Pusat IPv6 Termaju Negara - Tesis

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An Enhanced Mechanism To Detect Drdos Attacks On Dns Using Adaptive Thresholding Technique
(2023-03)
Al Ogaili, Riyadh Rahef Nuiaa
Demand for cyberspace-enabled services has expanded dramatically in recent years, in lockstep with the global Internet user population expansion. This rising demand for these services has increased the number of cyber threats launched by attackers, as well as the diversity and sophistication of the attack strategies used to target those services. By exploiting DNS flaws, cyber attackers conduct a Distributed Reflection Denial of Service (DRDoS) attack. As a result, these types of attacks exploit the method, functionality, and operation of open DNS resolvers to compromise the DNS. Additionally, to intensify the attack by boosting the attack bandwidth to overwhelm the victim with a vast number of DNS answers. As a result, traditional mechanisms are incapable of detecting these types of cyberattacks. As a result, existing detection mechanisms are unable to detect these forms of cyber intrusions. Thus, this thesis presents a mechanism for detecting DRDoS attacks on DNS that is strengthened by the use of modified metaheuristic algorithms and adaptive thresholding techniques based on machine learning algorithms (EMDDMAT).
Optimizing The Production Of Short-Peptide Tagged Ss3A Recombinant Protein As A Potential Serological Biomarker For Strongyloidiasis
(2023-02)
Hassan, Nur Hassanah Mohd
Strongyloides stercoralis is a human-pathogenic nematode with a unique ability to autoinfect causing a parasitic disease called strongyloidiasis. Although listed as one of the neglected tropical diseases by World Health Organisation (WHO), the infection has a worldwide distribution with approximately 613.9 million cases mostly in tropical countries. Human infection occurs when the infective filariform S. stercoralis larvae in contaminated soil penetrate the intact skin through direct contact, travel to the mouth through bloodstream before it gets swallowed and resides in the gut. In the gut, female adult larvae produce eggs parthenogenetically, continuing their life cycle without having to leave the host’s body through a process called autoinfection. In general, the post-infection symptoms vary in two different ways. In immunocompetent individuals, hosts usually exhibit minimal to no symptoms (asymptomatic) and causes a life-long infection whereas in immunosuppressed individuals, unchecked infection is highly inclinative towards developing hyperinfection syndrome, an event where the larvae over-proliferate and disseminate to organs including the lung, liver, and brain.
Secure Hybrid Scheme For Securing Mqtt Protocol Based On Enhanced Symmetric Algorithm
(2023-03)
Hintaw, Ahmed Jameel
Internet of Things (IoT) enables device and machine communication using TCP/IP protocol. Message Queuing Telemetry Transport (MQTT) is the most preferred protocol and is expected to be the de facto messaging IoT standard. Therefore, MQTT must achieve efficient security. Nevertheless, the most significant drawback of the MQTT is its lack of protection mechanisms which verifies only simple security objects such as non-encrypted authentication and authorization policies, and even there is no encryption mechanism. Data could be altered by intruders while in transit. Researchers have proposed various security techniques to address these issues. Meanwhile, the existing schemes for protecting the MQTT network have added processing overhead to the devices but remain vulnerable for various attacks. Therefore, this research work presented an integrated scheme known as “Secure Hybrid Scheme”, to protect the MQTT protocol against any exploitations that might result in sophisticated cyberattacks. The proposed cryptosystem utilized two algorithms: a dynamic variant of the Advanced Encryption Standard (D-AES) and Key policy attribute base encryption (KP-ABE). A secure hybrid scheme introduces a new design architecture of the symmetric AES algorithm to encrypt the MQTT payload called “D-AES”. The key expansion unit has been strengthened in the D-AES.
Providing Ubiquitous Positioning Using Context Aware Handover Algorithm
(2012-10)
Mohammod Sazid Zaman Khan
This research aims to solve the problem of ubiquitous positioning by designing a context aware handover algorithm for positioning systems. The algorithm performs handover among positioning systems based on important contextual factors related to position determination with efficient use of battery. In addition, the design ensures that no positioning system is allowed to operate for an indefinite period of time without getting a position. The proposed solutions are implemented in an Android application named Locate@nav6.
Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications
(2018-06)
Aun, Yichiet
Traffic classification is becoming more complex due to proliferations of mobile applications coupled with growing diversity of traffic classes. This motivates the needs for improved traffic classification method that preserve classification accuracy while supporting more traffic classes. This thesis identified domain-specific features that are effective for accurate, large-scale and scalable mobile applications classification using machine learning techniques. This thesis designed a context-aware traffic classification framework that includes a set of sequential algorithms from cleaning datasets, to identifying new features and detecting optimal classifier(s) based on problem contexts to improve classification accuracy in multi-variate traffic classification.

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