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RISS 인기검색어
- 검색키워드
- 저자 : Tamer AbuHmed (검색결과 6 건)
- 무료
- 기관 내 무료
- 유료
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UOIT Keyboard: A Constructive Keyboard for Small Touchscreen Devices
AbuHmed, Tamer,Kyunghee Lee,DaeHun Nyang IEEE 2015 IEEE transactions on human-machine systems Vol.45 No.6
<P>Many techniques have been proposed for reducing errors during text input on touchscreens. However, the majority of these techniques suffer from the same limitation, i.e., the keyboard keys are overcrowded on a small screen, resulting in high error rates and slow text inputs. To address this situation and resolve the problems associated with overcrowdedness, we introduce a new text-entry method called the “UOIT keyboard.” The idea behind the UOIT keyboard is to compose letters using “drawing-like typing” on the UOIT keyboard, which has 13 large keys that replace the 26 small keys that exist in the QWERTY keyboard. We describe the design, keys, and mechanism of the UOIT keyboard. A 24-participant user study was conducted to evaluate the speed and accuracy of the proposed entry method as compared with the QWERTY and multitap entry methods. As part of the evaluation, a questionnaire was used to collect participants' preferences. The UOIT keyboard has a mean entry speed of 11.3 words/min. The UOIT keyboard significantly reduces the typing errors with 3.8% total error rate comparing with 11.2% and 16.3% for QWERTY and multitap entry methods, respectively.</P>
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Deep Packet Inspection for Intrusion Detection Systems: A Survey
AbuHmed, Tamer,Mohaisen, Abedelaziz,Nyang, Dae-Hun The Korea Institute of Information and Commucation 2007 정보와 통신 Vol.24 No.11
Deep packet inspection is widely recognized as a powerful way which is used for intrusion detection systems for inspecting, deterring and deflecting malicious attacks over the network. Fundamentally, almost intrusion detection systems have the ability to search through packets and identify contents that match with known attach. In this paper we survey the deep packet inspection implementations techniques, research challenges and algorithm. Finally, we provide a comparison between the different applied system.
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Deep Packet Inspection for Intrusion Detection Systems: A Survey
Tamer AbuHmed,Abedelaziz Mohaisen,DaeHun Nyang 한국통신학회 2007 정보와 통신 Vol.24 No.11
Deep packet inspection is widely recognized as a powerful way which is used for intrusion detection systems for inspecting, deterring and deflecting malicious attacks over the network. Fundamentally, almost intrusion detection systems have the ability to search through packets and identify contents that match with known attacks. In this paper, we survey the deep packet inspection implementations techniques, research challenges and algorithms. Finally, we provide a comparison between the diflerent applied systems.
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CFG, 라이브러리 정보를 이용한 권한 기반 안드로이드 악성코드 탐지 기술의 성능 향상
박건우,Tamer AbuHmed,민대홍,양대헌,이경희 한국차세대컴퓨팅학회 2019 한국차세대컴퓨팅학회 논문지 Vol.15 No.6
In this paper, we propose a static Android malware detection technique to improve the detection performance and reliability. Based on the permission feature which is heavily used in the previous works, we additionally use the library dependency and control flow graph (CFG) as features for improving our detection performance. Library dependency and CFG-based features are efficient to detect Android malware, which is obfuscated using renaming technique because these are extracted by structural analysis. By combining these three features, we propose a novel malware detection model using bidirectional long short-term memory. As results, we achieved 99.62% overall detection rate. Our model is highly reliable: where the precision, recall and F1 scores are 100%, 99.26% and 99.62%, respectively. 본 논문에서는 안드로이드 악성코드의 증가 추세에 대응하여 향상된 성능의 정적 악성코드 탐지 기법을 고안하였다. 기존의 어플리케이션의 권한을 특징(feature)으로 사용하는 악성코드 탐지 기법에 라이브러리 사용 정보와 control flow graph (CFG)의 속성을 특징으로 추가하여 성능을 향상시켰다. 또한, 라이브러리와 CFG는 구조 분석을 통해 특징을 추출하므로 리네이밍(renaming) 난독화에 대하여 독립적이라는 특징이 있어 난독화에 취약한 권한 사용 탐지 기법을 보완하는 추가적인 이점을 가진다. 어플리케이션으로부터 추출한 세 가지 특징을 기반으로 양방향 장단기 기억 네트워크(bidirectional long short-term memory)를 이용한 악성코드 탐지 모델을 제안하였다. 세 가지 특징을 모두 사용한 악성코드 분류 모델을 안드로이드 악성코드와 일반 어플리케이션을 합친 데이터에 적용하였을 때 정확도 99.62%, 정밀도 100%, 재현율 99.26%, F1 99.62%로 높은 성능과 신뢰도를 보였다.
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Two-level Key Pool Design-based Random Key Pre-distribution in Wireless Sensor Networks
( Abedelaziz Mohaisen ),( Daehun Nyang ),( Tamer Abuhmed ) 한국인터넷정보학회 2008 KSII Transactions on Internet and Information Syst Vol.2 No.5
In this paper, the random key pre-distribution scheme introduced in ACM CCS`02 by Eschenauer and Gligor is reexamined, and a generalized form of key establishment is introduced. As the communication overhead is one of the most critical constraints of any successful protocol design, we introduce an alternative scheme in which the connectivity is maintained at the same level as in the original work, while the communication overhead is reduced by about 40% of the original overhead, for various carefully chosen parameters. The main modification relies on the use of a two-level key pool design and two round assignment/key establishment phases. Further analysis demonstrates the efficiency of our modification.
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Code authorship identification using convolutional neural networks
Abuhamad, Mohammed,Rhim, Ji-su,AbuHmed, Tamer,Ullah, Sana,Kang, Sanggil,Nyang, DaeHun Elsevier 2019 Future generation computer systems Vol.95 No.-
<P><B>Abstract</B></P> <P>Although source code authorship identification creates a privacy threat for many open source contributors, it is an important topic for the forensics field and enables many successful forensic applications, including ghostwriting detection, copyright dispute settlements, and other code analysis applications. This work proposes a convolutional neural network (CNN) based code authorship identification system. Our proposed system exploits term frequency-inverse document frequency, word embedding modeling, and feature learning techniques for code representation. This representation is then fed into a CNN-based code authorship identification model to identify the code’s author. Evaluation results from using our approach on data from Google Code Jam demonstrate an identification accuracy of up to 99.4% with 150 candidate programmers, and 96.2% with 1,600 programmers. The evaluation of our approach also shows high accuracy for programmers identification over real-world code samples from 1987 public repositories on GitHub with 95% accuracy for 745 C programmers and 97% for the C++ programmers. These results indicate that the proposed approaches are not language-specific techniques and can identify programmers of different programming languages.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We proposed three CNN-based code authorship identification systems. </LI> <LI> We explained various source code representations and our feature learning technique. </LI> <LI> We then fed the code representations into a CNN-based code authorship model. </LI> <LI> Large-scale code authorship process of different programming languages is conducted. </LI> <LI> Our technique identified a large number of programmers (1,600) with 99.5% accuracy. </LI> </UL> </P>
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