Detection Of Distributed Denial Of Service Attacks

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(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 8, No. 8, 2017Detection of Distributed Denial of Service AttacksUsing Artificial Neural NetworksAbdullah AljumahCollege of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz University, KSAAbstract—Distributed Denial of Services (DDoS) is a ruthlessattack that targets a node or a medium with its false packets todecline the network performance and its resources. Neuralnetworks is a powerful tool to defend a network from this attackas in our proposed solution a mitigation process is invoked whenan attack is detected by the detection system using the knownpatters which separate the legitimate traffic from malicioustraffic that were given to artificial neural networks during itstraining process. In this research article, we have proposed aDDoS detection system using artificial neural networks that willflag (mark) malicious and genuine data traffic and will savenetwork from losing performance. We have compared andevaluated our proposed system on the basis of precision,sensitivity and accuracy with the existing models of the relatedwork.Keywords— Distributed Denial of Services (DDoS); ANN; IDSI. INTRODUCTIONThe modern network world suffer due to security andthreat vulnerabilities despite being from different origin ormanufacturer or for different purpose and on the ground level,it is truly difficult technically and economically not feasible asfar as both creating and maintaining such systems and toensure that both the network and the associated systems arenot susceptible to threats and attacks [1]. IDS is a specialsecurity tool that is being used by the network experts to keepthe network safe and secure from network attacks which canFig. 1.come from many different sources [2]. It has emerged as oneof the basic and powerful tool in order to deal with datasecurity and availability issues over the communicationnetworks.These attacks have a major influence of the networks andthe systems as they include network performance, datasecurity, loss of intellectual property [3] and a real liability forthe compromised notes or networks data and that is why needa powerful IDS? Fig. 1 illustrates the architecture of IDS. Thedata packets received from the internet is forwarded to theprocessing unit where the format of the data is changed inorder to make it compatible with the associated IDS andeventually the data packets are categorized as an attack ornormal [4]. The normal data packet re allowed to pass throughbut the attack data packets as identified as attack type and arekept in the attack table and the alarm is raised and the defenseprocedure is invoked [5].Large amounts of research have been conducted toimprove IDS using artificial neural networks. The researchproved that the network data traffic can be filtered andmodeled more efficiently using artificial neural networks.Using artificial neural network proved itself as moreadvantageous as it take a thorough conscientious, perfect andaccurate training, validation and top level testing phasesbefore it is applied to the networks to detect malicious dataand network attacks[6].Intrusion detection system.306 P a g

(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 8, No. 8, 2017II. ARTIFICIAL NEURAL NETWORKNeural network (also known as artificial neural network) isan information processing model that is based and inspiredfrom the human nervous system like the human brain does forhumans [7]. The most important characteristic feature of thismodel is its unique structure of the system that processes theinformation. It consists of numerous exceptionallyinterconnected processing nodes (neurons) that worksimultaneously to solve the specified problems [8]. Fig. 2shows the real mathematical form of a neural network neuron.Neural networks, like humans do, learn by examples. Neuralnetwork is configured for a particular application, such as dataclassification or recognizing patterns through a learningprocess [9]. The learning process in humans requires synapticconnections adjustments between the neurons and same is thecase with neural networks as well.Fig. 3.Fig. 2.Block diagram of an artificial neuron.With the extra ordinary character of deriving meaningfrom complex and indefinite data, neural networks can be usedto recognize and detect the patterns that are exceptionallycomplicated to be even observed or detected by humans andeven by computer techniques [10]. After training process, aneural network can be treated as an expert one in the class orgroup information that has been given for analysis. This expertsystem can answer ―what if‖ questions. There are otheradvantages of neural networks which include Adaptivelearning, Self organization, Real time operation, redundantinformation coding, etc. [11]. Neural networks learn byexamples and cannot be programmed to accomplish anyspecific job [12]. These examples need to be selected correctlyand delicately otherwise the precious time of the system willget wasted or the network might work improperly.Architecture of the neural network.Neural network mainly have three categories of layerswhich include Input layer, Hidden Layers and output layers.Fig. 3 illustrates the basic architecture of the neural network.This is the most common architecture of neural networks. Theinput nodes are input nodes and rest of the nodes are activenodes. The input layer nodes are connected to hidden layernodes and the hidden layer nodes are connected to outputunits. The action of this neural network is decided by theweight that is put on hidden layer nodes. The main job of theinput nodes is to represent the raw information that is receivedby the network. This input and the weight on the connectionsbetween hidden nodes and input nodes decide the action of thehidden layer units. This action or activity of the hidden layernodes and the weight between output layer nodes and thehidden layer nodes decide the performance and the behaviorof the output layer nodes.III. DDOSDenial of Service (DoS) attacks is a deliberate, malicious,criminal attempt to deprive legitimate network users fromusing their network resources. DoS affect service providers inmany aspects, most notably crippling availability of servicesprovided by them. DDoS themselves are not powerful enoughto bring down any web service in present computationalresources scenario. A more sophisticated scalable anddistributed attack evolved out of DoS is DDoS or Distributed307 P a g

(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 8, No. 8, 2017Denial of Services. It was first reported by Computer IncidentAdvisory Capability (CIAC) in somewhere around summersof 1999 [20]. Since then almost all DoS attacks weresomehow of distributed characteristics.behalf of him, known as Botnets or simply Bots or Zombies(Fig. 4(a)). Attacker may have gain access to these computersby any means of infection [19].A more recent trend is to magnify the amplitude of attackso as overwhelm victim even with enormous amount ofresources, a way to get it is ―DNS Amplification‖ (Fig. 4(b)).To sabotage any website by DDoS there are broadly twomethods, first and primitive one is to send packet withmorphed packed to confuse routing protocols also known asvulnerability attack [21]. Second and somewhat advance andmore sophisticated mechanism involve attempts of either oneor both of following (a) at network/transport layer attackflooding web server to exhaust bandwidth, router processingcapability and hence paralyzing connectivity to the legitimateuser [21]; (b) attack at application layer for deprivinglegitimate user with services by consuming server resources ofprovider website, e.g. sockets, memory, disk I/O, etc. [22].A. Role of Amplifiers/ReflectorsDNS amplification is a phenomenon where a small queryis amplified several folds as this amplified query with muchlarger payload than original one is then directed to victimserver. Amplification of usually 70 folds is achieved easily[18].DNS amplification a kind of reflective attack wherespoofed IP of victim server is used for DNS query, in returnvictim server is flooded with large number of UDP packets.Usually attacker seldom acts directly, rather a series of precompromised nodes are chosen by him to launch attack on(a)308 P a g

(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 8, No. 8, 2017(b)Fig. 4.(a) Direct DDoS attack; (b) Reflexive DDoS attack.IV. CONSEQUENCES OF DDOSEffects of DDoS attacks on business installation areimmediately reflected as Revenue Losses, with loss rate goingas high as 300K/hour for service outage hours [13]. Withadvent of time, cost to mitigate DDoS attacks kept ever rising,in a survey by Forrester Research survey of Canadiandecision-makers, DDoS attacks were declared most expensivewith average cost associated with a typical DDoS reachingwell beyond 100,000 per security incident [14].Besides being attacked is direct blow onto marketreputation of any e-commerce website. In their findings, BellCanada mentioned, 67% corporate say DDoS cause negativeimpact to customers, 56% say it critically impacts the brandname while 55% are concerned with negative effects oncustomer relations [15].Al though, DDoS attacks are not meant for theft, butrecently there has been shift in DDoS activities with stealingof user data, customers information, intellectual properties,etc. while enterprise resources were busy in mitigation ofDDoS and related effects, known as Smoke-Screen effect. Inthe transitional time when IT experts of target organization arebusy to bring back critical application. On line, attacker try tobypass security checks and get away with crucial businessdata, e.g. during DDoS attack on Carphone Warehouse, whileinternal team was busy with DDoS mitigation, hackers stolepersonal and banking details of 2.4 million people [16]. Intheir security report, Kaspersky Lab has published, 26% ofDDoS attacks end up with Data Loss [17].309 P a g

(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 8, No. 8, 2017V. RELATED WORKWith the use of ANN for the detection of DDOS attacks byJie-Hao and Ming [24] in which the results where comparedwith output and the decision tree, ANN, Bayesian and entropy.The researchers recognize the user demands for any particularresource on the involved system and their control data.Moreover, the samples of such identifications were sent to theattack detection system for any vulnerabilities.Liu, Gu and et al. established a system called LearningVector Quantization (LVQ) neural networks to identifyattacks [25]. The technique is supervision type of quantization,which can be used for further procedures such as patternrecognition, data compression and multi-class classifications.Furthermore, the inputs where supplied to neural networks asdata sets in the form of numerical calculations.Akilandeswari and Shalinie [26], proposed a ProbabilisticNeural Network Based Attack Traffic taxonomy in order todetect various DDOS attacks. In contrast, the authors mainlyfocused on distinguished between Flash Crowd Event fromDenial of Service Attacks. Moreover, their work also involvedthe use of Bayes decision rule for Bayes interference coupledwith Radial Basis Function Neural Network (RBFNN) forprecisely classifying the DDOS attack traffic and thelegitimate traffic.Siaterlis & Maglaris [27] came up with a procedure ofsingle network characteristics to mitigate the attacks. With theuse of data fusion algorithm with Multi-layer Perceptron(MLP) in which the inputs where initialized from various nonactive measurement which were available on the network, andhence the data combined with the traffic which were generatedfrom the experimenters itself.Joshi, Gupta and Misra [28] used a design consideration ofneural network in order to detect zombie systems which werefueling the DDOS attacks. The main motive to their initiativewas to figure out the connection between the zombie computerand sample entropy. The entire process workflow compriseson the predictions with the help of feed-forward neuralnetwork. Another objective for their research is to utilize thecurrent infra for detecting and mitigating such attacks.Badishi, Yachin & Keidar [29] used an approach ofcryptography and authentication to defend DDOS attacks fromaffecting network resources and services. A very closeapproach proposed by Shi, Stoica and Anderson [30],However, DDOS attacks are detected using a differenttechnique called puzzling mechanism.Hwang and Ku [31] proposed a distributed technique tomitigate DDOS attacks. The mitigation system calledDistributed Change-point Detection (DCD), which primarilyreduces the risk of such attacks. The researcher suggests usingnon-parametric CUSUM (Cumulative Sum) algorithm toidentify any major or minor variations in the network traffic.The team also focused on the initial source of the attack fordetection.A group of author [32]-[34] proposed a system of packetmarking and entropy in which each packet is marked on everyrouter involved in communication in order to track the sourceof the packet. However, a number of techniques proposed bysome authors used ANN or infrastructure to defend againstDDOS attacks, where as a couple of them identified the sourceof the attack. In contrast, none of them describes any unknownor zero day attacks labeled as high or low risk attacks. Hence,our main objective is to detect and mitigate unknown DDOSattacks and differentiate our proposed solution from theauthors of [25]-[28].VI. CONCEPTUAL FRAMEWORKIf deployed properly the DDoS detectors can minimize thestrength of an attack. The DDoS detectors prevent themalicious packet from reaching the target after detection byanalyzing the network for abnormal behavior or theabnormalities in the network. It is important for DDoSdetectors to allow legitimate packets to pass through and reachthe destination. So, it is extremely important for the detectionsystem to be explicitly precise and checked against everypossible and imaginable patterns and cases. Most commonlyTCP, ICMP and UDP are used because of ease in practicality,implementation and documentation. The yearly report ofProplexic explained that these protocols are used by mostattackers to launch most of the DDoS attacks. Since we haveused ANN (artificial neural networks) for our detectionmechanism where it’s precision predominantly depend on thequality of the algorithm training and the associated datasetsand patterns used. The patters include packet source address,sequence numbers and ID along with port numbers of sourceand destination, all these entities of packets are used fortraining the ANN. Based on our analysis and experimentalverification, maximum number of zombies installed to opposethe operating system libraries in order to generate genuinepackets that the installed zombie agents use their integratedbuilt-in libraries. This is just to help the attackers inmanipulation and forging the message throughout the attack.Hence, it is easily possible to study the main properties ofauthentic packets that are created by authentic applicationsand can be easily compared with fake packets that are createdby the attack tools and feed them as input patterns to train theartificial neural networks. We launched difference kinds ofDDoS attacks at distinct levels in order to select the differentpatterns for input to the artificial neural networks by creatingan elite network infrastructure in unanimous and solitaryenvironments. We studied the results very carefully andcompared them with authentic traffic in order to verify thecharacteristic patterns that distinguish authentic traffic fromthe attack traffic. This segment of the process demandedthorough comprehension of how distinctive protocolinterchange data or do the communications. The java neuralnetwork simulator accepts the authentic and malicious patternin a specified format because the data sets are designed andassembled to accommodate both types of patterns. However79% of the datasets are used in training the algo and 21% areused to ratify the process of learning. The input entities arenormalized in order to increase the capability in delicateapplications like the one we have where exact detection isextremely important otherwise if applied directly will lead tovanquish the impact of smaller values because normalizationhas positive effect on artificial neural network's training andperformers.310 P a g

(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 8, No. 8, 2017A normal artificial neural network is made up of threelayers i.e. input layer, an output layer, and a hidden layer, thedatasets and patterns are given through input nodes for thelearning process. These input attributes indicate the mainpattern that distinguishes the genuine traffic from the attacktraffic. Then we selected three different structures oftopological artificial neural networks having three layers eachi.e., input layer, output layer and hidden layer. But everytopological artificial neural network structure will havedifferent number of nodes as shown in Table 1.TABLE I.NO. OF INPUT AND OUTPUT NODES FOR ICMP, TCP AND UDPTopological ANNstructureNo. of input nodesNo. of hidden nodesICMP34TCP54UDP43Our experiment shows 98.5% accuracy in selectedtopological structures when sigmoid invoking function ispaired with Back Propagation as shown in Table 2.TCP topological structure's input layers as shown in Fig. 4is composed of five nodes with TCP sequence, source IPaddress, source port number, destination port number andflags.However the computation process deals with hidden nodesregarding input and output nodes. A single node is used asoutput layer to represent 1 or 0 for attack and normal traffic,TABLE II.respectively. Fig. 5 displays the TCP topological artificialneural network structure, Fig. 6 displays ICMP topologicalartificial neural network structure and Fig. 7 displays the UDPtopological artificial neural network structure. Selecting anappropriate learning algo, invoking function and number ofhidden nodes where chosen on the early experiments wherethe accurate results were provided by Back Propagation andSigmoid. Bidirectional associative memory, Elliot, Sigmoidand Softmax are used as functions while the comparison wasbetween Quick-Prop, Back Propagation, BidirectionalAssociative Memory, Back Prop Weight Decay, Back Propthru time (16, 17, 18).ICMP topological structure is shown in Fig. 5 where ICMPID and sequence number, source IP address are the inputnodes.COLLECTIVE RESULTS OF LEARNING ALGO, INVOKING FUNCTION, HLProtocolLearningAlgorithmInvoking FunctionNo. of Hidden NodesDetection Accuracy andCPU UsageBest ResultsTCPBack PropagationSigmoid, Elliot, BAM,SoftmaxOne or more HiddenNodes98.6% and 66%-CPUUtilizationBest Recorded With 4 hidden nodesusing Sigmoid.UDPBack PropagationSigmoid, Elliot, BAM,SoftmaxOne or more HiddenNodes98.6% and 69%-CPUUtilizationBest Recorded With 3 hidden nodesusing Sigmoid.ICMPBack PropagationSigmoid, Elliot, BAM,SoftmaxOne or more HiddenNodes98.5% and 70%-CPUUtilizationBest Recorded With 4 hidden nodesusing Sigmoid.311 P a g

(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 8, No. 8, 2017Fig. 5.Fig. 6.ANN TCP topological structure.ANN ICMP topological structure.312 P a g

(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 8, No. 8, 2017Fig. 7.ANN UDP topological structure.UDP topological structure is shown in Fig. 6 where UDPsource port, UDP destination port, Packet size and source IPaddress are the input nodes.The supervised Back Propagation uses the weight that isrepresented by the numbers between the nodes to calibrate andlearn by the patterns (examples). So if we provide more newpattern then it would be better in detecting the attacks. Thealgorithm keeps on changing the numbers between the nodes(Weight) till the desired result is obtained (having flag either 1or 0). Fusing all the artificial neural network's as singleapplication against instances can be deficient in availability ifthe system breaks down technically. Thus, if one instance istechnically unavailable or down (for example an instance thatdetects TCP attack), the other two still will be present to detectTCP and ICMP attacks.In the meantime, instigating artificial neural networkinstances separately for every protocol bestows improvedmaintenance, more control to analyze and to train the algo.The moment detection system detects the forged packets, thedefense mechanism is invoked to allow the legitimate trafficgo through and drop the forged traffic and as soon as thesystem flags the traffic as normal the system unblocks theflagged traffic. The legitimate traffic floating through thenetwork and the system will not be interrupted because ofbeing already flagged as legitimate traffic by our proposedsystem.Besides the detection system provide the consciousnessabout attacks through communications via encryptedmessages. This kind of information exchange between thedetectors enhance the security system by identifying themalicious behavior and if required deploy countermeasures.VII. DESIGNWe designed our solution to monitor the networkcontinuously for malicious behavior by analyzing the headerinformation of retrieved packets of the networks using trainedartificial neural networks. Since retrieving a large amount ofdata in a network needs higher processing rate and is veryexpensive. Therefore, to overcome this for every protocol weused an individual packet threshold. If the amount of datapackets in specific network is higher than the specifiedthreshold of the protocol then the redeemed packets have to gothrough investigation. Based on our experiments, we selectedthe best threshold per protocol by counting the maximumnumber of data packets per unit time in selected distinctiveenvironment where the true values of threshold areconfigurable. The amount of data packets are segregated anddevised for examination, our proposed mechanism feeds thosepatterns into artificial neural network to decide thegenuineness of the retrieved packets. One DDoS detectionsystem is installed in every network to communicate throughencrypted message with other DDoS detectors as shown inFig. 8.313 P a g

(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 8, No. 8, 2017A.Output000ActionStatus0Traffic clear and allow traffic1Traffic malicious allow only genuine traffic topass through1101Traffic malicious allow only genuine traffic topass through1011Traffic malicious allow only genuine traffic topass through0111Traffic malicious allow only genuine traffic topass through0Repeat point 50100Repeat point 50010Repeat point 5B.111C.100If outcome from C is:Fig. 8.Detection, defense and cooperative mechanism.AFollowing are the details of Fig. 7.1) Install DDoS detectors on different networks.2) Each DDoS detector will maintain the registered IPaddress of each hop DDoS detector in order to communicatethrough encrypted message whenever an attack is detected.3) There should be continuous monitoring by DDoSdetector for abnormal behavior or data.4) Every passing packet is flagged as abnormal in case thevalue of passing packets is higher than the threshold.5) If the value of passing traffic is higher than thethreshold then:a) The organizer removes the undesired characters andarranges the packets accordingly.b) The victim IP addresses are identified by IPidentifier.c) The retried patterns are calculated by artificial neuralnetwork calculator and device them for artificial neuralnetwork engine.d) The patterns are taken as input by artificial neuralnetwork engine and produce a single output i.e. 0 for normaland 1 for attack.e) Step D is repeated three times to produce threeoutputs before the defense system is invoked.6) Then the detection system sends the output to thedefense system tack0111Attack1001Low rate attack0101Low rate attack0011Low rate attack0000No attackD. However, if the outcome matches none of the abovecombination then a value 2 is generated by the system thatmeans the traffic is unknown and is not used in the processof training artificial neural networks. In this scenario thesystem scans its local database to check if some data isreceived or detected by other hop DDoS detectors. If theneighbor DDoS detection systems respond with 0 or 1 thenthe algo is obsolete and outmoded as the algo detectionwas too. Thus proving that the local detector’s algo needsand offline retraining with up to date patterns else noaction is executed.7) The knowledge share block communicates with allenrolled neighbor DDoS detectors by sending them encryptedmessage in cooperating protocol used, destination IP and typeof attack. This information is also forwarded to security314 P a g

(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 8, No. 8, 2017offices by emails to let them know about these attacks forlogistics purpose.When we train the algo with old datasets the outcome ofthe detection system is two and artificial neural network hasthe special characteristics to detect the unknown pattern if thetype of attack or attack itself is similar to the pattern that thealgo was trained with. However the experimental resultsproved that if we train the system with old datasets then thealgo fails to detect the unknown patterns. The experimentsalso proved the fact that the system can detect the known andthe unknown attacks if we train the system with up to datepatterns while the algo that is trained with old datasets failedin such scenarios. In this situation the artificial neural networkof DDoS detection system (detector) that failed to detectattack while other neighboring DDoS detectors detect thesame attack that was trained with old datasets previously mustbe trained with latest up to date datasets but offline becausetraining process is supervised process and different patternsmust be instigated or re-instigated whenever required. Thus,when the algo training is not up to date the extra assistancecan be acquired from the share knowledge between thedetectors to make further decisions. In the meantime everydetector sends a complete email including full report of DDoSattacks acquired during that period to the security officers.One deployed detector may collect all the attacks and forwardit as a single email to the security officer. However, noinformation will be sent to the security officer in case thedeployed central point is down by any reason andconsequently no more countermeasures are deployed ifneeded. All the DDoS detectors are devised to work andprocess as a standalone element or distributed detectors whichcommunicates with other registered detectors throughencrypted message within the networks or that are deployed indifferent networks.Our solution is not confined to a least number of detectorsto communicate through encrypted messages. Thus in caseone DDoS detector stops functioning the other detectorsdeployed in the system can still send and receive messagestherefore making the solution durable, reliable and resistant toDDoS detector collapse or crash.To implement our designed solution, we have devised ourdetection module as plug-in and amalgamated it with Snort-AI(19). Snort AI is devised on Snort signature IDS project (20)and authors of this project are active in providing Snort AIplug-in and other amalgamation processes. The outcome ofthe IDS is combined with destination IP address to requestiptables (21) to elevate malicious or fake packets whileallowing legitimate data to pass through. In addition to this,we have also used RSA encryption technique for messageTABLE III.encryption over TCP connection while the deployed detectorsact as sender and receiver both.VIII. EVALUATIONWe used precision, susceptibility – expertise to recognizepositive results and specificity – expertise to recognizemalicious results, to evaluate our solution. Table 2 representsthe comparison of our results with other four approaches and asignature based solution for which quantitative assessmentsare recorded. We used legitimate and attack data traffic (highand low rate) to test our solution in an isolated and controllednetwork environment. During our experiments we launched 60rounds of genuine traffic and 60 rounds of DDoS attacks(ICMP, UDP, TCP) involving 80 to 90 zombies to target thedestination. We used UMware boxes to install the zombiesand attack from the virtual platform where the boxes wereconnected to the target devices using virtual routers. Wedeployed the DDoS detectors between the victims and thevirtual router where they examined the data traffic forirregularity and deformity.Based on the results obtained from our experiments oursolution provided a better result in terms of detection,precision, susceptibility and specificity as compared to othersolutions including Snort as shown in Table 3 and Fig. 10 to12, when all the tools were placed in the same manner andsame DDoS attacks were launched in the same environment atthe same time.The author (Author Name) used probabilistic neuralnetwork over two periods and the accuracy was calculated upto 92% and 97% for attack and normal traffic, respectively.Author name (6) compared back propagation and learningvector quantization. Since our solution is based on backpropagation we compared our solution to back propagationthat stipulates better precision and performance. In [22] Leuand Pai used as statistical method while [23] Xu, Wei andZang used KPCA and PSO-SUM to detect DDoS Attacks.KPCA (Kernel Principle Component Analysis) is used toeliminate unnecessary feat

Fig. 4. (a) Direct DDoS attack; (b) Reflexive DDoS attack. IV. CONSEQUENCES OF DDOS Effects of DDoS attacks on business installation are immediately reflected as Revenue Losses, with loss rate going as high as 300K/hour for service outage hours [13]. With advent of time, cost to mitigate DDoS attacks kept ever rising,

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