• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.66-78
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• 2015

With the development of bio computing technology, DNA watermarking to do as a medium of DNA information has been researched in the latest time. However, DNA information is very important in biologic function unlikely multimedia data. Therefore, the reversible DNA watermarking is required for the host DNA information to be perfectively recovered. This paper presents a reversible DNA watermarking using least square based prediction error expansion for noncodng DNA sequence. Our method has three features. The first thing is to encode the character string (A,T,C,G) of nucleotide bases in noncoding region to integer code values by grouping n nucleotide bases. The second thing is to expand the prediction error based on least square (LS) as much as the expandable bits. The last thing is to prevent the false start codon using the comparison searching of adjacent watermarked code values. Experimental results verified that our method has more high embedding capacity than conventional methods and mean prediction method and also makes the prevention of false start codon and the preservation of amino acids.

• Journal of Ecology and Environment
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• v.46 no.2
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• pp.126-135
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• 2022

Background: Pollinators are important ecological elements due to their role in the maintenance of ecosystem health, wild plant reproduction, crop production and food security. The pollinator-plant interaction supports the preservation of plant and animal populations and it also improves the yield in pollination dependent crops. Having knowledge about the plant-pollinator interaction is necessary for development of pesticide risk assessment of pollinators and conservation of endangering species. Results: Traditional methods to discover the relatedness of insects and plants are based on tracing the visiting pollinators by field observations as well as palynology. These methods are time-consuming and needs expert taxonomists to identify different groups of pollinators such as insects or identify flowering plants through palynology. With pace of technology, using molecular methods become popular in identification and classification of organisms. DNA metabarcoding, which is the combination of DNA barcoding and high throughput sequencing, can be applied as an alternative method in identification of mixed origin environmental samples such as pollen loads attached to the body of insects and has been used in DNA-based discovery of plant-pollinator relationship. Conclusions: DNA metabarcoding is practical for plant-pollinator studies, however, lack of reference sequence in online databases, taxonomic resolution, universality of primers are the most crucial limitations. Using multiple molecular markers is preferable due to the limitations of developed universal primers, which improves taxa richness and taxonomic resolution of the studied community.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.85-94
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• 2014

This paper studied the detection technique using file DNA-based behavior pattern analysis in order to minimize damage to user system by malicious programs before signature or security patch is released. The file DNA-based detection technique was applied to defend against zero day attack and to minimize false detection, by remedying weaknesses of the conventional network-based packet detection technique and process-based detection technique. For the file DNA-based detection technique, abnormal behaviors of malware were splitted into network-related behaviors and process-related behaviors. This technique was employed to check and block crucial behaviors of process and network behaviors operating in user system, according to the fixed conditions, to analyze the similarity of behavior patterns of malware, based on the file DNA which process behaviors and network behaviors are mixed, and to deal with it rapidly through hazard warning and cut-off.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.51-62
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• 2015

Of recent interests on high capacity DNA storage, DNA watermarking for DNA copyright protection, and DNA steganography for DNA secret communication are augmented, the reversible DNA watermarking is much needed both to embed the watermark without changing the functionality of organism and to perfectly recover the host DNA sequence. In this paper, we address two ways of DE based reversible DNA watermarking using noncoding DNA sequence. The reversible DNA watermarking should consider the string structure of a DNA sequence, the organism functionality, the perfect recovery, and the high embedding capacity. We convert the string sequence of four characters in noncoding region to the decimal coded values and embed the watermark bit into coded values by two ways; DE based multiple bits embedding (DE-MBE) using pairs of neighbor coded values and consecutive DE-MBE (C-DE-MBE). Two ways process the comparison searching to prevent the false start codon that produces false coding region. Experimental results verified that our ways have more high embedding capacity than conventional methods and produce no false start codon and recover perfectly the host sequence without the reference sequence. Especially C-DE-MBE can embed more high two times than DE-MBE.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.347-363
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• 2019

Malware has its own unique behavior characteristics, like DNA for living things. To respond APT (Advanced Persistent Threat) attacks in advance, it needs to extract behavioral characteristics from malware. To this end, it needs to do classification for each malware based on its behavioral similarity. In this paper, various similarity of Windows malware is estimated; and based on these similarity values, malware's family is predicted. The similarity measures used in this paper are as follows: 'TF-IDF cosine similarity', 'Nilsimsa similarity', 'malware function cosine similarity' and 'Jaccard similarity'. As a result, we find the prediction rate for each similarity measure is widely different. Although, there is no similarity measure which can be applied to malware classification with high accuracy, this result can be helpful to select a similarity measure to classify specific malware family.

• Korean Chemical Engineering Research
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• v.57 no.3
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• pp.321-330
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• 2019

With the rapid development trend in multidisciplinary science and convergence technology, digital data storages have been necessary in order to accumulate a huge amount of information with high security. The possibility that biological DNA code system can offer encoding and decoding information has been illustrated by many researchers. In this review, we summarized current issue of info-convergence nanohybrid system, so-called infohybrid. DNA-inorganic nanohybrid materials and devices to achieve DNA-based molecular information system are presented. The possible applications focusing on tracking-and-traceability management, authenticity verification, and nano-forensics are also reviewed with four steps of encoding, encrypting, decrypting and decoding. We also highlighted the potential of smart code system with Nano-Bio-Info-Cogno (NBIC) convergence technology through the recently published case study of Avatar DNA nanohybrid system with smart phone.

• Review of KIISC
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• v.28 no.5
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• pp.15-19
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• 2018

사물인터넷 기술은 기본적으로 다양한 대규모의 IoT 디바이스가 인터넷에 연결되어 디바이스로부터 수집되는 데이터을 통해 스마트한 서비슬 제공하는 기술이다. 그러나, 이런한 환경은 역으로 다양한 사양의 다비이스가 네트워크에 연결되기 때문에 가장 취약한 지점으로 통해 IoT 네트워크를 공격할 수 있게 된다. 따라서, 저사양 IoT 디바이스일지라도 전제 연결 네트워크의 보안 수준에 걸맞는 보안강도를 보장해야 하는 다소 역설적인 상황에 봉착하게 된다. 이러한 상황을 해결하기 위해 저사양 IoT 디바이스의 보안을 강화할 수 있는 기술이 시급하다고 할 수 있다. 본 논문에서는 이를 위해 PUF 개념을 일반화하여 디바이스 DNA라는 새로운 개념을 정의하고 기본적인 디바이스 DNA의 성질을 바이오인식 기술에서 차용하여 설명하고자 한다.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.131-134
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• 2002

As the increased use of computer, wired/wireless/mobile Internet, security in using Internet becomes a more important problem. Thus, biometric technology using physical and behavior characteristics of a person is hot issue. Many different types of biometric technologies of a person such as fingerprint, face, iris, vein, DNA, brain wave, palm, voice, dynamic signature, etc. had already been studied but remained unsuccessful because they do not meet social demands. However, recently many of these technologies have been actively revived and researchers have developed new products on various commercial fields. Dynamic signature verification technology is to verify the signer by calculating his writing manner, speed, angle, and the number of strokes, order, the down/up/movement of pen when the signer input his signature with an electronic pen for his authentication. Then signature verification system collects mentioned above various feature information and compares it with the original one and simultaneously analyzes to decide whether signature is forgery or true. The prospect of signature verification technology is very promising and its use will be wide spread in terms of economy, security, practicality, stability and convenience.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.47-54
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• 2023

This article reviews recent advances and perspectives in neuroscience related to consciousness, cognition, and neural networks in the brain. The neural mechanisms underlying cognitive processes, such as perception, attention, memory, and decision-making, are explored. The article also examines how these processes give rise to our experience of consciousness. The implications of these findings for our understanding of the brain and its functions are presented, as well as potential applications of this knowledge in fields such as medicine, psychology, and artificial intelligence. Additionally, the article explores the concept of a quantum viewpoint concerning consciousness, cognition, and creativity and how incorporating DNA as a key element could reconcile classical and quantum perspectives on human behaviour, consciousness, and cognition, as explained by genomic psychological theory. Furthermore, the article explains how the human brain processes external stimuli through the sensory nervous system and how it can be simulated using an artificial neural network (ANN) consisting of one input layer, multiple hidden layers, and an output layer. The law of learning is also discussed, explaining how ANNs work and how the modification of weight values affects the output and input values. The article concludes with a discussion of future research directions in this field, highlighting the potential for further discoveries and advancements in our understanding of the brain and its functions.