no code implementations • 24 November 2022 • SHAHID RAHMAN1, JAMAL UDDIN 1, HABIB ULLAH KHAN 2, HAMEED HUSSAIN3, AYAZ ALI KHAN 4, AND MUHAMMAD ZAKARYA 5, (Senior Member, IEEE)
Embedding different sizes of a particular secret message in a different image (such as Gray, Texture, Aerial and RGB images) came out with about 5. 466 percent of better score.
1 code implementation • IEEE 2021 • MUHAMMAD ZEESHAN1(Senior Member, QAISER RIAZ1(MEMBER, IEEE), MUHAMMAD A. BILAL1, MUHAMMAD K. SHAHZAD1, HAJIRA JABEEN2, SYED ALIHAIDER3, AZIZUR RAHIM1
Since its inception, the Internet of Things (IoT) has witnessed mushroom growth as a breakthrough technology.
no code implementations • IEEE 2020 • Jianyi Liu, Yu Tian, Ru Zhang, (Member, IEEE), YOUQIANG SUN, AND CHAN WANG
And 77. 58% success rate is obtained in the transfer test attack.
no code implementations • IEEE 2020 • SHIWEN ZHANG 1, 2, (Member, TINGTING YAO3, WEI LIANG 4, VOUNDI KOE ARTHUR SANDOR4, AND KUAN-CHING LI 5, (Senior Member, IEEE)
In this article, aiming at a multi-keywords query in LBS, we propose a novel efficient and privacy-preserving multi-keyword query scheme (PPMQ) over the outsourced cloud, which satisfies the requirements of the location and query content privacy protection, query efficiency, the confidentiality of LBS data and scalability regarding the data users.
no code implementations • journal 2020 • YUTING LI AND YUANMING WU, (Member, IEEE)
To defend selective forwarding attacks of malicious nodes, our scheme isolates them from the network.
no code implementations • IEEE Access 2020 • SEO JIN LEE1, PAUL D. YOO 2, A. TAUFIQ ASYHARI 3, YOONCHAN JHI4, LOUNIS CHERMAK 1, (Member, CHAN YEOB YEUN 5, AND KAMAL TAHA 5, (Senior Member, IEEE)
Specifically, the study proposes a lightweight ML-based IDS model namely IMPACT (IMPersonation Attack deteCTion using deep auto-encoder and feature abstraction).
no code implementations • 7 Aug 2018 • ZHAOJUN LU1, WENCHAO LIU1, QIAN WANG 2, GANG QU 2, (Senior Member, IEEE), AND ZHENGLIN LIU 1
A set of experiments is conducted to evaluate BARS in terms of security, validity, and performance, and the results show that BARS is able to establish a trust model with transparency, conditional anonymity, efficiency, and robustness for VANETs.
1 code implementation • journal 2018 • BYRON GRAHAM 1, RAYMOND BOND2, MICHAEL QUINN3, AND MAURICE MULVENNA2, (Senior Member, IEEE)
This paper highlights the potential utility of three common machine learning algorithms in predicting patient admissions.
no code implementations • IEEE 2018 • WEIDONG MIN, (Member, IEEE), HAO CUI, HONG RAO, ZHIXUN LI, AND LEIYUE YAO
Through measuring the changes of these characteristics and judging the relations between the people and furniture nearby, the falls on furniture can be effectively detected.