17,643 research outputs found
Immunotronics - novel finite-state-machine architectures with built-in self-test using self-nonself differentiation
A novel approach to hardware fault tolerance is demonstrated that takes inspiration from the human immune system as a method of fault detection. The human immune system is a remarkable system of interacting cells and organs that protect the body from invasion and maintains reliable operation even in the presence of invading bacteria or viruses. This paper seeks to address the field of electronic hardware fault tolerance from an immunological perspective with the aim of showing how novel methods based upon the operation of the immune system can both complement and create new approaches to the development of fault detection mechanisms for reliable hardware systems. In particular, it is shown that by use of partial matching, as prevalent in biological systems, high fault coverage can be achieved with the added advantage of reducing memory requirements. The development of a generic finite-state-machine immunization procedure is discussed that allows any system that can be represented in such a manner to be "immunized" against the occurrence of faulty operation. This is demonstrated by the creation of an immunized decade counter that can detect the presence of faults in real tim
ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System
Security of computers and the networks that connect them is increasingly
becoming of great significance. Computer security is defined as the protection
of computing systems against threats to confidentiality, integrity, and
availability. There are two types of intruders: the external intruders who are
unauthorized users of the machines they attack, and internal intruders, who
have permission to access the system with some restrictions. Due to the fact
that it is more and more improbable to a system administrator to recognize and
manually intervene to stop an attack, there is an increasing recognition that
ID systems should have a lot to earn on following its basic principles on the
behavior of complex natural systems, namely in what refers to
self-organization, allowing for a real distributed and collective perception of
this phenomena. With that aim in mind, the present work presents a
self-organized ant colony based intrusion detection system (ANTIDS) to detect
intrusions in a network infrastructure. The performance is compared among
conventional soft computing paradigms like Decision Trees, Support Vector
Machines and Linear Genetic Programming to model fast, online and efficient
intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special
track at WSTST 2005, Muroran, JAPA
The dendritic cell algorithm for intrusion detection
As one of the solutions to intrusion detection problems, Artificial Immune
Systems (AIS) have shown their advantages. Unlike genetic algorithms, there is
no one archetypal AIS, instead there are four major paradigms. Among them, the
Dendritic Cell Algorithm (DCA) has produced promising results in various
applications. The aim of this chapter is to demonstrate the potential for the
DCA as a suitable candidate for intrusion detection problems. We review some of
the commonly used AIS paradigms for intrusion detection problems and
demonstrate the advantages of one particular algorithm, the DCA. In order to
clearly describe the algorithm, the background to its development and a formal
definition are given. In addition, improvements to the original DCA are
presented and their implications are discussed, including previous work done on
an online analysis component with segmentation and ongoing work on automated
data preprocessing. Based on preliminary results, both improvements appear to
be promising for online anomaly-based intrusion detection.Comment: Bio-Inspired Communications and Networking, IGI Global, 84-102, 201
Nature-inspired survivability: Prey-inspired survivability countermeasures for cloud computing security challenges
As cloud computing environments become complex, adversaries have become highly sophisticated and unpredictable. Moreover, they can easily increase attack power and persist longer before detection. Uncertain malicious actions, latent risks, Unobserved or Unobservable risks (UUURs) characterise this new threat domain. This thesis proposes prey-inspired survivability to address unpredictable security challenges borne out of UUURs. While survivability is a well-addressed phenomenon in non-extinct prey animals, applying prey survivability to cloud computing directly is challenging due to contradicting end goals. How to manage evolving survivability goals and requirements under contradicting environmental conditions adds to the challenges. To address these challenges, this thesis proposes a holistic taxonomy which integrate multiple and disparate perspectives of cloud security challenges. In addition, it proposes the TRIZ (Teorija Rezbenija Izobretatelskib Zadach) to derive prey-inspired solutions through resolving contradiction. First, it develops a 3-step process to facilitate interdomain transfer of
concepts from nature to cloud. Moreover, TRIZ’s generic approach suggests specific
solutions for cloud computing survivability. Then, the thesis presents the conceptual prey-inspired cloud computing survivability framework (Pi-CCSF), built upon TRIZ derived solutions. The framework run-time is pushed to the user-space to support evolving survivability design goals. Furthermore, a target-based decision-making technique (TBDM) is proposed to manage survivability decisions. To evaluate the prey-inspired survivability concept, Pi-CCSF simulator is developed and implemented. Evaluation results shows that escalating survivability actions improve the vitality of vulnerable and compromised virtual machines (VMs) by 5% and dramatically improve their overall survivability. Hypothesis testing conclusively supports the hypothesis that the escalation mechanisms can be applied to enhance the survivability of cloud computing systems. Numeric analysis of TBDM shows that by considering survivability preferences and attitudes (these directly impacts survivability actions), the TBDM method brings unpredictable survivability information closer to decision processes. This enables efficient execution of variable escalating survivability actions, which enables the Pi-CCSF’s decision
system (DS) to focus upon decisions that achieve survivability outcomes under unpredictability imposed by UUUR
BIOLOGICAL INSPIRED INTRUSION PREVENTION AND SELF-HEALING SYSTEM FOR CRITICAL SERVICES NETWORK
With the explosive development of the critical services network systems and Internet, the need for networks security systems have become even critical with the enlargement of information technology in everyday life. Intrusion Prevention System (IPS) provides an in-line mechanism focus on identifying and blocking malicious network activity in real time. This thesis presents new intrusion prevention and self-healing system (SH) for critical services network security. The design features of the proposed system are inspired by the human immune system, integrated with pattern recognition nonlinear classification algorithm and machine learning. Firstly, the current intrusions preventions systems, biological innate and adaptive immune systems, autonomic computing and self-healing mechanisms are studied and analyzed. The importance of intrusion prevention system recommends that artificial immune systems (AIS) should incorporate abstraction models from innate, adaptive immune system, pattern recognition, machine learning and self-healing mechanisms to present autonomous IPS system with fast and high accurate detection and prevention performance and survivability for critical services network system. Secondly, specification language, system design, mathematical and computational models for IPS and SH system are established, which are based upon nonlinear classification, prevention predictability trust, analysis, self-adaptation and self-healing algorithms. Finally, the validation of the system carried out by simulation tests, measuring, benchmarking and comparative studies. New benchmarking metrics for detection capabilities, prevention predictability trust and self-healing reliability are introduced as contributions for the IPS and SH system measuring and validation.
Using the software system, design theories, AIS features, new nonlinear classification algorithm, and self-healing system show how the use of presented systems can ensure safety for critical services networks and heal the damage caused by intrusion. This autonomous system improves the performance of the current intrusion prevention system and carries on system continuity by using self-healing mechanism
Celebration Schedule 2015 (Friday)
Full presentation schedule for Celebration, Friday, May 1, 201
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