Ahmet Turan Özcerit

Jamming detection mechanisms for wireless sensor networks

The Jamming-style Denial of Service (J-DoS) attacks are significant causes of malfunctioning of Wireless Sensor Networks (WSNs). The nodes of WSNs are prone to external disturbances especially when they are used in hostile environments. The attackers mainly operate in the wireless communication medium by following a couple of diverse scenarios. In this paper, we have developed two detection mechanisms used for J-DoS attacks in order to differentiate the legitimate and adversary scenarios. The detection mechanisms designed utilize some network parameters and additional packets to separate and classify normal conditions and adversary ones. Having detected the type of the attacker, appropriate counter measures can be applied.

A new feature encoding scheme for HIV-1 protease cleavage site prediction

HIV-1 protease has been the subject of intense research for deciphering HIV-1 virus replication process for decades. Knowledge of the substrate specificity of HIV-1 protease will enlighten the way of development of HIV-1 protease inhibitors. In the prediction of HIV-1 protease cleavage site techniques, various feature encoding techniques and machine learning algorithms have been used frequently. In this paper, a new feature amino acid encoding scheme is proposed to predict HIV-1 protease cleavage sites. In the proposed method, we combined orthonormal encoding and Taylor’s venn-diagram. We used linear support vector machines as the classifier in the tests. We also analyzed our technique by comparing some feature encoding techniques. The tests are carried out on PR-1625 and PR-3261 datasets. Experimental results show that our amino acid encoding technique leads to better classification performance than other encoding techniques on a standalone classifier.

Prediction of MHC class I binding peptides with a new feature encoding technique

The recognition of specific peptides, bound to major histocompatibility complex (MHC) class I molecules, is of particular importance to the robust identification of T-cell epitopes and thus the successful design of protein-based vaccines. Here, we present a new feature amino acid encoding technique termed OEDICHO to predict MHC class I/peptide complexes. In the proposed method, we have combined orthonormal encoding (OE) and the binary representation of selected 10 best physicochemical properties of amino acids derived from Amino Acid Index Database (AAindex). We also have compared our method to current feature encoding techniques. The tests have been carried out on comparatively large Human Leukocyte Antigen (HLA)-A and HLA-B allele peptide binding datasets. Empirical results show that our amino acid encoding scheme leads to better classification performance on a standalone classifier.

OETMAP: a new feature encoding scheme for MHC class I binding prediction

Deciphering the understanding of T cell epitopes is critical for vaccine development. As recognition of specific peptides bound to Major histocompatibility complex (MHC) class I molecules, cytotoxic T cells are activated. This is the major step to initiate of immune system response. Knowledge of the MHC specificity will enlighten the way of diagnosis, treatment of pathogens as well as peptide vaccine development. So far, a number of methods have been developed to predict MHC/peptide binding. In this article, a novel feature amino acid encoding scheme is proposed to predict MHC/peptide complexes. In the proposed method, we have combined orthonormal encoding (OE) and Taylor’s Venn-diagram, and have used Linear support vector machines as the classifier in the tests. We also have compared our method to current feature encoding scheme techniques. The tests have been carried out on comparatively large Human leukocyte antigen (HLA)-A and HLA-B allele peptide three binding datasets extracted from the Immune epitope database and analysis resource. On three datasets experimented, the IC50 cutoff a criteria is used to select the binders and non-binders peptides. Experimental results show that our amino acid encoding scheme leads to better classification performance than other amino acid encoding schemes on a standalone classifier.

A Novel Chaotic System for Secure Communication Applications

The secure communication using synchronization between identical chaotic systems have been introduced in literature for a long time. A well-known practical application of chaotic synchronized systems is the Pecora and Carroll (P-C) secure communication method. In this paper, the P-C secure communication algorithm is applied to a novel three dimensional, autonomous chaotic attractor. Having a 45 ○ slope between sub-driver and sub-receiver circuits of a novel chaotic attractor clearly demonstrates that it can be used for the purpose of secure communications. DOI: http://dx.doi.org/10.5755/j01.itc.44.3.7720

Design and implementation of chaos based true random number generator on FPGA

Currently, chaotic signal generators is of importance in cryptographic applications and chaotic communication systems. One of the significant field of the chaotic signal oscillators are random number generators. In this paper, an FPGA-based new true random number generator system using discrete-time chaotic signal generator is presented. The system designed incorporates the Sprott 94 G chaotic system based on an FPGA deployed with IEEE 754 standard. In order to produce random bits a quantification process has been performed on the results produced by the chaotic oscillator unit. Furthermore, the XOR method has been determined as restoring function to obtain a true random bit generator. The maximum operating frequency of FPGA-based true random number generator has been able to reach up to 399,383 MHz. The 20,000-bit sequence has been generated by the designed system and they have been saved to the test result file. They have been tested using NIST test suite and FIPS-140-1 standards and successful results have been obtained. It is concluded that the FPGA-based system is able to be used in cryptologic applications.

The design and implementation of remote personel monitoring system in military zones

Nowadays, despite there are many advanced technological applications to maintain the security of buildings, premises or regions, human intervention is necessary when considering both economical facts and the effectiveness of the systems especially for the security of military zones. In the presented work, a highly secure system has been designed. With the system to be implemented, the location, postures (lying, standing, sitting, and etc.), health conditions (body temperature, heart rate, and etc.), and environment conditions of the guard officers can be monitored remotely so that the key personal can be warned to take required steps in emergency at once. The system consists of three basic devices. First, an originally designed portable Wireless Sensor Node (WSN), which detects the desired parameters and transfers them to a Wireless Central Unit (WCU) by a secure radio frequency communication, is placed on the guards. The WCU collects all information to the center coming from wireless sensor nodes. Finally, a Central Monitoring Unit (CMU), in which all information from the WCU is monitored visually and evaluated, has been designed and implemented.

24 bit renkli hareketli resimler (video) üzerinde geliştirilen sırörtme yöntemi

Techniques for information hiding ( Steganography) have nowadays become increasingly more sophisticated and widespread. Communication channels have insecure. So problems of security have become. Kriptography and Steganography have devised a technology for needs of data security. Kriptography has curious about data security, Steganography has curious about data security of communication. In this study. the encrypted message that is on .AVI format very different from the classical LSB data embedding technique based data embedding algorithm is mainly used as RGB. This coding technique has increased considerably as the relative capacity of data embedding.

A performance comparision about hiding methods for steganalysed audio files through chi-square and artificial neural networks

In this study, there has been carried a performance comparison on random and sequential hidden data of audio wav files by using the chi-square test and PNN with steganography technique that allows resolution of package. During performance of applied steganography method, it is assumed that hiding algorithm is known. It has been developed an analysis method for hidden objects created by using LSB steganography technique. LSB data hiding method that the hidden data embedded into last bits, has been strengthened by holding a performance comparison analysis both as random and sequential type about steganography algorithms. Lastly results has been optimized as real as possible by training of PNN neural network.

Audio file steganalysis through artificial neural networks

In this study, we have developed a new steganalysis method that combines both Chi-square and PNN techniques. We have tested our steganalysis method on digital audio files. The stego files under test have been assumed to contain a secret data file embedded by a known steganography method, which is LSB (Least Significant Bit) steganography in our test environment. The method we have developed first analyzes the LSB bits in cover audio files then obtained intermediate data are applied to PNN to achieve more realistic results. While most steganalysis methods focuses on only detection of secret data, our method can also determine the rate of secret data.