1,720,991 research outputs found
Error-correcting Petri nets
No full textThe paper introduces error-correcting Petri nets, an algebraic methodology for designing synthetic biologic systems with monitoring capabilities. Linear error-correcting codes are used to extend the net’s structure in a way that allows for the algebraic detection and correction of non-reachable net markings. The presented methodology is based on modulo-p Hamming codes—which are optimal for the modulo-p correction of single errors—but also works with any other linear error-correcting code
An innate immune system for the protection of computer networks
No full textThis paper presents design, implementation, and testing of NAIS, an artificial immune system for the protection of computer networks. Inspired by the biological innate immune system, NAIS consists of a collection of digital macrophages that scan the network for dangerous non-self processes, and kill them. NAIS is based on the observation that all significant network attacks are preceded by preparatory small-scale intrusions meant to gather the necessary information – information on servers and operating systems, logins, weak passwords, ill-installed or poorly maintained services, etc. This information is used to bypass the network’s defense barriers – access controls, firewalls – and to gain access to the machine before it is attacked. Such preparatory intrusions do not generate new processes, however the subsequent, actual intrusion will. Such processes will be recognized as non-self by the digital macrophages run by NAIS, and killed right away, thus defusing the attack. Telling illegal new processes from legal ones is a difficult matter, and amounts to providing a strong definition of non-self process. Our testing of NAIS proved our definition to be quite effective in protecting networks of one-service computers
Simulation of Error-Prone Biological Systems
No full textThe paper presents a methodology for the design of simulation models of biological systems that take the occurrence of random errors into account. We suggest: (1) representing the causal structure and prescribed behavior of the system to be simulated by means of Petri net N, (2) extending net N so as to allow for algebraic error detection via Hamming codes over a finite field, (3) simulating net N in a way that introduces unexpected states (random marking errors) into the process, and (4) algebraically detecting such "wrong" states and identifying their erroneous components
Real-time Detection of Pathological Traffic Situations via AIS
No full textThis paper suggests using AIS (Artificial Immune Systems) for automatically detecting and analyzing traffic anomalies due to unpredictable road situations. Various authors [DeCaTi2002], [ForHofSom1996], [TarSkorSoko2003] suggested models and methodologies based on the paradigm of the biological immune system in the framework of network intrusion detection [MykHebLev1994]. We suggest applying an AIS-based recognition model for detecting and controlling pathological road situations, such as traffic jams due to unexpected events. Our approach is based on the idea of defining pathological traffic patterns as antigens, and applying the singular value decomposition (SVD) of matrices of previous abnormal traffic logs to compute matching antibodies. Such antibodies will subsequently be applied to recognize actual traffic patterns as either self (normal traffic), or non-self (pathological traffic). This will be done by (a) using antibodies to map past pathological traffic logs into a subset of a k-dimensional shape space, and cluster them into recognition balls, (b) mapping every new incoming traffic log into a point P of the same shape space, and (c) determine the nature of the incoming traffic log by evaluating its minimum distance recognition balls
SIGNET : a Tool for Securing Complex Petri-Net Projects
No full textSIGNETTM is a multifunctional tool for making secure large, complex Petri net projects. It provides for: (a) authentication, digital signature, and public-key encryption of net plans and their analysis (reachability graphs and net invariants) to be transmitted or stored, and (b) detection and correction of marking errors in Petri nets used for monitoring system implementations
Intrusion Detection via Artificial Immune Networks
No full textThis paper has a two related aims: (1) introducing Artificial Immune Systems as a tool for the protection of computer security, and (2) introducing FIND, a prototypical IDS (Intrusion Detection System) based on this approach. FIND inputs a database previously recorded connection logs, and uses it to generate antibodies capable of recognizing if an incoming connection request of new type as either normal or attack. The learning capabilities of a supervised neural network make FIND capable to respond to the threats of an ever-changing network environment
NAIS : Intrusion Detection via Native Immune Systems
No full textThe paper suggests how to design intrusion detection systems as native artificial immune systems based on a strong definition of self and non-self system components. To illustrate the proposed approach, the paper presents NAIS (Native Artificial Immune System), a network intrusion detection system that mimics the behavior of the native immune system by running a number of macrophage processes, which search and kill non-self processes on a computer network
Profiling Network Attacks via AIS
No full textThe paper extends the intrusion detection methodology proposed by Tarakanov et al. in [8] to k-dimensional shape spaces, for k greater or equal 2. k real vectors, representing antibodies, are used to recognize malicious (or, non-self) connection logs. We suggest a method for recognizing antigens (generating such antibodies) via Singular Value Decomposition of a real-valued matrix obtained by preprocessing a database of connection logs [9]. New incoming connection requests are recognized by the antibodies as either self (normal request), or non-self (potential attack), by (a) mapping them into a k-dimensional shape space, and (b) evaluating the minimum Hamming distance between their image and that of a known attack logs. It is easy to see that using a shape space of dimension greater than 2 significantly reduces false positive
Detection and Analysis of Unexpected State Components in Biological Systems
No full textThis contribution presents a methodology aimed at detecting and analysing unexpected states of biological systems. We suggest using: (a) Petri nets in order to represent the causal structure of a biological system and its prescribed functional behaviour; and (b) algebraic coding theory (Hamming codes) to detect unexpected system states (mutations, unwanted situations, errors).Recent research aimed at modelling biological processes has successfully applied Petri nets to represent the causal structure and processes of biological systems in a way that makes the formal verification of the model easy. The formal language of Petri Nets is a powerful tool for representing biological systems together with their ”regular” behaviour. However, in every living system sooner or later something will ”go wrong” - a disease, or a mutation will occur - bringing the system into a state which our model didn’t take into account, a state that we will call an ”error state” for short. This paper is about introducing algebraic error-detection into Petri-net models. The basic idea is to turn reachable markings into ”legal” words of a linear error-correcting code. This is achieved by adding some control places to the Petri net, so that: (a) its incidence matrix becomes the generatrix of a linear code, and (b) reachable markings can be characterised as solutions of the code’s linear homogeneous system.”Illegal” markings - or, unexpected system states - may then be detected via linear algebra, without having to construct, and search, the net’s (always quite complex) reachability graph. Using suitable linear codes - that is, codes for which fast error-correction algorithms are available - ”mutant” components of ”illegal” markings are instantaneously identified, and can be analysed with regard to other structural properties of the net, such as boundedness and liveness. This contribution considers the case of single errors (point mutations): at most one unexpected component per system state. Single errors are detected via Hamming codes, for which a very fast error detection algorithm is known. We show that the number of control places to be added to the Petri net decreases exponentially with the net’s size. Coding theory offers a wide range of algorithms for the detection of terrors in transmission systems. Applying such algorithms to the detection of unexpected states of biological systems seems a very promising approach
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