161 A publcaton of VOL. 46, 15 CHEMICAL ENGINEERING TRANSACTIONS Guest Edtors: Peyu Ren, Yancang L, Hupng Song Copyrght 15, AIDIC Servz S.r.l., ISBN 978-88-9568-37-; ISSN 83-916 The Italan Assocaton of Chemcal Engneerng Onlne at www.adc.t/cet DOI: 1.333/CET154611 Applcaton of an Improved BP Neural Networ Model n Enterprse Networ Securty Forecastng Xun Chen *a, b, Lsheng Xu a, Meng Xu a a College of Geology and Envronment Central South Unversty, Changsha 4183, Chna; b Changsha Aeronautcal Vocatonal and Techncal College, Changsha 4114, Chna sy115baby@163.com In recent years, wth the rse of the global networ, the Internet technology as the core of large enterprse networ system s developng rapdly. It s wdely used n the feld of electronc commerce, nformaton servce, networ communcaton and other techncal means. At the same tme, the problem of networ securty has become ncreasngly promnent. Due to hstorcal and techncal reasons, the securty system of enterprse networ s stll very wea n Chna. The networ basc software and hardware s stll use a large number of foregn products, and the core of the networ securty technology cannot be fully mastered. So, the securty rss are obvous. Therefore, strengthenng the constructon of enterprse networ securty system and researchng the safe applcaton model has become the top prorty of our country's enterprse nformaton technology. Based on ths, we propose a smoothng method to mprove the ntal weghts and the ntal threshold, and use a test method to select the hdden layer node number of neural networ. So, we can mnmze the fttng error of the tranng. In ths paper, the computer networ securty data of an enterprse s selected, and all the ndees are scored by the eperts. The result of the scorng s the nput value of the mproved BP neural networ. Fnally, we use ths algorthm to predct the networ securty of a certan enterprse n the net three months. The score s.85,.88 and.91, whch s close to the actual value of networ securty. 1. Introducton In recent years, wth the rse of the global networ, the Internet technology as the core of large enterprse networ system s developng rapdly. It s wdely used n the feld of electronc commerce, nformaton servce, networ communcaton and other techncal means. At the same tme, the problem of networ securty has become ncreasngly promnent. Due to hstorcal and techncal reasons, the securty system of enterprse networ s stll very wea n Chna. The networ basc software and hardware s stll use a large number of foregn products, and the core of the networ securty technology can not be fully mastered. So, the securty rss are obvous. Therefore, strengthenng the constructon of enterprse networ securty system and researchng the safe applcaton model has become the top prorty of our country's enterprse nformaton technology. Networ securty rs predcton s an mportant component of networ securty awareness (Chna Internet Networ Informaton Center 1 and 13). At present, the most common methods of predcton are as follows. Grey theory method. In recent years, the applcaton of gray theory has been etended to many scentfc felds, such as envronment, clmate, health, medcal care, populaton and so on. In the area of networ securty, there are many research results ((Deng Julong (), Wang Cayn (13), Pu Tanyn (9), and Zheng Jelang (5)). The theory uses the sequences whch are generated by the orgnal sequence of the system to determne the best fttng curve, and t can effectvely deal wth the less data sample system. Tme seres method. Tme seres forecastng method reveals the rule of the phenomenon wth tme varaton, and ths rule s etended to the future, so as to realze the predcton of the phenomenon n the future (Yang Zhongjn (6), Guo Mngyue (9), Chang Tahua (1), and Zhang Jnghu (1)). Neural networ method. Neural networ s a nd of method to smulate human's cogntve process, whch s a nd of nonlnear dynamc system of nformaton dstrbuted storage and parallel processng. Its essence s a nd of Please cte ths artcle as: Chen X., Xu L.S., Xu M., 15, Applcaton of an mproved bp neural networ model n enterprse networ securty forecastng, Chemcal Engneerng Transactons, 46, 161-166 DOI:1.333/CET154611
16 nonlnear functon that represents the relatonshp between the nput value and the output value. Forecastng methods based on neural networs have many advantages, such as good nonlnear, dstrbuted and selforganzng learnng. It has good practcal value n mult varable forecastng and nonlnear forecastng. But the neural networ s a blac bo forecastng method, whch can only be used to ft the system's nput and output data. Therefore, the relatonshp between nput value and the output value s not clearly descrbed, and the results cannot be eplaned reasonably (Tang Chenghua (9), Xe La, Ca (13) Zhpng (8) and Xu Fuyong (5)). Based on ths, we propose a smoothng method to mprove the ntal weghts and the ntal threshold, and use a test method to select the hdden layer node number of neural networ. So, we can mnmze the fttng error of the tranng. In ths paper, the computer networ securty data of an enterprse s selected, and all the ndees are scored by the eperts. The result of the scorng s the nput value of the mproved BP neural networ. Fnally, we use ths algorthm to predct the networ securty of a certan enterprse n the net three months.. Neural networ model BP algorthm not only has the nput layer node, the output layer node, but also has one or more hdden layer nodes. Frstly, the nput sgnal s propagated forward to the hdden layer node. After the functon of the ectaton functon, the output sgnal of the hdden layer node s transmtted to the output layer node. Fnally,we get the output results. The S type functon s usually selected as the node's ectaton functon, whch s shown below; 1 f( ) 1 e Q (1) Here, Q s the Sgmod parameter, whch s prmarly responsble for the form of the ectaton functon. The learnng process of the algorthm s composed of forward propagaton and bacward propagaton. In the process of forward propagaton, the nput nformaton s processed by the hdden layer and the nformaton s transmtted to the output layer. Each layer of neurons only affects the state of the neurons n the net layer. If the output layer cannot get the epected output value, the algorthm s transferred to the process of the bac propagaton. In ths process, the error sgnal s returned along the orgnal path. By modfyng the weghts of each layer, the system error can be mnmzed. Set up any networ contanng n nodes, the characterstcs of each node are Sgmod type. For smplcty, the networ has only one output value whch s y. The output value of th node s O. The number of sample s N, ( 1,,, N) node net. For a node, the nput value s and the output value s O, and the nput of the j th node s: j y, the output value of th W O () We defne the error functon as: 1 E y y N ( ˆ ) (3) 1 Where, y ˆ s the actual output value of the networ. Defne Therefore, E y yˆ ( ), net E E E E O O Wj net Wj net and O f ( net ) net. (4) When j s the output node, O yˆ
163 E yˆ ' ( y y ) f ( net ) yˆ net ˆ (5) When j s not the output node, E E O E f ' ( net ) net O net O (6) E E net E E W O W W O net O net O net (7) m m mj m mj m m m m m m m Therefore, f ' ( net ) W m mj m E W j O m (8) 3. Improvement of BP networ The settng of the ntal weght and the threshold of the memory. One of the man problems of the BP neural networ model s the slow convergence speed and the length of the teraton tme. Through a large number of practcal applcatons, the ntal weghts and thresholds of BP neural networ can be randomly selected, the convergence speed of BP neural networ s greatly affected by the method. Some scholars put forward the correspondng ntal weghts and threshold selecton method, and they have acheved some results n the feld of ther research. On ths bass, ths artcle proposes a new method of the ntal smooth weght and threshold of memory. Methods are as follows: w1 ( ) Rnd( ) 1 ( ) Rnd( ) (9) w ( ) Rnd( ) ( ) Rnd( ) (1) w 1( ) w( ) w () 1( ) ( ) () 3,4,, N (11) Where, The ntal weghts for the th BP networ operaton s operaton s, the termnaton weghts for the 1 th BP networ operaton s threshold for the 1 th BP networ operaton s. 1 4. Smulaton eperment and result analyss w, the threshold for the th BP networ w 1, and the termnaton 4.1 Networ securty evaluaton nde system Networ and nformaton system s a comple system engneerng, whch ncludes the eternal factors and the nternal factors, and they are mutually restrcted. Therefore, we must have a standard, unfed, objectve crtera to measure networ securty. Accordng to the domestc and foregn networ securty evaluaton standard, and the basc requrements of the networ and nformaton system securty, we should fully consder the varous factors that affectng the securty of the networ, such as physcal securty factor, operaton safety factor, nformaton securty factors, system securty polcy and safety techncal measures. Therefore, we gve the networ securty evaluaton nde system. As shown n table 1:
164 Table 1: The networ securty evaluaton nde system Frst level nde Second level nde safety nde Varable Equpment safety X1 physcal securty Envronmental safety X Meda securty X3 Rs analyss X4 operaton safety Access control measures Audt measures X5 X6 Emergency technology X7 networ securty nformaton securty Informaton transmsson securty Defense Technology Data ntegrty X8 X9 X1 Data encrypton X11 Applcaton software X1 system securty polcy User dentty authentcaton X13 Data remote bacup X14 safety measures techncal Securty audt functon Ant hacng measures X15 X16 4. Data pre-processng of networ securty nde Table 1 reflects the securty of computer networs from dfferent angles. As the dmensons of the varous ndcators are dfferent, so we cannot mae a drect comparson. In order to mae the nde have comparablty, and to speed up the convergence rate of the neural networ, ths paper has carred on the normalzed processng to each nde: 1) for qualtatve ndcators: usng epert scorng method to determne ts data, and we have a normalzed treatment of varous ndcators. ) for quanttatve ndcators: the followng formula s used to normalze. ma mn mn Where, the normalzed values for the th ndcator s, the mnmum value of the th ndcator s the mamum value of the th ndcator s ma. (1) mn, and 4.3 Smulaton eperment In ths paper, the computer networ securty data of an enterprse s selected, and all the ndees are scored by the eperts. The result of the scorng s the nput value of the mproved BP neural networ. As the neural networ model of ths paper s a 16-X-1 model, we carry out the tranng of the sample accordng to prncple. The prncple s that the number of nodes n the hdden layer s 3/4 of the number of nodes n the nput layer. We try to set the number of nodes n the hdden layer to 11,1 and 13. From the results of tranng, t can be nown that the number of hdden layer nodes s X=1, and the system fttng resdual s the smallest.
165 Fgure 1: The number of hdden layer nodes s 11 n neural networ tranng Fgure : The number of hdden layer nodes s 1 n neural networ tranng Fgure 3: The number of hdden layer nodes s 13 n neural networ tranng It can be seen from the test results of fgure 1-3, the node number and the ntal value n ths paper can effectvely shorten the convergence perod, accelerate the tranng speed, and mae the fttng precson of the resdual error reach the hghest. Fnally, we use ths algorthm to predct the networ securty of a certan enterprse n the net three months, the score s.85,.88 and.91, whch s close to the actual value of networ securty.
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