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1 2386 Survey: Biological Insired Comuting in the Network Security V Venkata Ramana Associate Professor, Deartment of CSE, CBIT, Proddatur, Y.S.R (dist), A.P Y.Subba Reddy Associate Professor, Deartment of CSE, CBIT, Proddatur, Y.S.R (dist), A.P G.Rama Subba Reddy Associate Professor, Deartment of CSE, CBIT, Proddatur, Y.S.R (dist), A.P Dr.Pandurangan Ravi Professor & Princial, Deartment of CSE, CBIT, Proddatur, Y.S.R (dist), A.P ABSTRACT Traditional comuting techniques and systems consider a main rocess device or main server, and technique details generally serially. They're non-robust and non-adative, and have limited quantity. Indifference, scientific technique details in a very similar and allocated manner, while not a main management. They're exceedingly strong, elastic, and ascendible. This aer offers a short conclusion of however the ideas from biology are will never to style new rocessing techniques and techniques that even have a number of the beneficial qualities of scientific techniques. Additionally, some illustrations are a device given of however these techniques will be used in details security rograms. Keywords Bio-Insired,comuting,networksecurity,Robust,adaitive Date of Submission: January 20, 2015 Date of Accetance: January 23, I. INTRODUCTION The ower and recognition of current comuting systems is basically as a effect of more agile and quicker CPUs and additional and additional memory convenience at low value. Yet, these traditional comuting ways, architectures, systems, and networks largely think about a central rocess unit or a central server, they method data serially, and that they rely uon humans to be rogrammed and told what to try to (and how). It has some serious drawbacks. Foremost, the systems don't seem to be awfully secure. If one a art of a system breaks down, the comlete system is useless. Second, they're not adatations. Most comuting systems don't read (or have solely restricted learning caability), and can't change or adjust to fresh or surrising things while not human intervention. Third, there's solely restricted measurability. The bigger the organization becomes, or the extra nodes are value-added to the network, the uer the emloyment of the C.P.U. Or server becomes, till it cannot method all directions or service requests during a chea time any longer. In comarison, most scientific techniques rocess details in a similar and allocated way, without the lifestyle of a central control. They usually involve a huge variety of relatively easy ersonal units, which act in similar and communicate only regionally. For examle, the mind includes a huge variety of easy nerves (more or less comarative to on-off switches), each of which is connected only to a relatively small ortion of all other nerves. Yet quantity of details handling is going on in the mind, where each neuron works only asect of the handling, but they all do so in alike. In social est hives, such as bugs and bees, a huge variety of relatively easy individuals manage to build comlex nests or find the quickest ath between the home and a food source, again in a similar and allocated way. The human immunity rocesses is another examle, where (simle) ersonal defense cells erform only asect of the comlete task, but there are many of them working together in similar. [1] This arallel and distributed rocessing method makes these systems highly robust. If some individual units in the system break down, the system as a whole will still function. In fact, it is easy to reair or relace broken units without having to shut down the entire system. Furthermore, these systems are highly scalable. As many individual units can be added as desired, since there are only local interactions involved, and there will be no overload on one articular art of the system. Finally, most systems in nature are adative, either through learning (in individual organisms) or through evolution (at the level of oulations or secies). They can adjust to changing situations or even coe with entirely new situations. So, there are many advantages in biological systems that would be desirable to have in our comuting systems. In this aer, a quick summary is given of however ideas from biology are wont to style new comuting strategies. This can be tyically mentioned as biologically galvanized comuting [1]. These strategies overcome a number of the disadvantages of ancient comuting, creating them a lot of strong, adative, and ascendible. Esecially, 3 examles

2 2387 are reviewed: (1) genetic algorithms, (2) neural networks, and (3) artificial immune systems. Moreover, for everyone of those 3 strategies, some actual alications within the sace of knowledge security also are reresented, esecially in crytograhy, life science for security, and lato and network security. The biological ideas and concets underlying the strategies reresented here are often found in any commonlace textbook on biology, like [2] and [3]. 2. Genetic Algorithms in Crytograhy [2] Genetic algorithms were develoed within the 60s and 70s by John Holland and his colleagues and students. They were used each as easy models of evolution and adatation, and as new lato algorithms to seek out sensible solutions to troublesome imrovement issues. Later, they became very fashionable as a general imrovement tool, and that they are alied with success to a good vary of issues. This section offers a quick summary of the algorithmic rule (more details are often found in [4], [5], [6], and [7]), and a few secific alications of genetic algorithms in crytograhy and writing are described. 2.1 Genetic Algorithms A Genetic Algorithm (GA) could be a unique look for technique reinforced concets from genetic and natural rogress and choice. It s one among variety of rocess techniques generally mentioned as natural evolutionary comutation (EC). rather than trying to straight fix a move, an answer is rogressed over time by keeing a oulation of (initially random) candidate alternatives, creating subsequent generations by recombining comletely different comonents of this best alternatives within the oulation. This way, new candidate evaluate tested reinforced based on current samle, wherever the look for is target-hunting by a range method that refers the (currently) best alternatives within the oulation to use to make new ( offsring ) alternatives. Given some otimization disadvantage, first an aroriate crytograhy for alicant alternatives has to be discovered. Usually, this crytograhy requires the way of ersonality ost like bit ost (i.e., ost of 0s and 1s). This is often analogous to the scientific difference between the genotye (the inherited encoding) and also the cosmetics (the real tye and aearance) of a living thing. As an examle, in chart roblems wherever some the best ossible set or artition of the nodes has to be discovered (such as a lowest rotect or most cut set), a little sequence crytograhy are often used wherever every bit lace matches to 1 articular node within the chart. Develoment of the articular alicant quality (henotye) from a given bit sequence (genotye) is finished as follows. For every bit with value one, the corresonding node within the chart is surrounded within the alicant set (or laced on one asect of the alicant artition), and for every bit with value zero, the corresonding node isn't surrounded within the set (or laced on the other asect of the artition). This way, the GA will straight look for the (much simler) lace of bit ost rather than the lace of real alicant alternatives, even as organic rogress haens at the level of genotyes. Next, a fitness function needs to be designed which can be used to evaluate candidate alternatives. The main idea is that this oerate takes as its inut an secured candidate solution (e.g., a bit string), converts this into an actual candidate solution (e.g., a artition of the nodes of a grah), and returns a variety according to how good this candidate solution is for the given issue (e.g., the count of sides between nodes from different sides of the artition for the maximum cut roblem). This number, and fitness value, indicates the goodness of a candidate solution: higher fitness values mean better solution. This way, the GA can erform choice based on these fitness rinciles, just as natural choice haens at the level of the henotyes. Given an aroriate develoment and fitness function (which have to be develoed indeendently for each otimization roblem that is considered), the real criteria is relatively simle. Suosing a bit sequence develoment is used, the rimary GA erforms as follows which is roven in Algorithm 1 (the choice and cross-over & mutation roviders are described below): Algorithm 1: Selection and Mutation rocess [12] 1. Initialize the oulation with N random bit strings ( individuals ), calculate their fitness values, and set gen=1. 2. Create a mating ool by selecting (with relacement) N individuals from the current oulation based on fitness. 3. While still individuals in the mating ool, do: a. Remove the next air of individuals ( arents ) from the mating ool. b. With robability c erform crossover between the arents to create two children. c. With robability m erform mutation on the children. d. Place the children in the new oulation. 4. Relace the revious oulation with the new oulation, calculate the fitness of all individuals, and set gen=gen If gen < M go to ste 2, otherwise sto. There are many ways in which the selection oerator can be imlemented, but the main idea is that individuals with higher fitness values, comared to the rest of the oulation, have a higher chance of being selected than individuals with lower fitness values (i.e., fitness roortionate selection). In other words, the mating ool

3 2388 will (on average) contain multile coies of the best individuals in the current oulation and no (or just a few) coies of the worst individuals. The crossover oerator literally chos u the genotyes of the arent individuals and recombines them to create offsring genotyes. The most basic method is one-oint crossover, in which a random crossover oint is first chosen (somewhere between the first and last bit), and the first art of the first arent is recombined with the second art of the second arent to create the first child (and vice versa for the second child). Usually crossover is done with a certain robability c (often set in the range [0.6;0.95]) for each air of arents. If crossover is not erformed, the children will be identical to their arents. Finally, with a usually very low robability m, mutation is erformed, where a bit is flied at random. Examles of (one-oint) crossover and mutation are shown below. In the crossover examle, the crossover oint is (randomly) chosen between the 3 rd and 4 th bit. In the mutation examle, the 0 at the 9 th osition is mutated into a 1 (shown in bold). crossover mutation Finally, the creation of new generations of candidate solutions by selection and crossover & mutation is reeated for a set number M of generations. Other stoing criteria are ossible, of course, such as reaching a certain level of fitness or a maximum amount of comuting time. In short, the main idea of the algorithm is to evolve better and better solutions by reeatedly selecting the best candidate solutions from the current oulation and recombining arts of their genotyes to create subsequent generations of candidate solutions. 2.2 Alications of Genetic Algorithms in Crytograhy and Coding A little summary of the status of the art and of still start issuing in using transformative calculation methods (such as genetic algorithms) in secret writing is resented in [8]. In crytograhy, it is very imortant know how challenging it is to break a security technique. Obviously, methods that are very challenging to crack are recommended over methods that are more quickly damaged. Crytanalysis is all about examining (or attacking ) security methods to distinguish out how comfortable or challenging they are to break. Genetic methods have been alied efficiently in this field, for examle in fighting relacement cihers [9], [10] and transosition cihers [11]. Although this does not straight cause better cihers, it does dislay where their weak oints are, which often can hel in raising them. Furthermore, in [12] an inherited criteria were utilized efficiently to discover Boolean features with good crytograhic qualities, thus dislaying how these methods can also be used straight for building security methods. An essential strategy that is frequently used in crytograhy is that of roducing seudo random numbers. Hither, the aim is to get a random act (by some deterministic method) that is every bit singular as ossible, and which bears a higher interval (i.e., it will not answer it again itself quickly). An exciting scheme, using a transformative strategy just like GAs, was resented in [13], where mobile automata (simle identical and allocated rocessing devices) were advanced to generate seudo unique figures with a higher degree of entroy. As an examle, consider rogramming methods for transmitting information. Next to offering information rotection through rotection, it is likewise imortant that information reduction is reduced during transmitting of secured data. Inherited methods have been alied efficiently to imrove so-called turbo codes [14]. In this situation, the GA was able to find a small bit better rule than what was usable at the time. These rograms are just a selection of the many oortunities of imlementing inherited methods and other transformative calculations techniques in the area of information security. Next, an introduction to sensory systems, another naturally motivated rocessing method, is rovided. 3. Neural Networks in Biometrics for Security [3] The research on neural networks was ioneered by McCulloch and Pitts in 1943 [15]. They gave a logical (mathematical) model of a simle neuron, and demonstrated that a suitably constructed network of such artificial neurons can, in rincile, comute any comutable function. Thusly, a neural network is equivalent (in terms of comutational ower) to a general Turing machine, but with a very different architecture. In this section, first the concet of neural networks is briefly surveyed. A full introduction to comuting with neural networks is rovided in [16], and more detailed info can be found in any standard textbook on neural networks, such as [17] and [18]. Next, an examle of an alication of neural networks in biometrics for security is traced. 3.1 Neural Networks A neural networks (NN) is a arallel distributed rocessing (PDP) structure that is reared after the working of the brain. It can erform calculations, in articular category of information, and rovides an examle of an alternative design of calculations as oosed to serially and centrally based calculations of conventional handling systems.

4 2389 Our minds involve many (around 10 billion) simle tissues known as nerves. Each neuron includes a mobile whole body, an axon (a ointed transmission line through which substance alerts can travel), and many dendrites (a treelike framework of many branching tentacles ), which end in synases which tye relationshis with the axons of other nerves. Basically ut, each neuron gets information (the existence or lack of signals) from other nerves through the synatic relationshis, which journey down the dendrites of the mobile whole body. Here, the information are added u, and if a certain limit is obtained the neuron delivers out an indication itself through its axon, which is then develoed an feedback to yet other nerves which are linked with its axon. Nevertheless, not all synatic relationshis are equivalent. Some are more owerful than others, and then some information has a greater weight than others. Studying is obtained by modifying the strong oints (weights) of current synatic relationshis, or by rearing new or removing old relationshis. go forward and rovide as information to the first invisible art. These nerves then generate their results which rovide as information to the next invisible art (if resent), until the ultimate, or outcome, art is achieved. The condition of the nerves in the outcome art can then be considered as the answer. For examle, in category issues, if the condition of the first outcome neuron is 1 and that of the second one is 0, the feedback linked with one category. If their last declares are changed (i.e., 0 and 1, resectively), then the feedback linked with the other category (assuming there are two sessions into which to artition the inuts). Other system architectures are of course also ossible, such as reeated systems, where relationshis can nourish returning to ast levels as well, or lines systems, where the nerves are organized in a lines with relationshis between nearby nerves. This will be exalianed in figure 2. A simlified examle of a real neuron is illustrated in the x figure below. A neuron receives inuts ( i ) from other w neurons, which are weighted ( i ) and the en added ( y ). The roduction of a neuron is a function of this weighted sum of inuts, and can in turn constitute the inut to other nerve cells inut to other neurons. In the simlest case, each inut can be either 0 (absence of signal) or 1 (resence of signal), and the outut function is a ste function such that the outut is 0 if the weighted sum of inuts is below a certain threshold value, and 1 if it is above the threshold value. In more realistic cases, the inuts and oututs are real valued numbers within some range, and the outut function is for examle a sigmoidal shae which is shown in figure 1. Figure 1: Neural Network Architecture Any variety of such nerves can be linked with each other to tye an synthetic sensory system. A conventional system structure that is often used is a nourish ahead system. In such a NN, there is one art of feedback nerves, one or more levels of hidden nerves, and one art of outcome nerves, as shown in the determine on the next web age. The nerves in the feedback art are initialized with some feedback design, and the resultss from this art Figure 2: Internal Architecture of Neural Network Given some neural network structure, it is not straight aarent how to set the loads on the relationshis to get a certain system actions. However, several coaching methods have been develoed to imrove these loads. The rimary concet of these methods is to continuously existing the system with examle information for which the aroriate resonse is known. The loads in the system are then modified based on the quantity of mistake between the aroriate resonse and that given by the system. This is recurring until no more mistakes are created, or the quantity of mistake dros below a certain limit. The system can now be said to have discovered the given rocess. At the next level, the system can be used to execute the rocess on new information which it might not have seen before. 3.2 Alications of Neural Networks in Fingerrint Recognition One lace where neural networks have become very oular is icture handling, such as design identification and category, disturbance filtration, advantage recognition, etc. As an rogram in biometrics for rotection, they can be used efficiently for finger marks identification. Finger rint identification is often divided u in two stages: (1) function removal, and (2) category. In the first level, certain functions from a finger marks icture are roduced,

5 2390 such as variety guidelines, archways and whorls, delta oints, etc. (for a more secific summary, see for examle [19]). In the second level, these functions are used to identify (or classify) the given finger marks icture. Neural systems have been used efficiently in both of these levels, often roviding increase to high correct category rices and low incorrect being rejected rices, and frequently outerforming more conventional methods (see for examle [20], [21], [22], [23], and [24]). Furthermore, sensory systems can be used in the same way for other icture identification rojects in biometrics rotection, such as retina or eye check out categories, or for seech identification. Finally, as a last examle of naturally motivated handling in the lace of information rotection, a brief summary of synthetic immunity rocesses for comuter rotection is rovided in the next section. 4. Artificial Immune Systems for Comuter Security A very recent concet that is still being develoed is that of building a c defense mechanisms. The task of such a rogram is to rovide c and network security based on the technicalities of the individual defense mechanisms. This area first rovides a high-level and somewhat simle summary of the individual defense mechanisms. A good release to this subject can be found in [25]. Next, an examle of an execution of a simle c defense mechanisms is given to demonstrate the usefulness of the concet. 4.1 The human Immune System The individual defense mechanisms is a comlicated and multi-layered rogram. The asect that is of most attention here is the flexible defense reaction. A brief summary of this is given below, with many information remaining out. However, the common qualities of this asect of the defense mechanisms rovide as a lace to start for the style of a synthetic immunity rocesses for comuter and system rotection. The body system includes many different kinds of elements (mostly roteins), which are generally known as self. Everything else, such as things that make us ill, is generally known as non-self. So, the main rocess of the defense mechanisms is to differentiate non-self from self, and induce a reaction whenever non-self necessary rotein are recognized. However, this is not always easy as there are an aroximated non-self necessary rotein that the immunity rocesses needs to identify, in comarison to about self necessary rotein. The way the defense mechanisms resolves this roblem is by using a owerful and allocated rogram. Whenever you want, many detector tissues, such as socalled T-cells, flow through our organizations. These tissues older in a body known as the thymus, where they are revealed to most of the self necessary roteins that create u our systems. If any of the growing T-cells holds to any of these self necessary roteins, that T-cell is transferred. Hence, the only T-cells that revent the thymus are those that do not unite to self necessary roteins. Therefore, if a grew u T-cell does combine to a rotein, it suggests this must be a non-self roteins, and an aroriate defense reaction will be triggered. Nevertheless, not all T-cellular telehones are caable to merge two (or recognize ) entirely ossible non-self necessary roteins, but some T-cells combine to some non-self necessary roteins, other T-cells to others, etc. In this way, the defense mechanisms is an allocated rogram. [4] It is also owerful, as T-cells are consistently changed through an inherited rocess such as the difference (or unique mutations ). This way, the set of non-self necessary rotein that the defense mechanisms is able to identify, changes eventually. Since it is imossible to identify all ossible non-self necessary rotein at any once, this owerful rogram is the next best solution. Furthermore, because of this, no two individuals will have exactly the same set of T-cells at some oint, so what might make me fed u, my next door neighbor might be safe from, and the other way around. [5] Lastly, the defense mechanisms also has a memory. It is able of keeing in mind illness-causing non-self necessary rotein (antigens), so that when a erson gets contaminated with the same antigen, it is identified instantly and an aroriate defense reaction can be activated, avoiding the real sickness from haening again. 4.2 Comuter Immunology Forrest and learners were some of the officers of using concets from the human immunity rocesses to design an attack recognition rogram for comuter systems and systems [26], [27]. In articular, in [28] they show the results of a basic execution based on checking short series of rogram hone calls. Temorarily, the idea is as follows. In the first level, a data source of rogram contact series during normal activities is built. This data source thus contains the series that reresent self. In the next level, rogram contact series are examined during rogram function that might contain attack efforts. These series are then as oosed to available data source, and any series that is not resent in the data source ( non-self ) activates an alarm. This way, irregular activities can be easily recognized, and aroriate activities can be conducted if necessary. Obviously, the data source containing regular actions have to be modified regularly. For examle, including new customers or alication and comonents to the rogram will modify the regular actions, or a user s actions might

6 2391 modify over time (different rojects, different main concerns, etc.). However, with this style, the attack recognition rogram becomes more flexible, as it is able of acknowledging irregular actions that has not been noticed before. In other terms, the rogram can recognize, for examle, new malware or new fighting systems, without the need for installing new malware signatures from some main server first. Furthermore, different comuter systems will have different data source of self actions, so a malware that infects one c, might not be able to contaminate every other c. This way, the system as a whole also has a better (distributed) security. The (small-scale) illustrations and simulator that have been alied so far indicate the stability of these concets, and dislay a aealing ucoming. Currently, the concets and styles are still being designed further, and are also being grabbed by others [29], [30]. Pc immunology and synthetic immunity rocesses are now an effective area of analysis. 5. Conclusion Traditional handling techniques have several drawbacks, such as a lack of sturdiness and flexibility, and limited scalability. In contrast, scientific techniques, being mostly similar allocated handling techniques, are highly effective, convenient, and scalable. Naturally motivated handling includes the design, execution, and alication of new c techniques and techniques that integrate these beneficial qualities of scientific techniques. In this aer, a brief summary of biologically motivated handling has been resented, with some secific examles of how these techniques can be used in details rotection in articular. Many of these techniques have already been alied efficiently, such as inherited methods and sensory networks, and some are still being further develoed, such as c immunology. It is clear that the area of details rotection can benefit greatly from these new and interesting handling techniques. References [1]. L. N. de Castro and F. J. Von Zuben, Recent Develoments in Biologically Insired Comuting. Idea Grou Publishing, [2]. S. Alters, Biology: Understanding Life. Mosby, [3]. N. A. Cambell, L. G. Mitchell, and J. B. Reece, Biology: Concets & Connections, 2 nd edition. Benjamin Cummings, [4]. J. H. Holland, Adatation in Natural and Artificial Systems. University of Michigan Press, [5]. J. H. Holland, Genetic Algorithms, Scientific American, vol. 267 (1), , [6]. D. E. Goldberg, Genetic Algorithms in Search, Otimization, and Machine Learning, Addison- Wesley, [7]. M. Mitchell, An Introduction to Genetic Algorithms. MIT Press, [8]. P. Isasi and J. C. Hernández, Introduction to the alications of evolutionary comutation in comuter security and crytograhy, Comutational Intelligence, vol. 20 (3), , [9]. R. Sillman, M. Janssen, B. Nelson, and M. Kener, Use of a genetic algorithm in the crytanalysis of simle substitution cihers, Crytologica, vol. 17 (1), , [10]. A. Clark and E. Dawson, Otimisation heuristic for the automated crytanalysis of classical cihers, Journal of Combinatorial Mathematics and Combinatorial Comuting, vol. 28, , [11]. R. A. J. Matthews, The use of genetic algorithms in crytanalysis, Crytologica, vol. 17 (2), , [12]. W. Millan, A. Clark, and E. Dawson, An effective genetic algorithm for finding Boolean functions, in Proceedings of the International Conference on Information and Communications Security, [13]. M. Sier and M. Tomassini, Co-evolving arallel random number generators, in Proceedings of the Parallel Problem Solving from Nature Conference, 1996, [14]. N. Durand, J.-M. Alliot, and B. Bartolomé, Turbo codes otimization using genetic algorithms, in Proceedings of the Congress on Evolutionary Comutation, [15]. W. S. McCulloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biohysics, vol. 5, , [16]. R. P. Limann, An introduction to comuting with neural nets, IEEE ASSP Magazine, vol. 4,. 4-22, [17]. J. A. Anderson, Introduction to Neural Networks. MIT Press, [18]. S. Haykin, Neural Networks: A Comrehensive Foundation. Prentice-Hall, [19]. N. Ratha and R. Bolle, Eds., Automatic Fingerrint Recognition Systems. Sringer-Verlag, 2004.

7 2392 [20]. P. A. Hughes and A. D. P. Green, The use of neural network for fingerrint classification, in Proceedings of the 2 nd International Conference on Neural Networks, 1991, [21]. M. Kamijo, Classifying fingerrint images using neural networks: Deriving the classification state, in Proceedings of the 3 rd International Conference on Neural Networks, 1993, [22]. C. L. Wilson, G. T. Candela, and C. I. Watson, Neural network fingerrint classification, Journal of Artificial Neural Networks, vol. 1 (2), , [23]. H. V. Neto and D. L. Borges, Fingerrint classification with neural networks, in Proceedings of the 4 th Brazilian Symosium on Neural Networks, 1997, [24]. A Ceguerra and I. Korinska, Automatic fingerrint verification using neural networks, in Proceedings of the International Conference on Artificial Neural Networks, 2002, [25]. C. A. Janeway and P. Travers, Immunobiology: The Immune System in Health and Disease. Current Biology Ltd., 2 nd edition, [26]. S. Forrest, S. A. Hofmeyr, and A. Somayaji, Comuter Immunology, Communications of the ACM, vol. 40 (10), , [27]. A. Somayaji, S. A. Hofmeyr, and S. Forrest, Princiles of a comuter immune system, in New Security Paradigms Worksho, 1998, [28]. S. Forrest, S. A. Hofmeyr, A. Somayaji, and T. A. Longstaff, A sense of self for Unix rocesses, in Proceedings of the IEEE Symosium on Comuter Security and Privacy, [29]. J. Kim and P. Bentley, The human immune system and network intrusion detection, in Proceedings of the 7 th Euroean Conference on Intelligent Techniques and Soft Comuting, [30]. J. Kim and P. Bentley, Towards an artificial immune system for network intrusion detection: An investigation of dynamic clonal selection, in Proceedings of the Congress on Evolutionary Comutation, 2002,

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