Computer Department

A&M-Tote problem of wrambling a discrete-time analog sequence for the purpose of privacy encoding is studied. A large family of linear orthogonal invertible scrambling transformations is desrfbed that result in a negligible expmsion of banhvidth and can therefore serve as building blocks in a secure communication system. I N THIS PAPER we study the problem of scrambling a discrete-time analog sequence for privacy purposes. We are interested in the situation where the sequence to be scrambled is bandlimited and it is desired that the scrambling operation not expand the bandwidth. We will show how to find linear orthogonal invertible scrambling transformations which result in a negligible expansion of bandwidth. In Part II of this paper [3] we will apply these results to scrambling an analog waveform.

Using artificial immune system techniques for malware detection has two major benefits. First, increasing the ability to come over some of the traditional detector's drawbacks, like dealing with the new and polymorphic malware and the increased number of false alarms caused by wrong decision. Second take advantages of the capabilities to learn, adapt, self-tolerance and memories actions, which make it a good example that we can take for solving some major problems in many fields, including the problem of malware detection in computer security which suffering from the rapid increasing in the malware and the problem of false positive alarms. In this paper, we try to highlight the recent techniques applied in malware detection using the artificial immune system from two points of view: self-nonself theory, danger theory.

Detecting palindromes in DNA sequence is a central problem in computational biology. Identifying palindromes could help scientists advance the understanding of genomic instability. DNA sequences containing long adjacent inverted repeats (palindromes) are inherently unstable and are associated with many types of chromosomal rearrangements. In this paper, we present a simple web-base tool to assist biologist detecting palindromes in DNA sequence.

Artificial immune system (AIS) is a computational system inspired by the principles and processes of the Biological immune system which has the capabilities to learn, adapt, self tolerance and memories actions, which make it a good example that we can take for solving some major problems in many fields, including the problem of malware detection in the field of computer security. The main idea is to detect any type of files that trying to harm the computer system by infecting some executable software when these files running, spread it to other files or computers. In this paper, we proposed a framework to detect malware using the innate immune system combined with danger theory to eliminate tow major drawbacks of current malware detection methods; detection accuracy and high false positive alarms.

The massive amount of malware created everyday made the process of malware detection is a significant process to protect data and systems. The methods used are varying from signature based to behavior based, and from static to dynamic detection. Detection accuracy is the main obstacles facing the researchers in this field. Artificial immune system is one of the methods used frequently these days because of its ability to simulate the human immune system and take advantage of its strength in the detection of diseases. In this paper we introduce a dynamic hybrid signature-behavior base model by applying the innate immune system to enhance the detection accuracy. The proposed model is using the portable executable (PE) file representation and API call logs extracted from windows environment because of the wide spread of this type of files in different platforms. The results show that the proposed model accomplishes a better performance in detection of known malware, new unknown malware and polymorphic malware.