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COVID-19 is an infectious and contagious disease caused by the new coronavirus. The total number of cases is over 19 million and continues to grow. A common symptom noticed among COVID-19 patients is lung infection that results in breathlessness, and the lack of essential resources such as testing, oxygen, and ventilators enhances its severity. Chest X-ray can be used to design and develop a COVID-19 detection mechanism for a quicker diagnosis using AI and machine learning techniques. Due to this silver lining, various new COVID-19 detection techniques and prediction models have been introduced in recent times based on chest radiography images. However, due to a high level of unpredictability and the absence of essential data, standard models have showcased low efficiency and also suffer from overheads and complexities. This paper proposes a model fine tuning transfer learning-coronavirus 19 (Ftl-CoV19) for COVID-19 detection through chest X-rays, which embraces the ideas of transfer learning in pretrained VGG16 model with including combination of convolution, max pooling, and dense layer at different stages of model. Ftl-CoV19 reported promising experimental results; it observed training and validation accuracy of 98.82% and 99.27% with precision of 100%, recall of 98%, and F1 score of 99%. These results outperformed other conventional state of arts such as CNN, ResNet50, InceptionV3, and Xception.

Role of gene expression in cancer and cellular process is a complex problem that continues to haunt and challenge researchers. Sheer number of genes and inter related biological processes make the process of identifying more complex. Gene classification and analysis is a very difficult task for data scientists, as it requires various data, information and facts for making different quality articulation. Latent Dirichlet Allocation (LDA) has shown its compatibility for investigation and quality articulation in solid and malignant growth tissues. LDA is used to connect or grouping of genomic data and results are subsequently used for investigation. This work proposes A Modified Latent Dirichlet Allocation (MLDA) for gene classification and achieves quality articulation. The proposed MLDA identify and group differentially expressed genes between healthy and cancer tissues of various types. Experimental results report better performance of MLDA as compared to current state of art methods over Breast and Lung cancer data sets.

In mobile edge computing (MEC), it is difficult to recognise an optimum solution that can perform in limited energy by selecting the best communication path and components. This research proposed a hybrid model for energy-efficient cluster formation and a head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) and a modified k-mean clustering (MKM) method for MEC. We utilised a CNN to determine the best-transferring strategy and the most efficient partitioning of a specific task. The MKM method has more than one cluster head in each cluster to lead. It also reduces the number of reclustering cycles, which helps to overcome the energy consumption and delay during the reclustering process. The proposed model determines a training dataset by covering all the aspects of cost function calculation. This training dataset helps to train the model, which allows for efficient decision-making in optimum energy usage. In MEC, clusters have a dynamic nature and frequently change their location. Sometimes, this creates hurdles for the clusters to form a cluster head and, finally, abandons the cluster. The selected cluster heads must be recognised correctly and applied to maintain and supervise the clusters. The proposed pairing of the modified k-means method with a CNN fulfils this objective. The proposed method, existing weighted clustering algorithm (WCA), and agent-based secure enhanced performance approach (AB-SEP) are tested over the network dataset. The findings of our experiment demonstrate that the proposed hybrid model is promising in aspects of CD energy consumption, overhead, packet loss rate, packet delivery ratio, and throughput compared to existing approaches.

Mobile ad-hoc network is a dynamic and self configuring network composed of mobile nodes that are cooperative and intelligent in nature, which forms a temporary network without any base station. Due to dynamic nature, route among source and destination is not fixed and it can change with time, this results in routing overhead, congestion and higher energy consumption in nodes. In this condition, clustering becomes very relevant in highly dense network in which the clusterhead initiates the routing instead of normal nodes. This paper proposes an efficient way for cluster formation and selection of stable cluster heads (E-CFSA) with high energy level. E-CFSA forms node cluster using k-means algorithm where distance with centroids acts as the key parameter. Afterwards, artificial neural network (ANN) is applied in each cluster to select efficient clusterhead. It also updates weight of input parameters such as mobility, packet drop, energy and number of neighbor nodes in order to minimize the errors at target neuron using back propagation algorithm. The overall process identifies and selects the nodes as clusterheads that possess longer stability and higher energy level. This procedure reduces the repetitive selection of cluster heads and re-affiliation of member nodes in a cluster. Thereafter, performance is evaluated in terms of overhead, packet delivery ratio, throughput and cluster head stability time with variation in speed of node or number of nodes in MANET. The experimental results obtained through proposed E-CFSA are compared to other existing mechanisms and a comparison is drawn. These comparative results very clearly revealed that proposed E-CFSA outperformed its peers.

Cloud Computing is the decentralized type of architecture which is vulnerable to various type of security attacks. Homomorphic encryption is the encryption scheme to encrypt objects which are used to access data from the cloud server. Homomorphic encryption scheme has major disadvantage of key management and key sharing which reduce its efficiency. Particle swarm optimization algorithms (PSO) are nature-inspired meta-heuristic algorithms that are population dependent known for social behavior of birds and fishes. These concepts are used as an inspiration to build scientific approach for complex problem solving. Depending upon the quality of measure, the solutions are enhanced by the algorithms initiating from a randomly distributed set of particles. By moving the particles around the search space using a set of mathematical expressions, the improvisations are achieved. In this research work, technique of PSO is applied which generate key for the encryption. PSO in is the optimization algorithm used to generate fixed number key for the encryption. PSO based homomorphic algorithm is implemented in MATLAB and simulation results shows that it performs well in terms of execution time, resource utilization. The execution time and resource utilization of PSO based homomorphic algorithm is less as compared to homomorphic algorithm. The result is optimized upto 10% approx in the improved algorithm as compared to existing algorithm.

A wireless sensor network (WSN) is a network of devices that can communicate after gathering information by monitoring any region through wireless links. Due to this delicate arrangement several numbers of attacks directly affects the WSN functions especially denial of service (DoS). DoS is the most popular and frequent among all to affect WSN. Recently, blackhole attack has taken over it and comprises security and integrity of WSN. Secure and reliable data transmissions are the prime requirements of WSN but new evolving attacks are threats to achieve this objective. This paper proposes an algorithm that detects and recovers nodes from blackhole assaults in WSN. The proposed algorithm Trusted and Secure routing (TSR) involves detector nodes, moves in the network algorithm identifies blackhole attacks in the network, marks this node as balckhole and subsequently excludes from the network. It enables to transmit data securely with alternate path using detector node. The proposed algorithm increases the performance and the delivery ratio of data in WSN. The experimental results show reliable and secure data transmission from DoS and blackhole attacks.

“Steganography” is a strategy that defeats unapproved clients to approach the critical information, to imperceptibility and payload limit utilizing the diverse system like discrete cosine transform (DCT) and discrete shearlet transform (DST). The available methods till date result in good robustness but they are not independent of file format. The point of this exploration work is to build up an autonomous of record organize and secure concealing information conspire. The independent of file format and secure hiding data scheme is increased by combining DST and least significant bits (LSB) technique. In like manner a proficient plan is produced here that are having better MSE and PSNR against various characters.

In today’s era, vehicular ad hoc network (VANET) is an interesting research area, due the dynamics that it brings. VANET is composed up of different nodes that exchange and transfer information among them and develop an infrastructure for communication. VANET shares and exchanges information; therefore, it needs to be secured. This paper presents a (LSR-VANET) with traffic status. LSR-VANET employs a new cluster-based group signature technique for analyzing the performance and implementing security. LSR-VANET also allows all the transmission through the cluster head that in turn enhances the security and authentication in VANET. LSR-VANET is then evaluated on the basis of packet delivery ratio, routing load and throughput.

Medical diagnosis and extracting patterns that can be converted into useful knowledge is a challenging work for researchers. Medical records are based on real-time data that have high dimensionality which makes task of extracting patterns even more complex. Prediction of various diseases often suffers due to large dimensionality that includes thyroid, diabetes, cancer, etc. Data mining is a process of finding out information and constructing a knowledge base from a huge amount of data, which remains useful. Data mining achieves these tasks involving classification, clustering, regression, and prediction. Reduction of dimensionality with the motive of knowledge discovery is an essential aspect of data mining as it helps in prediction/determination for various data analytics filed like medical diagnosis, business modeling, and government data analysis. This paper presents cooperative method for classification of thyroid using machine learning fusing C4.5 and random forest classification technique (CCTML). Experimental results are compared with other conventional techniques and the results obtained through CCTML reported better classification accuracy and outperform conventional methods.

Intrusion detection concept plays a vital role in personal computer (PC) security design. Intrusion detection system (IDS) essentially fits in as a unit which monitors the user activities, incoming traffic, and then distinguishes or classifies which one is intrusion and which one is normal or legitimate. Fundamentally IDS recognizes any misuse or unauthorized access which is essentially an attack to the crucial assets of the system or network. It detects the malicious traffic data on a network or a host. The task of desired classification often gets affected by the presence of noisy, redundant, and irrelevant data input. Occurrence of noise in dataset leads to poor classification as it increases the possibility of wrong detection of a class. High misclassification rate and low detection rate by the classifier in IDS enlarges feature space. To overcome this limitation, hybrid intrusion system (H-IDS) is proposed in this paper. H-IDS uses a hybrid strategy with support vector machine (SVM) and intelligent water drops (IWD) to execute the feature selection and classification techniques for IDS. Experimentations reveal that proposed H-IDS helps to accomplish the goal by attaining high classification, detection, and precision as compared to current state of the art.

Internet of Things (IoT) has been changing the way of operations for multiple segments like Industries, Health Care, and Manufacturing. It also holds a chance to change how Educational Institutions operates and enhance student learning experience. It has enormous opportunities for Educational Segment which will enhance the learning experiences for students, teachers and other stakeholders. The development of IOT Systems, devices, applications, and services are already in the consideration and process by the students and researchers. Therefore, this paper presents a framework to capture validated information of individual Higher Educational Institutions (HEI) through IOT devices to avail the assessment based platform to evaluate and enhance the educational experience. It also describes the processes to automate the survey of Educational Institutions and provide analytical report using IOT components and Machine Learning. To ease the understanding of different methods we provide a prototype with its practical implementations using common processes in a friendly manner.

Mobile ad hoc network (MANET) brings simplicity and flexibility but also introduces constraints due to mobility, dynamic configuration, limited resources, and security. Security is among the most important limitations in MANET. Because of its distinctive features, MANET creates a variety of serious challenges. The various protection schemes aligned with routing makes efforts in lowering threat perception. In this paper, a novel security mechanism named secure multipath routing (SMR) for MANET is proposed for jamming attacks that also takes care of routing and load balancing. The proposed technique will first identify the attacked node, and then, it will not allow other attacker node/s. Thereafter, as a corrective measure it blocks the activity of offender and drops it from the routing. SMR is then judged against conventional AODV and AOMDV protocols in NS-2.35. Experimental results show that the proposed SMR demonstrates better results in terms of throughput, average end-to-end delay, packet delivery ratio, and routing overhead.

Mobile ad hoc network (MANET) is a lively network that is self configuring in nature because of its mobility characteristics. MANET plays an important role in situations where fixed wired pre-defined backbone cannot be created or remains more costly. MANET brings ease and flexibility but at the same time it also introduces various limitations. This paper proposes Fuzzy logic based efficient packet dropping detection approach (FL-EPDDA) for MANET to overcome that challenges of malicious nodes in MANET. FL-EPDDA use fuzzy inference system (FIS) to identify malicious nodes in MANET. FIS uses three input parameter packet delivery ratio, packet forward and residual energy of node. FIS classifies the network nodes weather it is malicious or not with the help of these inputs. FL-EPDDA identifies the above discussed activities and it also discovers a trusted and secure path for secure data transmission. FL-EPDDA very efficiently classifies normal working mobile nodes and malicious nodes within MANET. Experimental are performed under parameters like packet delivery ratio, average throughput and packet drop rate with variation in malicious nodes in MANET. Comparative experimental results shows the proposed FL-EPDDA outperforms current state of art AODV under all the test conditions.

In the daily life, everybody keeps on using Internet after the waking up that includes the communication among different people of this world. On one side, Internet has made everyone’s life very convenient and provides many facilities that can be used on social networking sites such as chat, messaging, comment, and blogging. This way everyone keeps on sharing the personal data at different places like Web sites, chats, social media. The text mining can be defined as the process of finding useful information from the given text. There exist various methods that remain helpful in analyzing texts and extracting the information but these often suffer from various complexities. Also, theoretically, it is quite difficult to analyze and extract the information from these raw data. This paper presents an Effective feed forward artificial neural network (FP-ANN) for text mining that generates different textual patterns from several resources and provides results very precisely with lesser computation time, lower cost, and overhead. FP-ANN approach calculates the hash label and discovers importance between inputs for text mining.

This paper presents an energy-efficient and quality of service achieved through MapReduce in cloud environment. Although decrease in operating costs residue to be a key desire for movement to Cloud environments, Power consumption is a big thing for data centers and cloud service providers. Many big data applications execute on Hadoop MapReduce framework for handling large workloads. Cloud computing, Virtual machine allocation policy is an important aspect while dealing with multiple component architectures. Data allocation, privacy allocation, resource optimization are challenging task in any architecture. Traditional algorithm faces either a long computation time or high cost over the computation. In this approach which is Hadoop driven matrix based architecture is used for data processing. A hybrid combination of both the approach makes user able to process data effectively with time and cost. The approach is performed on Ubuntu 16.04, HDFS and Java API and maximum power consumption of 112 W as a result.

Cloud computing is one of the most important and remarkable innovations of recent times. Since all the data is stored in servers located at different locations operating on different hardware so data consistency, availability, and security while accessing becomes a challenging task. Cloud introduces different data access management techniques that make sure of information security within the cloud even in the scenarios of outsourcing and untrusted cloud servers. Information access management in the cloud is a difficult issue due to the distributed architecture of cloud storage systems. Low trust of such a storage paradigm leads to limited proliferation and acceptability. These challenges further soar while addressing the issues of security, since it remains a massive risk. In this scenario, hybrid information prove a meaningful solution as it assists and stores all these varied information depending on privacy, needs of information and initial supply and offered capability. In this context, this paper proposes a cloud security hybrid querying system (CSHQS) that help to overcome data storage complexity and its subsequent access. The query input mechanism help in data computation, its processing, and deriving output with input test. The experimental results show the efficiency of the proposed CSHQS as it outperforms the existing state of art.

Security is an important need for any organization to establish trust and consequently prevents misuse. Intrusion detection system (IDS) is a defense mechanism that strives to differentiate between legitimate and malicious activities with intent to secure the resource and henceforth establish trust. Due to this importance significant amount of research has been carried in this domain. This paper introduces concept of Rough sets in IDS as theoretically it describes the upper and lower approximations. This theory is a key concept in order to determine a wide range of various ranges or cases of uncertainty. Integration of rough set in IDS facilitates better identification of potential malicious contents. Experimental results clearly indicates that NB tree-based IDS with rough set reports better detection rate and lower false positive, subsequently outperforms the existing state of the art.

Ad-hoc networks play an important role in situations where fixed wired pre-defined backbone cannot be created. In mobile adhoc network (MANET), discovery of routing path with minimum routing and control overhead are important challenges. In this paper, Distributed multi criteria based clustering approach (DMBCA) is introduced for overcoming routing overhead of network in mobile ad-hoc network. The proposed DBMCA use multiple metric clustering techniques for defining “optimal node factor” that can use in selection of cluster head. DMBCA is then compared with conventional AODV routing protocol in NS-2.35. Experimental results show that the proposed DMBCA achieves much better results in terms of performance parameters such as packet delivery ratio, average throughput and routing overhead than conventional AODV technique.

Delay-tolerant network (DTN) allows nodes to follow, store, and forward packets through different delivery mechanisms. These data bundles are created by sender to send data to multiple destinations. DTN supports multicast approach for communication. DTN enables the capability of transmission that involves long interruption, wait, and interoperable communications among these networks. Congestion in wireless network remains a challenging issue as enhancing the available bandwidth of network is not possible. Multicast communication in DTN network might be an intricate subject because nodes generously move within the surroundings that often become insecure due to the absence of centralized architecture. This paper proposes a network-coding-based congestion control scheme for delay-tolerant network communication (NCCCS-DTN). NCCCS-DTN using network coding functions is better than its state of the art (CASPaR). The proposed NCCCS-DTN reduces the bandwidth consumption and also lowers load on the link. NCCCS-DTN approach provides best route and also utilizes bandwidth and offers efficient congestion-free communication.

Self-tuning and detector power is introduced to address improper coverage of NSA.Cooperation among agents makes correct identification of anomaly and lower FAR.EPAADPS improves anomaly detection and reduces FAR compared to state of art.Preventive mechanism identifies and contains novel and zero day attacks. Artificial Immune System (AIS) is inspired from Biological Immune System (BIS) and demonstrates a lot of interesting facets and intelligence that include self-learning, self adaption, self regulatory, distributed with self/non-self detection capabilities. Due to these astonishing qualities AIS are predominantly used in anomaly detection where anomalies are treated as non-self that needs to be detected. Therefore, AIS appears appropriate for development of a proactive system to identify and prevent novel and unseen anomalies. This paper presents "An Efficient Proactive Artificial Immune System based Anomaly Detection and Prevention System (EPAADPS)" which embodies immune attributes to distinguish self and non-self in quest to identify and prevent novel, unseen anomalies. Negative Selection Algorithm (NSA) is a key AIS concept and is used for anomaly detection in various publications. Despite its relative success, detector selection and thereafter anomaly detection demands a more effective algorithm. This paper put forwards concept of self-tuning of detectors and detector power in NSA with the intension to make a detector evolve and facilitate better and correct self and non-self coverage. Thereafter, agents accompanying detectors collaborate and communicate between themselves to proactively discover correct anomalies and then take appropriate preventive measures. The performance of EPAADPS is contrasted with closely related state of art RNS algorithm using real valued representation and Euclidean distance. Experimental results revels promising EPAADPS performance which very comfortably outperforms the RNS. Furthermore, these results also demonstrate that EPAADPS shows remarkable resilience and intelligence in detecting novel unseen anomalies and with preventive measures to overcome the threat perception.

Data in the network is not secure and can be accessed by unauthenticated and unauthorized users. So to make data secure in the network, various encryption algorithms are used. Security along with confidentiality of the data is to be maintained in the network. Encryption is the technique by which data is encoded and sent to the receiver, where it gets decoded back to the plaintext. Encryption algorithm or technique should take minimum time and space for the execution. There are various encryption algorithms which are frequently used and some of them are high in measure of time and space complexity. This paper presents a more Secure and Time Efficient Encryption Method (STEEM) which is concentrating on high security of the encrypted text along with the time efficiency. This paper shows the working flows of the proposed work which would be solely responsible for achieving more efficiency on prime parameters.

Image compression goal is to reduce the size of an image but without losing the information, the loss of information some time is acceptable but not always, today’s world the compression of image, video done with various techniques for the variety of an application. Medical science is one of the main application area for the image compression in which we compress the human body part images without losing the information and use in future. In this paper we proposed a hybrid model for the Bio-medical image compression which is better in the terms of result by measuring performance evaluation parameters to improve the value of performance parameter.

Image enhancement is one of the key concerns pertaining to better quality image photography captured through modern digital cameras. Probability of digital images getting compromised through lightning and weather conditions remains high. Due to these environmental limitations, many a time loss of information from images is reported. Major role of image amplification is to bring out hidden details of an image from the sample. It provides multiple options for enhancing the visual quality of images. This paper addresses problem of early skin cancer detection using image enhancement techniques and presents a multi-scale retinex with color restoration (MSR-CR) technique for skin cancer detection. The actual skin portion suffering from cancer is identified by comparing enhanced image with available ground truth image. Experimental result shows significant improvement over previously available techniques.

Services provided by cloud computing are the very fascinating as it offers of free access or paid access to the buyers. This paradigm facilitates a verity of facets to the users. Being such an interesting concept and having so many beneficial features, the cloud also suffers from several security risks. This results in creating a lot of challenges at the time of implementation. Since, cloud services are not confined to limited boundaries therefore chances of misadventure leading to compromise is immense. Intrusion detection system (IDS) within the cloud environment is an interesting idea and this paper presents “A neural network-based intrusion detection system for cloud computing (NN-IDS)”. It discusses the various integrated methods that provide detection in cloud environment and also takes prevention measures for the malignant functions found within the cloud. Experimental results show that NN-IDS performs much better than existing work and shows significant improvement in accuracy and precision.

Mobile adhoc networks (MANET) is an interesting concept, as all mobile nodes work like router. They can receive and transmit data packet any time in hostile environment even when the source and the destination mobile nodes are not directly connected to each other. In this circumstance, data packets are forwarded to the destination mobile node by relaying the transmission through intermediary mobile nodes. Due to selfish behavior, packets are intentionally dropped by intermediate nodes, also intermediate nodes continuously forward or broadcast control packets in network that consumes energy of node and lowers performance. This makes discovery of secure routing path and detection of selfish nodes and their activities as important challenges in MANET. This paper proposes a weight-based secure approach for identifying selfish behavior in MANET (WSISB). The proposed WSISB identifies the above-discussed activities and it will also discover a trusted path for secure data transmission. Experimental results show that WSISB performs much better than existing techniques and show significant improvement in terms of performance.

Content-based image retrieval based on color, texture and shape are important concepts that facilitate quick user interaction. Due to these reasons, humongous amount of explores in this direction has been done, and subsequently, current focus has now shifted in improving the retrieval precision of images. This paper proposes a dominant color and content-based image retrieval system using a blend of color, shape, and texture features. K-dominant color is extracted from the pixels finding and can be gathered in the form of cluster or color clusters for forming a cluster bins. The alike colors are fetched on the basis of distance calculations between the color combinations. Then the combination of hue, saturation, and brightness is calculated where hue shows the exact color, and the color purity is shown by saturation, and the brightness of the percentage degree increases from black to white. Experimental results clearly indicate that the proposed method outperforms the existing state of the art like LBP, CM, and LBP and CM in combination.

Human skin detection strives to spot skin from the pictures. Automatic skin detection is considered as a significantly difficult and complex as skin image differ on the aspects of contents due to variation in size, style, orientation, alignment coupled with different contrast and background. This paper proposes a skin detection approach using localization, tracking, extraction, enhancement, and recognition. This approach remains sensitive to the color palette and uses edge detection technique. Also, color classification box incorporates a deep impact on the performance of the rule. The proposed approach detects single as well as multiple persons in a picture. Promising results are obtained on variety of pictures, except in few pictures wherever color distinction is hard to even when edge detection rule.

Hand gesture recognition system is employed to provide interface between computer and human using hand gesture. This paper presents a technique for human computer interface through common hand gesture that is efficient to commemorate 25 aspersion gestures from the American sign language hand alphabet. The prospect of this paper is to develop up an algorithm for hand gesture recognition with reasonable accuracy. This work uses a domain independent learning methodology to automatically stir low-level spatio-temporal descriptors for high-level cross recognition by Correlated variance programming. Feature extraction is the most important orientation for gesture recognition and is indeed important in terms of giving input to a classifier. In this work Canny edge detector algorithm is used to find edge of the segmented and morphological filtered image which yields boundary of hand gesture in the image then Correlated variance mean based programming applied for recognition of gesture. Experimental results very precisely indicate that the developed method outperforms the existing state of the art.

Immune System (AIS) is a convoluted and complex arrangement derived from biological immune system (BIS). It possesses the abilities of self-adapting, self-learning and self- configuration. It has the basic function to distinguish self and non-self. Negative Selection Algorithm (NSA) over the years has shown to be competent for anomaly detection problems. In the past decade internet has popularized and proliferated into our lives immensely. Internet attack cases are increasing with different and new attack methods. This paper presents a Cooperative Negative Selection Algorithm (CNSA) for Anomaly Detection by integrating a novel detector selection strategy and voting between them to effectively identify anomaly. New introduced mechanisms in CNSA enable it to cover more self region correctly and efficiently. It also reduces computational complexities. Experimental results show high anomaly detection rate with less false positive alarm and low overhead in most of the cases.

Immune System (AIS) over the years has caught attention of researchers of various domains for complex problem solving. AIS model the procedure and methodologies of Biological Immune System (BIS) which protects the body from diverse attacks and different challenges. Scientists over the years are amazed with the appealing features of BIS that can be exploited. The most significant of them is its ability to distinguish self and non-self. This theory forms the basis of Negative Selection Algorithm (NSA) in AIS. NSA is competent for anomaly detection problems. From this perspective this research paper presents a Novel Immunity inspired approach for Anomaly Detection (NIIAD) with the feature of fine tuning. The main intention of adopting finetuning is to covering more self region and identifying non self region proficiently. Experimental results reflects high detection ratio with less false alarm and low overhead.

Wireless mobile ad-hoc networks are those networks which has no physical links between the nodes. There is no fixed topology in this network due to the random mobility of nodes, interference, multipath propagation and path loss. Many Routing protocols have been proposed and developed for accomplishing this task. The intent of this paper is to analyze three ad-hoc routing protocols AODV, DYMO, FISHEYE against wormhole attack in wireless network. This paper concentrates evaluating the performance of routing protocols when wormhole attacks involve in wireless network. The performance analysis for above protocol is based on variation in speed of nodes in a network with 50 nodes. All simulation is carried out with QualNet 5.0 simulator.