University of Memphis

About: University of Memphis is a education organization based out in Memphis, Tennessee, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 7710 authors who have published 20082 publications receiving 611618 citations. The organization is also known as: U of M.

Are Metabolic Risk Factors One Unified Syndrome? Modeling the Structure of the Metabolic Syndrome X

Abstract: The metabolic syndrome, manifested by insulin resistance, obesity, dyslipidemia, and hypertension, is conceived to increase the risk for coronary heart disease and type II diabetes. Several studies have used factor analysis to explore its underlying structure among related risk variables but reported different results. Taking a hypothesis-testing approach, this study used confirmatory factor analysis to specify and test the factor structure of the metabolic syndrome. A hierarchical four-factor model, with an overarching metabolic syndrome factor uniting the insulin resistance, obesity, lipid, and blood pressure factors, was proposed and tested with 847 men who participated in the Normative Aging Study between 1987 and 1991. Simultaneous multi-group analyses were also conducted to test the stability of the proposed model across younger and older participants and across individuals with and without cardiovascular disease. The findings demonstrated that the proposed structure was well supported (comparative fit index = 0.97, root mean square error approximation = 0.06) and stable across subgroups. The metabolic syndrome was represented primarily by the insulin resistance and obesity factors, followed by the lipid factor, and, to a lesser extent, the blood pressure factor. This study provides an empirical foundation for conceptualizing and measuring the metabolic syndrome that unites four related components (insulin resistance, obesity, lipids, and blood pressure).

Immunity-Based Intrusion Detection System: A General Framework

Abstract: This paper focuses on investigating immunological principles in designing a multi-agent system for intrusion/anomaly detection and response in networked computers. In this approach, the immunity-based agents roam around the machines (nodes or routers), and monitor the situation in the network (i.e. look for changes such as malfunctions, faults, abnormalities, misuse, deviations, intrusions, etc.). These agents can mutually recognize each other's activities and can take appropriate actions according to the underlying security policies. Specifically, their activities are coordinated in a hierarchical fashion while sensing, communicating and generating responses. Such an agent can learn and adapt to its environment dynamically and can detect both known and unknown intrusions. This research is the part of an effort to develop a multi-agent detection system that can simultaneously monitor networked computer's activities at different levels (such as user level, system level, process level and packet level) in order to determine intrusions and anomalies. The proposed intrusion detection system is designed to be flexible, extendible, and adaptable that can perform real-time monitoring in accordance with the needs and preferences of network administrators. This paper provides the conceptual view and a general framework of the proposed system. 1. Inspiration from the nature: Every organism in nature is constantly threatened by other organisms, and each species has evolved elaborate set of protective measures called, collectively, the immune system. The natural immune system is an adaptive learning system that is highly distributive in nature. It employs multi-level defense mechanisms to make rapid, highly specific and often very protective responses against wide variety of pathogenic microorganisms. The immune system is a subject of great research interest because of its powerful information processing capabilities [5,6]. Specifically, its' mechanisms to extract unique signatures from antigens and ability to recognize and classify dangerous antigenic peptides are very important. It also uses memory to remember signature patterns that have been seen previously, and use combinatorics to construct antibody for efficient detection. It is observed that the overall behavior of the system is an emergent property of several local interactions. Moreover, the immune response can be either local or systemic, depending on the route and property of the antigenic challenge [19]. The immune system is consists of different populations of immune cells (mainly B or T cells) which circulate at various primary and secondary lymphoid organs of the body. They are carefully controlled to ensure that appropriate populations of B and T cells (naive, effector, and memory) are recruited into different location [19]. This differential migration of lymphocyte subpopulations at different locations (organs) of the body is called trafficking or homing. The lymph nodes and organs provide specialized local environment (called germinal center) during pathogenic attack in any part of the body. This dynamic mechanism support to create a large number of antigen-specific lymphocytes (as effector and memory cells) for stronger defense through the process of the clonal expansion and differentiation. Interestingly, memory cells exhibit selective homing to the type of tissue in which they first encountered an antigen. Presumably this ensures that a particular memory cell will return to the location where it is most likely to re-encounter a subsequent antigenic challenge. The mechanisms of immune responses are self-regulatory in nature. There is no central organ that controls the functions of the immune system. The regulation of the clonal expansion and proliferation of B cells are closely regulated (with a co-stimulation) in order to prevent uncontrolled immune response. This second signal helps to ensure tolerance and judge between dangerous and harmless invaders. So the purpose of this accompanying signal in identifying a non-self is to minimize false alarm and to generate decisive response in case of a real danger[19]. 2. Existing works in Intrusion Detection: The study of security in computer networks is a rapidly growing area of interest because of the proliferation of networks (LANs, WANs etc.), greater deployment of shared computer databases (packages) and the increasing reliance of companies, institutions and individuals on such data. Though there are many levels of access protection to computing and network resources, yet the intruders are finding ways to entry into many sites and systems, and causing major damages. So the task of providing and maintaining proper security in a network system becomes a challenging issue. Intrusion/Anomaly detection is an important part of computer security. It provides an additional layer of defense against computer misuse (abuse) after physical, authentication and access control. There exist different methods for intrusion detection [7,23,25,29] and the early models include IDES (later versions NIDES and MIDAS), W & S, AudES, NADIR, DIDS, etc. These approaches monitor audit trails generated by systems and user applications and perform various statistical analyses in order to derive regularities in behavior pattern. These works based on the hypothesis that an intruder's behavior will be noticeably different from that of a legitimate user, and security violations can be detected by monitoring these audit trails. Most of these methods, however, used to monitor a single host [13,14], though NADIR and DIDS can collect and aggregate audit data from a number of hosts to detect intrusions. However, in all cases, there is no real analysis of patterns of network activities and they only perform centralized analysis. Recent works include GrIDS[27] which used hierarchical graphs to detect attacks on networked systems. Other approaches used autonomous agent architectures [1,2,26] for distributed intrusion detection. 3. Computer Immune Systems: The security in the field of computing may be considered as analogous to the immunity in natural systems. In computing, threats and dangers (of compromising privacy, integrity, and availability) may arise because of malfunction of components or intrusive activities (both internal and external). The idea of using immunological principles in computer security [9-11,15,16,18] started since 1994. Stephanie Forrest and her group at the University of New Mexico have been working on a research project with a long-term goal to build an artificial immune system for computers [911,15,16]. This immunity-based system has much more sophisticated notions of identity and protection than those afforded by current operating systems, and it is suppose to provide a general-purpose protection system to augment current computer security systems. The security of computer systems depends on such activities as detecting unauthorized use of computer facilities, maintaining the integrity of data files, and preventing the spread of computer viruses. The problem of protecting computer systems from harmful viruses is viewed as an instance of the more general problem of distinguishing self (legitimate users, uncorrupted data, etc.) from dangerous other (unauthorized users, viruses, and other malicious agents). This method (called the negative-selection algorithm) is intended to be complementary to the more traditional cryptographic and deterministic approaches to computer security. As an initial step, the negativeselection algorithm has been used as a file-authentication method on the problem of computer virus detection [9].

Negative Selection Algorithm for Aircraft Fault Detection

Abstract: We investigated a real-valued Negative Selection Algorithm (NSA) for fault detection in man-in-the-loop aircraft operation. The detection algorithm uses body-axes angular rate sensory data exhibiting the normal flight behavior patterns, to generate probabilistically a set of fault detectors that can detect any abnormalities (including faults and damages) in the behavior pattern of the aircraft flight. We performed experiments with datasets (collected under normal and various simulated failure conditions) using the NASA Ames man-in-the-loop high-fidelity C-17 flight simulator. The paper provides results of experiments with different datasets representing various failure conditions.