Pusat IPv6 Termaju Negara - Tesis

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Pembangunan Pelantar Penghantaran Laluan Isyarat Berasaskan Sel Stem Mesenkima Untuk Terapi Bersasar Tumor Her2-Positif
(2024-11)
Sun, Yuliang
Her2 (+) tumours have the characteristics of strong migration, poor sensitivity to chemotherapy drugs, poor recovery, easy recurrence, and high malignancy. However, traditional methods of targeted treatment are often hindered by biological barriers, which isolate drug molecules in healthy tissues and cause damage to normal cells. Here, the study designed an intelligent delivery platform by introducing artificial signal pathways to mesenchymal stem cells (msc). This intelligent delivery platform is different from traditional targeting of tumour microenvironment or enhancing affinity with tumours, endowing the delivery platform with tumour signal recognition ability, enabling it to autonomously distinguish between tumours and normal tissue cells, and feedback editing instructions. Specifically, the artificial signalling pathway of msc is divided into two parts: recognition structure and response structure, which respectively play a role in identifying tumours and responding to tumour signals. To enable msc to efficiently release drugs, this study endowed msc with affibody-notch (core)-vp64-gal4/uas-hsv-tk artificial signalling pathway and constructed an intelligent delivery platform for controllable drug release (mscintelligent).
Secured Communication Mechanism For Message Authentication And Data Confidentiality In Secs/Gem Protocol
(2024-03)
Jaisan S. M., Ashish
This study proposes SECS/GEM-AE, a mechanism for securing SECS/GEM data messages using AES-GCM encryption and evaluates its performance against the standard SECS/GEM protocol and SECS/GEMsec, a security mechanism proposed to authenticate SECS/GEM messages.
Passive Rule-Based Approach To Detect Sinkhole Attack In 6lowpan Rpl Based Internet Of Things Networks
(2024-02)
Subh, Al Sarawi Shadi Mustafa
The aim of this thesis is to propose a Passive Rule-Based Approach named PRBA to detect sinkhole attacks in 6LoWPAN RPL-based IoT Networks, which consists of four stages to achieve four research objectives, which are: (1) Data collection and preprocessing stage that fulfills the objective to transform the collected power consumption values and the captured ICMPv6 network traffic into a meaningful format; (2) Feature Selection stage that fulfills the objective to decrease the size of the features by selecting the most significant features that contribute to detecting sinkhole attacks; (3) Behavioral Indicators stage that fulfills the objective to identify abnormal behavior of sinkhole attacks using the features of the ICMPv6 and power consumption from the previous stage; and (4) Sinkhole Attack Detection stage that fulfills the objective to decide whether there is a sinkhole attack in two steps.
Approach For Optimizing Course Recommendation Based On Integrating Modified Felder-Silverman Learning Style Model With Meta-Heuristics Algorithm
(2023-12)
Ashraf, Erum
E-learning's popularity surges due to technology, flooding Massive Open Online Course (MOOC) platforms with courses, causing information overload. Recommender systems filter courses but struggle with learning styles due to lack of standardized datasets and measurement approaches, hindering data collection in resource-constrained educational institutions. This research streamlines course selection by matching it with learners' styles. It involves identifying potential courses learning style, validated through genetic and surrogate meta-heuristics optimization algorithms, and employing Felder-Silverman model for learning style identification. The proposed scheme supported the personalized course recommendations to students suitable with student learning style.
Enhanced Steganography Framework Based On Lossless Compression And Histogram
(2024-07)
Mohammad Kasasbeh, Dima Suliman
Steganography is an effective cybersecurity technique that facilitates covert communication by hiding information's existence within a spoof image. The trade-off between embedding capacity, imperceptibility, and reversibility has presented a new challenge in steganography. Balancing these factors is crucial and essential for the development of effective steganography. This thesis proposes an enhanced steganography framework to increase embedding capacity, maintain imperceptibility, and achieve reversibility. Three models were proposed: compression, embedding map generation, and reversible steganography. The compression model was modified to include a hybrid lossless text compression algorithm to reduce redundant secret information and boost embedding capacity. The Embedding Map Generation Model proposed two techniques to determine the best RGB channels and generate the embedding location map before the embedding process. In the reversible steganography model, a 3D embedding process is proposed. It hides compressed bitstreams in the 2D-PEH with a lower local complexity value. Additionally, it hides decompression keys along with other extraction parameters using the generated embedding location map.

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