Format Template Vol. 4, No. 2 | July – December 2021 SJET | P-ISSN: 2616-7069 |E-ISSN: 2617-3115 | Vol. 4 No. 2 July – December 2021 1 An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm Fozia Hanif 1, Urooj Waheed2, Samia Masood2, Rehan Shams3, Syed Inayatullah1 Abstract: With the advancement in technology, there has been a keen interest of researchers and industrial institutions in the use of Underwater Sensors Networks (UWSN s). This study is devoted to the secure communication between the underwater sensors networks which are nowadays most widely used for oceanographic abnormalities, and to track submarines that perform the surveillance and navigation. But UWSNs has its limita tions such as multipath, propagation delay, low bandwidth, and limited battery as compared to traditional WSNs that causes a low life in comparison with WSNs. Secure communication in UWSNs is more difficult due to the above-mentioned limitations which need ultralightweight components. There are many miscellaneous attacks due to which sensors can lose both data availability and integrity . In this study we have designed a computation and space efficient algorithm for secure underwater senor communication. The proposed algorithm will generate two-halves of the key through a genetic algorithm (GA). Genetic algorithm is an evolutionary technique, that produces very good results in many engineering problems. In cryptography, the most important part is the key generation procedure that plays a major role in data transfer. The secure key is the basic requirement of data encryption and by the help of GA, this study provides a complex key generation procedure for one part of the key. Genetic algorithm includes some basic steps such as initial population generation, crossover, and mutation. However, a new fitness function is introduced to enhance the efficiency of GA along with the different procedures of crossover and mutation. After that encryption algorithm is proposed for the secure communication between UWSNs and its performance is evaluated based on throughput, running time, space usage, and avalanche effect. Keywords: UWSNs; Security; Cryptography; Genetic Algorithm; Linear congruential procedure; pseudo-random number; avalanche affect. 1. Introduction Wireless sensor networks ha ve wide a pplica tions in fields such a s home, industry, environmenta l observa tion, milita ry monitoring, a nd disa ster relief [1]. Recent a dva nces in wireless communications and electronics ha ve ena bled the development of 1 Depa rtment of Ma thematics, University of Ka ra chi, Ka ra chi, Pa kista n . 2 Depa rtment of Computer Science, DHA Suffa University, Ka ra chi, Pa kista n. 3 Depa rtment of Telecommunica tion Engineering, Sir Syed University of Engineering and Technology, Ka ra chi, Pa kista n. Corresponding Author: ms_kha ns2011@hotmail.com sma ll low-cost sensor nodes tha t communicate over short dista nces. Wireless sensor networks a re comprised of severa l sensor nodes that communica te via wireless technology. In this pa per, we will propose a new wa y of symmetric cryptogra phy for sending the da ta between underwa ter sensor networks with mailto:ms_khans2011@hotmail.com F. Hanif (et al.), An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm (pp. 01 - 17) Sukkur IBA Journal of Emerging Technologies - SJET | Vol. 4 No. 2 July – December 2021 2 high security. In symmetric cryptogra phy, there is only one key which is responsible for both the encryption a nd decryption. Therefore, the key genera tion procedure should be very complex to genera te a strong key to stop any intruders from guessing or detecting the key. The key genera tion procedure beha ves as a ba ckbone of a ny cryptogra phic a lgorithm; therefore, this study is going to use GA for genera ting the key for the encryption procedure. Cryptogra phy ha s a lwa ys been a most importa nt requirement in the IoT a pplica tion but a s the mode of communication cha nges the requirement of security changes as well but limita tions in underwa ter sensors a re more a s compa red to other wa ys of communica tion [3-4]. Some fea tures of WSN a nd UWSNs a re the sa me due to the but ha rsh environment of UWSNs there a re more constra ined in UWSNs a s compa red to WSN such a s unrelia ble communica tion cha nnels, dyna mic networks topology, insecure environment, a nd vulnera bility [5]. The proposed a lgorithm genera tes its key which is of 128 bits in two steps: the first ha lf of the key will be ca lcula ted by the a nchor node through Genetic Algorithm (GA) procedure a nd the rest of pa rt of the key is ca lcula ted by the sensor node by using some other procedure a nd merger of these two pa rts will be the fina l key [6, 7]. To a void the pa ssive a tta ck here the da ta fra me will be sent through some a uthentica tion code to a void the a tta ck a nd a fter receiving the fra me sensor will simula te the code by itself to ma tch with the incoming code a fter this ma tching of a uthentication code the data fra me can open by receiving sensor otherwise it will disca rd the da ta fra me. The proposed resea rch scheme will ca lcula te the two pa rts of the key sepa ra tely, a uthentication codes, a nd then it performs the process of encryption a nd decryption. The novelty of this study is tha t GA ha s never been used in da ta communica tion of UWSNs and our result session will prove tha t how GA will give more ra ndomness to the key genera tion procedure a nd this complete lightweight process will not only enha nce the security but a lso provides low computa tional complexity [8-9]. This pa per is orga nizing a s follows: After the introduction section 1.1 gives the litera ture review, section 2 indica tes the security issues in underwa ter sensors communica tion, section 3 discusses Genetic a lgorithm a long with a brief discussion of its steps, a fter tha t this pa per gives key genera tion procedure through GA with deta ils steps implementa tion then section 4 gives the ca lcula tion of a uthentica tion code a nd final key forma tion, section 5 shows the encryption for underwa ter communication and decryption procedure a lso. Section 5 indica tes a n a na lysis of the result by using different pa ra meters and shows the security a na lysis a nd in the end, we ha ve the conclusion a nd references. 2. Literature Review Cryptogra phy ha s been a ma jor requirement for ma ny yea rs for a ny type of communica tion system, but the cryptogra phic a lgorithm is dependent upon the environment through which its communica tion occurs. Ma ny resea rchers ha ve ma de their efforts to perform underwa ter communication but due to the limita tions of the underwa ter environment, it is not so ea sy to perform underwa ter communica tion smoothly [10,11]. Severa l GA-ba sed a lgorithms ha ve been ma de for secure cryptogra phy [12,13], a lso ma ny resea rchers ha ve proved tha t the performa nce of GA produced better results [14] but for underwa ter sensor communica tion, GA ha s never been used before. Soniya Goya t in [15] sa ys tha t if the quality of the ra ndom numbers produced by the method is good then the key genera tion is a lwa ys better. Ultra -Lightweight cryptogra phy ha s been presented for underwa ter sensor networks tha t repla ce the S-box with 8 round itera tion block cipher a lgorithms [16]. The effort of modifica tion of RC6 ha s a lso been ma de by [17]. Due to the compu ta tional complexity symmetric cryptogra phy gives better results in underwa ter sensors as compa red to a symmetric cryptogra phic a lgorithm [18, 19]. F. Hanif (et al.), An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm (pp. 01 - 17) Sukkur IBA Journal of Emerging Technologies - SJET | Vol. 4 No. 2 July – December 2021 3 Another improvisa tion for secure communica tion in underwa ter sensors wa s presented by [20], which dea ls with XOR, left- right shift for the lower computational cost and genera tes the ra ndom key by pseudo -random number genera tor tha t reduces the spa ce stora ge. To reduce the computational burden [21] ha s a lso presented secure underwa ter communica tions ba sed on fully ha shed MQV. Gove Nitin Kuma r Ka ur in [22] uses the concept of bra in Mu wa ves, genetic a lgorithms, a nd pseudora ndom bina ry sequence. Fa iya z Ahma d ha s proposed a model tha t ma kes use of GA to genera te Pseudo-ra ndom numbers [23]. The litera ture review shows tha t a lthough a lot of efforts ha ve been ma de to improve the security of UWSNs through different encryption a lgorithms, most of them a re both spa ce a nd complexity expensive. The need to design a scheme tha t utilizes the minimal spa ce a nd computational ca pa cities of the underwa ter sensors while providing a completely secure a nd efficient communica tion still exists. The proposed model a ddresses a ll these issues a nd provides a secure wa y of communication using minimal sensor resources. 3. Security Issues in Underwater Sensors Communication Ma ny a pplica tions a re a ssocia ted with underwa ter environments such as surveilla nce, ocea n monitoring, a nd disa ster mitiga tion to mea sure the level of the sea due to the melting process of the ice sheet. All this ca n be possible due to ra ndomly pla ced underwa ter sensors tha t collects some important hydrologic da ta for exa mple pressure, tempera ture, a nd sa linity. The most important ta sk which is performed by underwa ter sensors is to sense the da ta a nd pass it to the releva nt ba se sta tion, but security threa ts a re the ma jor issue while tra nsferring the da ta [24-26]. There a re ma ny constra ints during underwa ter communica tion. UNSNs ca n be ea sily a ffected by va rious a tta cks and ma licious threa ts, these a tta cks ca n be either a ctive or pa ssive [27]. A pa ssive a tta ck is an a ttempt by miscella neous nodes to obta in the tra nsmitted da ta without cha nging the opera tion tha t’s why it is very difficult to detect. Wherea s a ttra ctive a ttacks a re ea sy to indica te, a nd it tries to delete, a lter distra ct the tra nsmitted da ta in the network. The a ctive a tta ck mostly a ttempts by externa l nodes which do not belong to the networks. The main fea ture of security in UWSNs a re key ma na gement, intrusion ma na gement, trust issues, secure loca liza tion, secure synchroniza tion, a nd routing security [28, 29]. To a chieve the security requirements a nd setup or mecha nism should be proposed tha t protect UWSNs from these a bove-mentioned treats [30]. The ma in goa ls of cryptogra phy a re repudia tion, integrity, confidentia lity, and a uthentication. The encryption schema should sa tisfy cha llenges of underwa ter such tha t, it should be a da ptable for underwa ter tra nsmission, lower computa tion with less overhea d, cost, a nd energy-efficient and ensure high security. The ma in fea tures of any encryption a lgorithm of UWSNs a re to provide integrity a nd confidentia lity in between nodes using less spa ce a nd high security with lower computations [31,32]. In this study, we develop a n a lgorithm that not only genera tes the key using the Genetic Algorithm but a lso provides a n encryption scheme for secure communica tion that proves to be efficient in terms of spa ce usa ge, running processing time, a nd the a va lanche effect. 3.1. System Architecture The underwa ter environment consists of va rious sensor nodes tha t ca n be a ble to communica te with ea ch other. The communica tion between underwa ter sensor nodes is done with the help of a sink node and a ba se sta tion which route the da ta from one sensor to a nother. For the proposed a rchitecture it is a ssumed tha t a ll sensors can sense, communicate, a nd able to ca lcula te. It is a well-known fa ct tha t routing the da ta be- tween underwa ter sensor nodes is not that much ea sier a s compa red to tra ditional Wire- less Sensor Networks (WSNs), beca use of the continuous movement of the sensors in the ocea n. The ma in purpose of underwa ter F. Hanif (et al.), An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm (pp. 01 - 17) Sukkur IBA Journal of Emerging Technologies - SJET | Vol. 4 No. 2 July – December 2021 4 sensors is to sense the da ta from the environment a nd rout them between the nodes. The figure 1 shows the UWSNs environment where different nodes such a s sink node, sensor node a nd the ba se sta tion. Fig. 1. System a rchitecture for underwa ter a coustic networks 4. Genetic Algorithm A genetic a lgorithm is a n evolutiona ry procedure tha t is ba sica lly used to optimize ma ny problems like shortest pa th, intrusion in WSN, ba ndwidth utiliza tion, a nd ma ny more [33, 34]. The rea son behind using the genetic a lgorithm in genera ting the key in UWSNs is, tha t cryptogra phy through GA provides the lightweight complexity which is the mea sure requirement within the UWSNs. GA a pproach is completely ra ndom which enha nces the cryptogra phic encryption a nd decryption, a lso the elitism Genetic a lgorithm sta rts with the ra ndom results ca lled chromosomes which ca n be genera ted through ma ny ra ndom procedures, is considered a s the results of the given problem [35]. Furtherly these ra ndomly genera ted results ca n be ma de more a ccurate by using different steps of genetic procedure which a re fitness mea sure, crossover, a nd muta tion. To get more a ccura te results through GA it is very importa nt to ha ve a strong fitness function tha t a pplies on initia l ra ndom genera tion to mea sure its fitness. The fitness function decides which chromosome ca n go for the process of crossover. In crossover two chromosomes will produce two more fitted chromosomes tha t ca n be tested a ga in, by using a fitness function. After getting better chromosomes from crossover, we a pply muta tion to a chieve globa l optima from loca l optima [36]. In the proposed a lgorithm we ha ve used the a bove-mentioned steps of genetic a lgorithm to genera te the ha lf pa rt of the key for symmetric cryptogra phy. These tra ditional steps of GA do ha ve ma ny va ria tions a ccording to a scena rio a nd environment [37]. We ha ve performed these steps in our own wa y by ma king the fitness function a ccording to the suita ble pa ra meters tha t a re rela ted to the conditions of the cryptogra phic a pproa ch. F. Hanif (et al.), An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm (pp. 01 - 17) Sukkur IBA Journal of Emerging Technologies - SJET | Vol. 4 No. 2 July – December 2021 5 4.1. Key Generation Procedure The cryptogra phic a lgorithm sta rts with the process of key genera tion; here we a re genera ting the ha lf pa rt of 64 bits key with the help of a genetic a lgorithm (GA) which is an evolutiona ry-based procedure. The rea son behind choosing GA for the key genera tion is, tha t it is completely a ra ndom procedure which ma kes key guessing very difficult, a nd to make this even more difficult the rema ining ha lf part of 64 bits will be genera ted through some other procedure a nd combina tion of both pa rts will be used for the a pplica tion of encryption and decryption [38]. 4.2. Steps of Proposed Genetic Algorithm The genetic a lgorithm is ma inly consistin g of some tra ditiona l steps: initia l ra ndom popula tion genera tion, crossover, fitness function, a nd muta tion. Although the procedure to perform these opera tions a re different in ea ch genetic a lgorithm but the ba sic steps a re the sa me in a ll GA. In the next section, we expla in the performa nce of each step of GA in deta il. 4.2.1. Initial Random Population Generation: As defined ea rlier tha t we a re genera ting the 128 bits key for the proposed cryptogra phic a lgorithm a nd 64 bits key will be genera ted through GA which mea ns we need to genera te 64 bits ra ndom numbers a s a n initia l popula tion a lso ca lled chromosomes in the genetic field. The process for genera ting the 64 bits bina ry ra ndom numbers is ba sed on linea r congrega tiona l procedure [39]. The deta il of this procedure will be given in section 2. 4.2.2. Crossover Procedure: After performing step one, a ll the newly genera ted chromosomes will go under the presses of crossover with the help of pseudora ndom number genera tor for 64 bits which will be discussed in 2.1, the resulta nt number obta ined from this procedure will decide a bout the crossover point. Crossover is of severa l types one point, two points, three points, a nd ra ndom point therefore the resulta nt number will decide the ra ndom point for the crossover opera tion a nd is given by figure 2 in which the bits a fter the selected point will be excha nged by both pa rent chromosomes to get two new resulta nt species. Fig. 2. Ra ndomly selected one Point Cross Over a nd excha nging the bits a fter 6 ra ndom point. 4.2.3. Fitness Function: The fitness of a ll newly genera ted chromosomes will be checked by using the fitness function. This procedure ca n reduce the number of popula tions by the surviva l of fitness which mea ns, only those species will exist which ha ve the best fitness a mongst a ll. The proposed Fitness function for the proposed a lgorithm is given by Eq. (1), 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 𝐹𝑢𝑛𝑐𝑡𝑖𝑜 𝑛 = 1 − 𝑆𝐸𝐶 𝐺𝑎𝑝 𝑉𝑎𝑙𝑢𝑒 (1) where, SEC = Shannon entropy of chromosome In informa tion theory, entropy is a mea sure of the uncerta inty in a ra ndom va ria ble. About this, the term Sha nnon entropy usua lly refers, to which qua ntifies the expected va lue of the informa tion conta ined in a messa ge (in cla ssica l informa tics it is mea sured in bits). Sha nnon entropy a llowing to estima te the a vera ge minimum number of bits F. Hanif (et al.), An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm (pp. 01 - 17) Sukkur IBA Journal of Emerging Technologies - SJET | Vol. 4 No. 2 July – December 2021 6 needed to encode a string of symbols ba sed on the a lpha bet size a nd the frequency of the symbols ca n be ca lcula ted by using the followin g formula , 𝐻 (𝑋) = − ∑ 𝑝(𝑥) log𝑏 𝑝(𝑥 𝑖 ) 𝑛 𝑖 =1 (2) In Eq. (2) p(xi ) is the lower proba bility, i.e. p(xi )→0, the higher the uncerta inty or the surprise [40]. Simila rly, the Ga p test is performed to ca lcula te the ga p between two repea ting numbers [41]. The ga p test is used to determine the implica tion of the interva l between recurrences of the sa me digit. If the va lue of the a bove fitness function is close to 1 then it will be considered a s the fitted va lue a nd the threshold for the proposed a lgorithm is more tha n 89%. The most fitted va lue will be recorded a s the best. 4.2.4. Mutation: The process of muta tion is helpful to a chieve globa l optima . All the chromosomes which a re grea ter tha n the threshold va lue in the la st procedure will go under the process of muta tion by a ga in using the ra ndom point muta tion procedure. The pseudora ndom number will genera te the ra ndom number a nd the bina ry bit will be flip of a ccording to the resulta nt ra ndom number to genera te a new chromosome a s given by figure 3. After a pplying muta tion on ea ch chromosome, we a ga in ca lcula te the fitness va lue of ea ch resulta nt by using (1) a nd select the best one a mong them if the ca lcula ted fitness va lue a fter muta tion is less tha n the previously recorded va lue then this process will be repea ted for 50 rounds till, we ha ve the more fitted va lue then the recorded one. If still we ha ve the chromosomes whose va lue is less tha n the previously recorded va lue, then the recorded va lue is considered a s fina l to become the better ha lf of the key. If the ca lcula ted fitness a fter muta tion is grea ter tha n the recorded va lues, then this va lue is considered a s the first ha lf of the key. The whole procedure to genera te the first ha lf of the key ca n be seen in the flow cha rt of figure 4. Fig. 3. Ra ndom Point Muta tion 4.3. Linear Congruential Procedure for 64 Bits Binary Number The A 64-bit linea r congruentia l genera tor (LCG) is defined by the following recursive formula , 𝑋𝑛 ≡ 𝑎𝑋𝑛 − 1(mod 𝑚), 𝑛 ≥ 1 (3) Where m is the prime modulus, multiplier a a nd seed X0 a re between 1 a nd (m -1) for a 64-bit computer in Eq. (3). The first bit of a signed integer is the sign bit, so the la rgest modulus presenta ble a s a n ordina ry integer is 263−1 for a 64-bit ma chine. Three prerequisites for a n idea l LCG a re full period, ra n domness, a nd efficiency [42, 43]. The ma ximal period of a n LCG is m − 1, ca lled a full period LCG. An LCG is rela tively ea sy to implement and rea sona bly fast. To genera te a random number, it is importa nt to ha ve two pa ra meters of an LCG: multiplier a nd modulus. Here we consider the 64-bit LCGs with prime modulus. Three forms of prime modulus a re useful: Mersenne prime modulus, Sophie–Germa in prime modulus, a nd la rgest prime modulus [44]. The distribution of Mersenne primes is spa rse, so we ca n consider the la rgest Mersenne number 261 − 1, denoted a s MP. There a re infinitely ma ny Sophie–Germa in primes. The la rgest Sophie–Germa in prime 263 − 4569 is chosen a nd is denoted a s SG. The la rgest prime modulus but not Mersenne prime a nd Sophie–Germa in prime ones sma ller than 263 is 263 – 25, denoted a s LP. For a 64 -bit LCG, we pla ce the AF or DF restriction on the multiplier. Since the number of multipliers is a stronomical, a n exhaustive sea rch a ppears to F. Hanif (et al.), An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm (pp. 01 - 17) Sukkur IBA Journal of Emerging Technologies - SJET | Vol. 4 No. 2 July – December 2021 7 be impra ctica l. For porta bility a nd correctness, two types of restriction on multiplier a re distinguished: a pproxima te fa ctoring (AF) multiplier a nd double-precision floa ting-point (DF) multiplier [45]. Fig. 4. The complete sequence of the proposed GA 4.4. Pseudo-Random Number A Pseudo-ra ndom number is a deterministica lly genera ted numbers that a ppea r to be ra ndom. To genera te these ra ndom numbers of va rious a rithmetic a pproa ches a re used, on computers in the pa st thirty yea rs or so. These a pproa ches a re usua lly recurrence rela tions a nd new numbers a re genera ted from the ea rlier one by a pplying some simple scra mbling opera tion. The most used method which is fa st a s well (or genera tor) is the so -ca lled multiplica tive congruentia l genera tor (sometimes a lso ca lled the power residue genera tor). It consists of computing Xi+1 = Xi a (mod m), where Xi is a pseudo-ra ndom number, Xi+1 is the next pseudo-ra ndom number, a is a constant multiplier a nd, modulo m mea ns tha t the number Xi*a is divided by m repea tedly till the rema inder is less tha n m which is 64 in this ca se. The rema inder is then set equa l to the next number Xi+1. The process is sta rted with a n initia l va lue Xo ca lled seed [46]. In the proposed a lgorithm we perform the one-point crossover tha t performs a ccording to the ra ndom number genera ted by pseudo -random number a nd bit of tha t number ma rk as a crossover point a nd performs the one-point crossover a s given by the figure1. 4.4.1. Summary of the 1st part of 64 bits key generation process: • Initia lly genera te the ra ndom number of 64 bits by using the linea r congruentia l procedure. • First, genera te the pseudo-ra ndom number a nd obta ined a ra ndom number from 0 to 63 a s a crossover point, a nd perform the crossover opera tion. • Apply the fitness test on ea ch genera ted number obta ined from step 1 with the F. Hanif (et al.), An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm (pp. 01 - 17) Sukkur IBA Journal of Emerging Technologies - SJET | Vol. 4 No. 2 July – December 2021 8 help of eq (1) a nd stored the best va lue, which is grea ter or equa l to 0.89. • Genera te the pseudo-ra ndom number and obta ined a ra ndom number from 0 to 63 a s a m uta tion point a nd perform a muta tion procedure. • Aga in, ca lcula te the fitness va lue a nd compa re the best va lue with the stored va lue. If the stored va lue is less tha n the ca lcula ted va lue then stops, otherwise perform the a bove procedure until 50 rounds. If the ca lcula ted va lue is still less tha n the stored va lues, then fina lly stored va lues a re supposed to be the fina l 64 bits first pa rt of the key. 5. GENETIC ALGORITHM As discussed ea rlier tha t in UWSNs data will be sent through some fra mes a nd each fra me ha s different fields for different types of da ta informa tion. To a void a tta cks tha t may come in the form of a da ta fra me, an a uthentication code (AC) will be ca lcula ted by ea ch sensor to receive a ny da ta fra me. The sensor will open the incoming da ta frame after the verifica tion of AC, otherwise, it will be disca rded. This a uthentication code is going to be used in the ca lcula tion of the remaining part of the key. The steps of genera ting the a uthentication code a re a s follows, 1. Ta ke the numeric pa rt of the sensor number a nd ma ke it squa re ta ke the mid-va lue 2. a nd the successive va lue a nd convert it into 32 bits bina ry va lues. 3. Now consider the a lpha betic pa rt of sensor number a nd va lue and convert it into 32 bits bina ry form a nd ta ke the XOR between the va lue obta ined from step I a nd II. 4. After a pplying the XOR the resulta nt is the a uthentication code for the incoming da ta fra me. 5.1. Final Key Formation 1. Merge the a uthentication code a nd the fra me number (which is numeric) of the incoming fra me a nd convert it into 32 bits bina ry form a nd fina lly, we obta ined the 64 bits va lue. 2. A combina tion of the first ha lf of the key which wa s genera ted through GA a nd the other ha lf va lue obta ined from step 1, is considered a s the 128 bits key for the proposed cryptogra phic suit for data communica tion in UWSNs. 𝐹𝑖𝑛𝑎𝑙 𝐾𝑒𝑦 = 64 𝑏𝑖𝑡𝑠 𝑓𝑟𝑜𝑚 𝐺𝐴 + 𝐴𝐶 (𝐹𝑟𝑎𝑚 𝑒 𝑛𝑜 . ) (4) 5.2. Algorithms for The Encryption & Decryption: The step-by-step deta iled encryption a nd decryption a lgorithm s used in the proposed solution a re represented below: ALGORITHM FOR ENCRYPTION START Step 1: Brea k the input Text file into 128 bits. Step 2: Split it into four equa l pa rts of 32 bits. Step 3: P1=1st Pa rt a nd 3rd Pa rt. P2 = 2nd Pa rt a nd 4th Pa rt. Step 4: Split 128 bits key into four equa l pa rts of 32 bits. Step 5: K1 = 1st Pa rt a nd 3rd Pa rt a nd K2 = 2nd Pa rt a nd 4th Pa rt. ENCRYPTION OF P1 Step 6: Ta ke P1 Convert a ll cha ra cters of input pla intext into its ASCII. Step 7: Store a nd identify the minimum ASCII va lue. F. Hanif (et al.), An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm (pp. 01 - 17) Sukkur IBA Journal of Emerging Technologies - SJET | Vol. 4 No. 2 July – December 2021 9 Step 8: Perform the modulus opera tion on ea ch character value by using the minimum ASCII va lue. Step 9: Perform XOR with K 1 = d1 Step 10: Find ba se 64 va lue of K 1 Step 11: Pick 8 a lpha bets = B1 (16 cha ra cters), use it a s the first row of ma trix write 128 bits of d1 in the form of the column below ea ch a lpha bet. Step 12: Apply the shifting of column (store the a rra ngement of B1 a fter column shifting) = R 1 . Step 13: Aga in a pply the shifting of rows by writing B1 a s the first column a nd the set of new va lues of R 1to be fix a s a row corresponding to ea ch a lpha bet of key the column (store the a rra ngement of B1 a fter row shifting) = E1. Step 14: Apply [bit XOR [(mod K1+E1 ,64)]] convert it into For ASCII va lues. Step 15: Encrypted text of P1 . ENCRYPTION OF P2 Step 16: P1 ta kes the tra nspose of P1 XOR K2. Step 17: Applying left rota tion by 5. Step 18: Add K2 in the resulta nt. Step 19: Apply right rota tion by 3 Step 20: a dd key in the resulta nt. Step 21: Apply 2’s Compliment. Step 22: Convert it into ASCII va lues. Step 23: Encrypted text of P2. END ALGORITHM FOR DECRYPTION START DECRYPTION OF P1 Step 1: Ciphertext C1 Step 2: Apply XOR with . Step 3: Ca lcula te the reverse of mod 64 a nd subtra ct with K 1 . Step 4: By using the stored a rra ngement of B1 a fter shifting of rows, perform rea rra ngement of rows till getting the a ctua l a rra ngement of B1 (a ccording to ba se 64). Step 5: By using the stored va lue of B1 a fter column shifting, rea rra nge the columns till getting the a ctua l va lue of K 1 (a ccording to ba se 64). Step 6: Perform XOR with 64 bits key. Step 7: Perform the reverse mod opera tion a ccording to the stored ASCII minimum va lue to get the origina l text P1 . F. Hanif (et al.), An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm (pp. 01 - 17) Sukkur IBA Journal of Emerging Technologies - SJET | Vol. 4 No. 2 July – December 2021 10 DECRYPTION OF P2 Step 8: Ta ke the ASCII va lues equiva lent to ciphertext C 2. Step 9: Apply 2’complement. Step 10: Subtra ct K2. Step 11: Apply left rota tion by 3. Step 12: Subtra ct K2. Step 13: Apply right rota tion by 5. Step 14: Ta ke XOR with K2. Step 15: Obta ined origina l text P2. FINAL TEXT Step 16: Split 64 bits of P1 into two pa rts. Step 17: P1=1st Pa rt a nd 3rd Pa rt a nd P2 = 2nd Pa rt a nd 4th Pa rt. Step 18: Merge a ll pa rts a ccording to the sequence. Step 19: The resulta nt 128 bits is the origina l pla in text. END 6. Results and Analysis This section provides the results a nd a na lysis of the performa nce of the proposed a lgorithm. The eva lua tion ca n be done ba sed on the ma in fea tures of a ny existing cryptogra phic a lgorithm. The deta ils of our eva lua tion will show how the proposed a lgorithm ma inta ins its security while implemented. Fig. 5(a). Avera ge Running Time for Encryption procedure by different Algorithms Fig. 5(b). Avera ge Running Time for Decryption procedure by different Algorithms F. Hanif (et al.), An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm (pp. 01 - 17) Sukkur IBA Journal of Emerging Technologies - SJET | Vol. 4 No. 2 July – December 2021 11 Fig. 5(c). Avera ge Throughput for Encryption procedure by different Algorithms Fig. 5(d). Avera ge Throughput for Decryption procedure by different Algorithms 6.1. Equations Elitism Criteria in GA We ha ve genera ted our key through the genetic a lgorithm a nd while genera ting the ra ndom number for a key genera tion we have used the concept of elitism in our coding which enha nces the selection criteria for a ny genera ted chromosomes. It is importa nt to ma inta in a dequate selection pressure, as dema nded by the a pplica tion, to a void genetic drift. Elitism ca n increa se the selection pressure by preventing the loss of low “sa lience” genes of chromosomes due to deficient selection pressure; it improves the performa nce of optimality a nd convergence of GAs in ma ny ca ses. Elitism provides a means for reducing genetic drift by ensuring tha t the best chromosome(s) is a llowed to pa ss/copy their tra its to the next genera tion [47]. 6.2. System Environment The proposed a lgorithm wa s implemented in MATLAB a nd the compa rison ha s been eva lua ted a gainst some benchmark symmetric encryption a lgorithms like 3 DES, MARS, Rivest Cipher (RC6), Da ta Encryption Algorithm (DES), Below FISH, a nd AES in terms of running time, throughput for encryption a nd decryption a long with the a va la nche effect a nd spa ce usa ge [47]. The da ta which is used in our experiments a re rea l da ta that has been used between sensor communica tion a nd the proposed a lgorithm ha s been tested for different file size which is ra ndomly ta ken between some interva ls of [1K-1M], [1M-100M], [100M-500M], [500M-1 G] a nd [1G-5G]. The a im is to show tha t the performa nce of the proposed a lgorithm in terms of a ll the a bove-mentioned fea tures is better tha n the existing a lgorithms which a re supposed to be th e benchmark techniques. The indica ted results a re ba sed on a vera ge va lues beca use ea ch test wa s conducted severa l times. The cryptogra phic a lgorithm sta rts with the process of key genera tion. 6.3. Performance Analysis Here we discuss our implementa tion and results in terms of performa nce for security fea tures like throughput, processing time, and the a va la nche effect. Figure 5 (a ), 5(b) is showing the a verage running time of encryption a nd decryption procedure for the proposed a lgorithm together with other benchma rk procedures for different input file sizes a s mentioned a bove. The running time is ma inly the time ta ken by any F. Hanif (et al.), An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm (pp. 01 - 17) Sukkur IBA Journal of Emerging Technologies - SJET | Vol. 4 No. 2 July – December 2021 12 a lgorithm to encrypt/ decrypt a ny pla in text into ciphertext. According to the figure it can be seen tha t the proposed a lgorithm has a better performa nce in terms of running time for encryption a nd decryption. After this, we eva lua te our GA ba sed method for the throughput of the encryption/decryption procedure. Throughput in (bytes/sec) ca n be define by using the following formula given in (5), 𝑇ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡 = ∑ 𝐼𝑛𝑝𝑢𝑡 𝐹𝑖𝑙𝑒 𝑆𝑖𝑧𝑒 ∑ 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑜 𝑛 𝑇𝑖𝑚𝑒 (5) Figure 5(c), 5(d) showing the a vera ge throughput for the encryption a nd decryption for the proposed a lgorithm, a nd a s compa red to other techniques it ca n be ea sily seen that our a lgorithm ha s better throughput for ra ndom file sizes. 6.4. Energy consumption and network lifetime Ba la ncing the energy is not a n ea sy ta sk in underwa ter sensor networks. A ba la nced network is one in which the rema ining a mount of energy is the sa me in the end, which means tha t ea ch node does not die before others. Sensors ba la nce their energies by sha rin g the duties which need a n extra a mount of energy. In underwa ter sensor networks, the initia l energy of ea ch sensor node is the sa me, but in the proposed technique the energy consumption of ea ch sensor is a lmost identical a fter the tra nsmission process. Since in the provided a lgorithm the sensors a re in sleep mode before receiving a nd a fter tra nsmitting the da ta to the ba se sta tion in ea ch round. During the execution of the proposed method energy of ea ch sensor a fter 100 rounds is a lmost the sa me. But a fter 500 rounds of GA, it is evident tha t the energy of some sensors is a lmost the sa me, a nd some have fluctua ted a t the end of the network lifetime. From ta ble 1 the mea n a nd sta ndard devia tion of the rema ining energy of sensor nodes which mea ns tha t the energy va ria tion of a ll sensors is a lmost the sa me. Table 1: The mea n a nd sta ndard devia tion of the rema ining energy for three simula tion runs Rounds 100 300 500 Mea n 0.38 0.29 0.24 S. D 0.009 0.013 0.025 Fig. 6(a). Sensor’s energy level a fter 100 rounds. Fig. 6(b). Sensor’s energy level a fter 500 rounds. 6.5. Security Analysis This section will discuss the eva lua tion of our proposed a lgorithm on the ba sis of the security purpose which is known a s a va lanche effect. The a va la nche effect is mea suring the strength of the a lgorithm for ha cking and cra cking threa ts. Rea l-time threa ts such as brute force a tta cks ca n be mea sured by a va la nche effects, a nd it requires the number of bits tha t ha ve been cha nged during the process of encryption from pla in text to ciphertext. Ava la nche effect ca n be ca lculated by using the formula , F. Hanif (et al.), An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm (pp. 01 - 17) Sukkur IBA Journal of Emerging Technologies - SJET | Vol. 4 No. 2 July – December 2021 13 𝐴𝑣𝑎𝑙𝑎𝑛𝑐ℎ𝑒 𝑒𝑓𝑓𝑒𝑐𝑡 = 𝑁𝐹𝐵𝐶𝑇 𝑁𝐵𝐶𝑇 X 100% (6) where, NFBCT = No. of flipping bits in the ciphertext NBCT = No. of bits in the ciphertext According to figure 7 AES is the only a lgorithm tha t ca n ma na ge a high a va lanche effect a s compa red to other benchmark a lgorithms. But proposed the a lgorithm shows the highest a va la nche effect in compa rison to AES a nd off course rest of the other encryption techniques. Fig. 7. Ava la nche effect of different a lgorithms in terms of % Fig. 8. Spa ce usa ge of different a lgorithms (in bytes) 7. Security Evaluation As expla ined ea rlier, UWSNs possess a limited ba ttery with very low spa ce a nd lots of security issues. The results from the proposed eva lua tion ca n conclude tha t the presented a lgorithm produced the lowest running possessing time, highest throughput, and highest a va la nche effect, a nd this ma de our proposed a lgorithm fa st, secure, efficient, and relia ble. In this section, we a na lyze the security of the proposed a lgorithm a ga inst va rious a ttacks rela ted to underwa ter sensor networks. For providing secure cryptogra phic a lgorithms, it is importa nt to ta ke ca re of different kinds of a tta cks tha t might occur while tra nsferring the da ta between the nodes. 7.1. Plain text attack: Considered the most ba sic a tta ck on the cryptogra phic a lgorithms, this a tta ck a rises when an a tta cker tries to a tta ck both pla in text a nd ciphertext. This a tta ck works during the da ta tra nsmission for encryption a nd sna tches the chunks of pla in text. Since it is difficult to the get key, therefore, a tta ckers try to genera te the method of encryption with the help of some portion of the pla in text a nd ciphertext. La ter it is used for the decryption of ciphertext. In the proposed technique the pla in text is not tra nsmitted within the sensor nodes only cipher text is sent. So it is impossible tha t this a tta ck ca n occur be-ca use if the a tta cker ca n a ble to ca pture some portion of the ciphertext then it is impossible for the a tta cker to get the key a s the key is upda ted in every round a nd the cipher-text a lso cha nges in ea ch round. 7.2. Ciphertext Attack: A very common a tta ck ca lled ciphertext a tta ck in which a n a tta cker ca n only be a ble to a ccess some portion of ciphertexts. This a ttack could be very da ngerous if corresponding pla in text is extra cted or even more ha rmful if the key ca n be deduced. This a tta ck could ea sily occur if the ciphertext is sent through the network but, in the propose technique, the ciphertext is divided into four pa rts, a nd encryption is implemented piecewise on the pla in text, which ma kes it a lmost impossible for tha t this F. Hanif (et al.), An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm (pp. 01 - 17) Sukkur IBA Journal of Emerging Technologies - SJET | Vol. 4 No. 2 July – December 2021 14 a tta ck to oc-cur. Also, in the proposed technique the key does not directly send over the network. It will be ca lcula ted on the end of the da ta receiving senor by using some informa tion given by the network which is not complete. Therefore, without the informa tion of key, it is difficult for the ciphertext to be decrypted by using the proposed technique. 7.3. Related-key attack: This a tta ck can occur in a ny form of crypta na lysis in which the a tta cker tries to pick the opera tion of cipher by using different keys tha t a re initia lly not completely known to a ccept some ma thematical opera tions rela ted to key a re known. For exa mple, if the a tta cker got some informa tion tha t the la st 80 bits of the keys a re a lwa ys the sa me a s ha ving the informa tion a bout the a ctual bits. But in the proposed technique the key is not a lwa ys the sa me some ea ch text. As per the key genera tion procedure which is ca lcula ted on the ba sis of sensor number a nd ra ndomly genera ted ID a t ea ch round therefore it is not possible tha t this a tta ck will occur for the proposed technique. As the key is genera ted by using ra ndomly genera ted pa ra meters which ma ke this a tta ck impossible to occur by using this technique. 7.4. Man in the Middle Attack (MIM): The proposed technique is highly vulnera ble to this a tta ck, beca use of the two rea - sons, the proposed technique is ba sed on the ra ndomly genera ted pa ra meters. Secondly, the proposed technique genera tes the ha sh function for the a uthentica tion code tha t is why this a tta ck becomes the mea ningless for the proposed technique. Three people have involved in this a tta ck: the victim, the a tta cker, a nd the person to which the victim tries to communica te. This a ttack tries to a ccess the secret pa ra meter va lues, but due to the involvement of a uthentica tion code which will be verified by sender a nd receiver, that ma kes this a tta ck wea ker for the proposed technique. This a tta ck is possible if the a t-ta cker tries to hea r the conversa tion between the sender a nd receiver, but for the pro-posed a lgorithm if this ha ppened then the da ta will be decrypted by using the key. As defined ea rlier the key does not send over the network it will be ca lcula ted by the receiving sensor tha t is why ma n in the middle a tta ck is helpless a s it ca n’t get the key. 7.5. Hello Flood Attack: This a tta ck sends HELLO pa ckets in order to consume network resources. But in the proposed technique the receiving senor does not receive a ny pa ckets without an a uthentica tion code, therefore the proposed technique is immune to this a tta ck. 7.6. DoS Attack: This a tta ck utilizes the network ba ndwidth by sending a dvisory pa ckets which pre-vent the user from utilizing the services a nd resources. This a tta ck usually occurs on the cluster head of the network. The proposed methodology overcomes this a tta ck by sending the a cknowledgment messa ge by the ba se sta tion. 7.7. Compromise Cluster Head Attack : This a tta ck tries to get a ll the informa tion from the cluster nodes by ma king them believe tha t it is workin g a s a cluster hea d. The ma in ta sk of this a tta ck is to extra ct the ba sic informa tion from the da ta . In our proposed technique the cluster hea d sends the da ta from the nodes without decrypting it, a lso in the proposed technique encryption is de- pending on ma ny pa ra meters such as sensor number, a uthentica tion code, ra ndomly genera ted numbers a t ea ch F. Hanif (et al.), An Energy Efficient Crypto Suit for Secure Underwater Sensor Communication using Genetic Algorithm (pp. 01 - 17) Sukkur IBA Journal of Emerging Technologies - SJET | Vol. 4 No. 2 July – December 2021 15 round. To encrypt the da ta the a ttacker, must know the complete informa tion a bout a ll the opera tions a long with the secret bina ry string which is genera ted by the key genera tion procedure. Therefore, due to a ll a bove-mentioned fa c-tors, we ca n ea sily sa y tha t for the proposed technique this a tta ck is not possible to occur. 8. Conclusion This study ha s intended to provide the secure crypto ba se encryption a lgorithm (GA) for the communica tion between underwa ter sensors. The proposed a lgorithm ca n be a b le to genera te the first ha lf portion of the key by using a genetic a lgorithm tha t is ba sed on 32 bits, a fter tha t, it ca lcula tes the a uthentica tion code for the fina l key genera tion of 128 bits. In GA a fitness function ha s been introduced with the pa ra meters tha t helped in the identifica tion of the best chromosomes a mong the ra ndom popula tion. By the help of GA steps first pa rt of the key will be genera ted and the rema ining pa rt will be genera ted by some different procedure. Furthermore, there a re two sepa ra te procedures of encryption a lso, with the idea to ma ke encryption procedure trickier without involving complex steps to a void the computa tional complexities. Overa ll procedure ha s been designed in a way tha t ca n be a ble to a void different threa ts which a re very obvious in underwa ter communica tion. An a uthentica tion code has been used to protect the da ta from pa ssive a tta cks. The compa rison ha s been implemented with other benchmark symmetric encryption techniques to show the efficiency of the proposed cryptogra phic a lgorithm in terms of running processing time, throughput, a nd the a va la nche effect. The rea son behind using the GA technique is due to its ra ndomness. GA is a ra ndom procedure, a nd it ma kes key guessing a lmost impossible. The novelty of the proposed work is tha t GA ha s never been involved in underwa ter secure communica tion. This study ha s proved that with the help of GA a nd the present -ed encryption procedure we ca n efficiently be done the secure communica tion between underwa ter sensors a nd security ca n be made even better by the implementa tion of the proposed a lgorithm. 9. 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