic field indoor positioning system accuracy can reach 95% with ANN.

A Comparative Analysis of N-Nearest Neighbors (N3) and Binned Nearest Neighbors (BNN) Algorithms for Indoor Localization

In this study, performances of classification algorithms N-Nearest Neighbors (N3) and Binned Nearest Neighbor (BNN) are analyzed in terms of indoor localizations. Fingerprint method which is based on Received Signal Strength Indication (RSSI) is taken into consideration. RSSI is a measurement of the power present in a received radio signal from transmitter. In this method, the RSSI information is captured at the reference points and recorded for creating a signal map. The obtained signal map is knows as fingerprint signal map and in the second stage of algorithm is creating a positioning model to detect individual’s position with the help of fingerprint signal map. In this work; N-Nearest Neighbors (N3) and Binned Nearest Neighbors (BNN) algorithms are used to create an indoor positioning model. For this purpose; two different signal maps are used to test the algorithms. UJIIndoorLoc includes multi-building and multi floor signal information while different from this RFKON includes a single-building single floor signal information. N-Nearest Neighbors (N3) and Binned Nearest Neighbors (BNN) algorithms are presented comparatively with respect to success of finding user position.

Security Attacks on IoT

This study includes what the concept of IoT (Internet of Objects) expresses conceptually. It is stated that there are three generally accepted layers even though IoT does not have a complete layer structure. These layers are; Object layer, Network layer and Application layer. In addition, the most common security epidemics at IOT are; Botnet, Man in the Middle Attacks, Social Engineering, Data and Identity Defeats and Denial of Service attacks are expressed by examples and analyzes. Finally, these attacks describe how to take precautions in the layers of IOT.