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RISS 인기검색어
- 검색키워드
- 저자 : ( Tohari Ahmad ) (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
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Increasing the Performance of Difference Expansion-based Steganography when Securing Medical Data
Tohari Ahmad,Muhammad Holil 한국산학기술학회 2014 SmartCR Vol.4 No.4
Steganograpy has been widely used for hiding secret data in various types of other data, such as images, audio, or video. In order to obtain this secret data, an extraction algorithm is used according to the respective steganographic embedding algorithm. Nevertheless, many data hiding methods have three common problems: reversibility or obtaining both secret and cover data in the extraction process, similarity between cover data and stego data, and the size of the secret data that can be hidden. In this paper, we focus on the last two problems while still maintaining reversibility (the first problem) by proposing four methods which are developed based on Difference Expansion. In the first three methods, we use a threshold to control the stego image quality, while in the fourth we use the smoothness level instead of a threshold value. In these proposed methods, we use text and images as the secret and cover data, respectively, where the secret data is embedded into the difference of pixel blocks. The size of these blocks varies. In addition, the difference is reduced before the secret data is embedded. The experimental results show that the secret data as well as the cover data can be reconstructed to be exactly the same as the original. Furthermore, both the capacity of the secret data and the similarity level increase for certain block sizes.
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Increasing Accuracy of Process-based Fraud Detection Using a Behavior Model
Solichul Huda,Riyanarto Sarno,Tohari Ahmad 보안공학연구지원센터 2016 International Journal of Software Engineering and Vol.10 No.5
Process-based fraud (PBF) is fraud caused by deviation from a business process model. Some studies have proposed methods for PBF detection; however, these are still not able to fully detect the occurrence of fraud. In this context, we propose a new method of PBF detection which carries out the behavior of the originators (users who perform events) to adjust the levels of fraud occured in the events. In this research, we propose a method of PBF detection with behavior model in order to increase accuracy. This is done firstly by analyzing the business processes that correspond to those in the standard operating system (SOP). Secondly, by calculating the event execution performed by the originator and his/her relations within the organization, whose behavior is then analyzed. Thirdly, by using the number of deviations and the originator behavior to calculate the attribute value. By using attribute importance weights, an attribute rating of each originator is kept. Finally, Multi Attribute Decision Making is used to decide the PBF rating of a case, on the basis of which it is decided whether fraud occurred or not. The experimental results show that this behavior model is able to reduce false positive and false negative, therefore, the method can increase the accuracy level by 0.03.
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B-Corr Model for Bot Group Activity Detection Based on Network Flows Traffic Analysis
( Dandy Pramana Hostiadi ),( Waskitho Wibisono ),( Tohari Ahmad ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.10
Botnet is a type of dangerous malware. Botnet attack with a collection of bots attacking a similar target and activity pattern is called bot group activities. The detection of bot group activities using intrusion detection models can only detect single bot activities but cannot detect bots' behavioral relation on bot group attack. Detection of bot group activities could help network administrators isolate an activity or access a bot group attacks and determine the relations between bots that can measure the correlation. This paper proposed a new model to measure the similarity between bot activities using the intersections-probability concept to define bot group activities called as B-Corr Model. The B-Corr model consisted of several stages, such as extraction feature from bot activity flows, measurement of intersections between bots, and similarity value production. B-Corr model categorizes similar bots with a similar target to specify bot group activities. To achieve a more comprehensive view, the B-Corr model visualizes the similarity values between bots in the form of a similar bot graph. Furthermore, extensive experiments have been conducted using real botnet datasets with high detection accuracy in various scenarios.
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Audio Data Hiding Based on Sample Value Modification Using Modulus Function
( Mohammed Hatem Ali Al-hooti ),( Supeno Djanali ),( Tohari Ahmad ) 한국정보처리학회 2016 Journal of information processing systems Vol.12 No.3
Data hiding is a wide field that is helpful to secure network communications. It is common that many data hiding researchers consider improving and increasing many aspects such as capacity, stego file quality, or robustness. In this paper, we use an audio file as a cover and propose a reversible steganographic method that is modifying the sample values using modulus function in order to make the reminder of that particular value to be same as the secret bit that is needed to be embedded. In addition, we use a location map that locates these modified sample values. This is because in reversible data hiding it needs to exactly recover both the secret message and the original audio file from that stego file. The experimental results show that, this method (measured by correlation algorithm) is able to retrieve exactly the same secret message and audio file. Moreover, it has made a significant improvement in terms of the following: the capacity since each sample value is carrying a secret bit. The quality measured by peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), Pearson correlation coefficient (PCC), and Similarity Index Modulation (SIM). All of them have proven that the quality of the stego audio is relatively high.
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Audio Data Hiding Based on Sample Value Modification Using Modulus Function
Al-Hooti, Mohammed Hatem Ali,Djanali, Supeno,Ahmad, Tohari Korea Information Processing Society 2016 Journal of information processing systems Vol.12 No.3
Data hiding is a wide field that is helpful to secure network communications. It is common that many data hiding researchers consider improving and increasing many aspects such as capacity, stego file quality, or robustness. In this paper, we use an audio file as a cover and propose a reversible steganographic method that is modifying the sample values using modulus function in order to make the reminder of that particular value to be same as the secret bit that is needed to be embedded. In addition, we use a location map that locates these modified sample values. This is because in reversible data hiding it needs to exactly recover both the secret message and the original audio file from that stego file. The experimental results show that, this method (measured by correlation algorithm) is able to retrieve exactly the same secret message and audio file. Moreover, it has made a significant improvement in terms of the following: the capacity since each sample value is carrying a secret bit. The quality measured by peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), Pearson correlation coefficient (PCC), and Similarity Index Modulation (SIM). All of them have proven that the quality of the stego audio is relatively high.
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