Rangkaian Neural Untuk Sistem Dapatan Semula Perkataan Daripada Pangkalan Data

Rangkaian neural buatan yang diaspirasikan oleh kecekapan otak manusia memproses maklumat digunakan dengan meluas dalam aplikasi-aplikasi yang melibatkan pengkelasan atau pemetaan corak. Kelebihan utamanya iaitu sifat ketegapannya dalam persekitaran hingar dan keupayaan untuk mengecam input yang tidak sempurna atau cacat menjadikannya alat yang sesuai digunakan untuk dapatan semula maklumat yang pantas berbanding kaedah p engkomputeran konvensional, bagi menangani cabaran dapatan semula yang lebih realistik. Dapatan semula bersekutu menggunakan rangkaian neural adalah untuk mendapatkan semula maklumat (rekod) dengan betul daripada pangkalan data bila kekunci input yang cacat dimasukkan. Model rangkaian neural yang digunakan dalam kajian ini adalah rangkaian Counter propagation, yang merupakan gabungan rangkaian Kohonen dengan algoritma pembelajaran tidak terselia dan rangkaian terselia Grossberg, dengan sifat pengkelasan corak tanpa penyeliaan pada lapisan Kohonen menjadi bahagian paling penting bagi sistem. Kajian memfokuskan penyelidikan kepada prestasi rangkaian khususnya ketepatan pengkelasan bila skema-skema pengkodan yang berbeza digunakan untuk mewakilkan input. Tujuh skema pengkodan telah diaplikasikan dalam kajian ini, dengan jumlah bit perwakilan dan asas pengkodan yang berbeza. Data-data yang digunakan untuk ujian merupakan set bebas ralat, set data dengan ralat tunggal dan set yang mempunyai ralat berganda. Secara keseluruhannya semua eksperimen memberikan keputusan pengecaman yang baik, malah dengan setiap skema perwakilan yang digunakan, rangkaian telah berjaya mengecam dengan tepat kesemua set ujian dengan peratus pengecaman 100%, walaupun dengan bilangan unit persaingan, bilangan pusingan dan masa latihan yang tersendiri. Walau bagaimanapun, rangkaian yang mengaplikasi skema perwakilan dengan asas pengkodan tertentu menunjukkan prestasi yang lebih baik berbanding penggunaan skema tanpa asas pengkodan. Kajian menunjukkan ketepatan pengkelasan dan kecekapan sistem dipengaruhi oleh bentuk perwakilan input yang digunakan, saiz lapisan persaingan serta tempoh pusingan latihan yang optimum

A naturally inspired statistical intrusion detection model

Growing interest in computational models based on natural phenomena with biologically inspired techniques in recent years have been tangible. The use of immune mechanisms in intrusion detection is promising. In [1] we proposed a new IDS model based on the Artificial Immune System (AIS) and a statistical approach. In this paper we are going to enhance that model in terms of detection speed and detection rate as well as overall overload. In contrast with the work in [1] here we do not use the concept of clonal selection and we use binary detector sets which leads to lower overload and therefore higher performance. The model is examined with DARPA data set which is famous among IDS researchers

Investigation of bypassing malware defences and malware detections

Nowadays, malware incident is one of the most expensive damages caused by attackers. Malwares are caused different attacks, so considerations and implementations of malware defences for internal networks are important. In this papers, different techniques such as repacking, reverse engineering and hex editing for bypassing host-based Anti Virus (AV) signatures are illustrated, and the description and comparison of different channels and methods when malware might reach the host from outside the networks are demonstrated. After that, bypassing HTTP/SSL and SMTP malware defences as channels are discussed. Finally, as it is important to find and detect new and unknown malware before the malware gets in to the victims, a new malware detection technique base on honeynet systems is surveyed

Finer garbage collection in LINDACAP.

As open systems persist, garbage collection (GC) can be a vital aspect in managing system resources. Although garbage collection has been proposed for the standard Linda, it was a rather course-grained mechanism. This finer-grained method is offered in Lindacap, a capability-based coordination system for open distributed systems. Multicapabilities in Lindacap enable tuples to be uniquely referenced, thus providing sufficient information on the usability of tuples (data) within the tuple-space. This paper describes the garbage collection mechanism deployed in Lindacap, which involves selectively garbage collecting tuples within tuple-spaces. The authors present the approach using reference counting, followed by the tracing (mark-and-sweep) algorithm to garbage collect cyclic structures. A time-to-idle (TTI) technique is also proposed, which allows for garbage collection of multicapability regions that are being referred to by agents but are not used in a specified length of time. The performance results indicate that the incorporation of garbage collection techniques adds little overhead to the overall performance of the system. The difference between the average overhead caused by the mark-and-sweep and reference counting is small, and can be considered insignificant if the benefits brought by the mark-and-sweep is taken into account

Towards green frameworks for digital forensics investigation

Despite the fact that digital forensics involves strict procedures and complies with fixed regulations and principles, but as this paper presents, there are plenty of opportunities that can be practically employed in digital forensics to make this science greener. Virtualization can cost effectively reduce the number of workstations running forensic tools in the lab. Cloud computing and consolidating servers and storage devices in green data centers not only facilitate managing and securing services but also decline the number of required network and cooling facilities. Forensic labs can also be optimized with regard to environmental preservation. Using remote protocols and digitalizing paperwork procedures are environmentally helpful practices to accelerate investigation progress as well. Improving electrical power needs of labs and forensic devices is another issue that should be taken into consideration. Employing storage devices with optimal energy usage in digital forensics may highly reduce energy consumption. This paper study established green technologies particularly in information technology field and suggests a framework for implementing compatible techniques in digital forensics in order to reduce greenhouse gas pollutants, limit carbon emissions, and preserve the environment

IP spoofing defense: An introduction

In current Internet communication world, validity of source IP packet is and important issue.The problems of IP spoofing alarm the legitimate user of the Internet.This paper review recent progress of spoofing defenses by various researchers.Techniques and mechanisms proposed are being categorized to better illustrate the deployment and functionality of the mechanism.Overall, this paper summarizes the current anti spoofing mechanism in the Internet

An investigation of back-propagation neural network on university selection

Processing thousands of applications can be a challenging task, especially when the applicant does not consider the university requirements and their qualification, while in some cases, the selection officer may face difficulties in deciding if more than one candidate has the same qualification for a limited vacancy of a particular program. In this paper, we present an investigation on university selection using back-propagation neural network to assist the selection officer in selecting eligible applicants based on SPM results. The experiments have shown the back-propagation method produced better performance with the average more than 90% accuracy for student selection across all of sets of the test data

A model for ranking and selecting integrity tests in a distributed database

Checking the consistency of a database state generally involves the execution of integrity tests on the database, which verify whether the database is satisfying its constraints or not. This paper presents the various types of integrity tests as reported in previous works and discusses how these tests can significantly improve the performance of the constraint checking mechanisms without limiting to a certain type of test. Having these test alternatives and selecting the most suitable test is an issue that needs to be tackled. In this regard, the authors propose a model to rank and select the suitable test to be evaluated given several alternative tests. The model uses the amount of data transferred across the network, the number of sites involved, and the amount of data accessed as the parameters in deciding the suitable test. Several analyses have been performed to evaluate the proposed model, and results show that the model achieves a higher percentage of local processing as compared to the previous selected strategies

Establishing private communications in open systems using multicapabilities

Private and secure communications can be vital for certain applications, for example secure bidding, e-commerce, and online bank transactions. However, it is impossible to carry out private conversations in the open tuple-space model, despite it being a popular coordination model for distributed, heterogeneous systems, where agents can communicate via a common data space without having to know each other's identity, or having to arrange for a definite rendezvous. In this paper, we present an algorithm for establishing private communications in tuple-space systems using capabilities. In view of the fact that open systems need to be scalable, capabilities may provide a finer control to the loose control coordination of tuple-space systems whilst maintaining their flexibility. As capabilities can only refer to uniquely identifiable objects, we have introduced the concept of multicapabilities—capabilities for groups of unnamed objects—to be applied to nameless tuples