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The emergence of ICT opens a new paradigm on how we solve problems, including within the area of wealth management. This paper will review the existing techniques of managing secrets in society. This paper will also cover the background of publications, and the sub-topic of interest and issues arising. The important issue of human life will be raised i.e., the need to have a bridge between our time alive and when we die, so that our important secrets can be safely handed over to our heirs. We have identified that gaps exist in current implementations, or that interest in the subject is either scarce or contains missing information. We will propose the framework of an information system to provide a solution to these problems. Several threats and other challenges will also be highlighted. This paper will conclude with a discussion on the need to proliferate further research in this area for the benefit of society and the countryÃ¢ÂÂs economic sustainability.
The outcome of malware execution could be very harmful, either run via software distribution, self-propagation or as a part of direct hacking activities. The aim of this study is to detect malware as it begins to execute and propose a data mining approach for malware detection using sequences of API calls in a Windows environment.
Signature based antivirus as one of the form of techniques of malware detection has the ability to detect and remove any known malware by simply identifying the signature of any malicious file present in its database. The rate at which malware is increasing is very high and it has been an important problem that threatens the security of a computer system. At this level, the focus of this research will be on one set of malware family i.e. the Brontok worms, because it has long been a huge burden to most Window based user platform and because of some of its unique qualities. A prototype of antivirus developed was able to scan files and accurately detect any traces of the Brontok malware signatures found in the scanned files. This research produced a detection model by extracting the signatures of the Brontok and uses n-gram technique to break down the signatures, making it easier to remove redundancies between the signatures of the types of Brontok malware that was used in this research and could accurately differentiate between the signatures of malicious files and normal files. In the experiment, we successfully detect the presence of Brontok worms 100% while also correctly identify the benign ones. The techniques employed in the experiment provides the researcher an insight on creating a good signature based detector which could be used to create a more credible solution to eliminate any threats of old malware which may resurface in the future.