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Tsochev, Georgi, Trifonov, Roumen, Yoshinov, Radoslav, Manolov, Slavcho, Pavlova, Galya.  2019.  Improving the Efficiency of IDPS by Using Hybrid Methods from Artificial Intelligence. 2019 International Conference on Information Technologies (InfoTech). :1-4.

The present paper describes some of the results obtained in the Faculty of Computer Systems and Technology at Technical University of Sofia in the implementation of project related to the application of intelligent methods for increasing the security in computer networks. Also is made a survey about existing hybrid methods, which are using several artificial intelligent methods for cyber defense. The paper introduces a model for intrusion detection systems where multi agent systems are the bases and artificial intelligence are applicable by the means simple real-time models constructed in laboratory environment.

Naik, N., Shang, C., Shen, Q., Jenkins, P..  2018.  Vigilant Dynamic Honeypot Assisted by Dynamic Fuzzy Rule Interpolation. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). :1731–1738.

Dynamic Fuzzy Rule Interpolation (D-FRI) offers a dynamic rule base for fuzzy systems which is especially useful for systems with changing requirements and limited prior knowledge. This suggests a possible application of D-FRI in the area of network security due to the volatility of the traffic. A honeypot is a valuable tool in the field of network security for baiting attackers and collecting their information. However, typically designed with fewer resources they are not considered as a primary security tool for use in network security. Consequently, such honeypots can be vulnerable to many security attacks. One such attack is a spoofing attack which can cause severe damage to the honeypot, making it inefficient. This paper presents a vigilant dynamic honeypot based on the D-FRI approach for use in predicting and alerting of spoofing attacks on the honeypot. First, it proposes a technique for spoofing attack identification based on the analysis of simulated attack data. Then, the paper employs the identification technique to develop a D-FRI based vigilant dynamic honeypot, allowing the honeypot to predict and alert that a spoofing attack is taking place in the absence of matching rules. The resulting system is capable of learning and maintaining a dynamic rule base for more accurate identification of potential spoofing attacks with respect to the changing traffic conditions of the network.

Berjab, N., Le, H. H., Yu, C., Kuo, S., Yokota, H..  2018.  Hierarchical Abnormal-Node Detection Using Fuzzy Logic for ECA Rule-Based Wireless Sensor Networks. 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC). :289-298.

The Internet of things (IoT) is a distributed, networked system composed of many embedded sensor devices. Unfortunately, these devices are resource constrained and susceptible to malicious data-integrity attacks and failures, leading to unreliability and sometimes to major failure of parts of the entire system. Intrusion detection and failure handling are essential requirements for IoT security. Nevertheless, as far as we know, the area of data-integrity detection for IoT has yet to receive much attention. Most previous intrusion-detection methods proposed for IoT, particularly for wireless sensor networks (WSNs), focus only on specific types of network attacks. Moreover, these approaches usually rely on using precise values to specify abnormality thresholds. However, sensor readings are often imprecise and crisp threshold values are inappropriate. To guarantee a lightweight, dependable monitoring system, we propose a novel hierarchical framework for detecting abnormal nodes in WSNs. The proposed approach uses fuzzy logic in event-condition-action (ECA) rule-based WSNs to detect malicious nodes, while also considering failed nodes. The spatiotemporal semantics of heterogeneous sensor readings are considered in the decision process to distinguish malicious data from other anomalies. Following our experiments with the proposed framework, we stress the significance of considering the sensor correlations to achieve detection accuracy, which has been neglected in previous studies. Our experiments using real-world sensor data demonstrate that our approach can provide high detection accuracy with low false-alarm rates. We also show that our approach performs well when compared to two well-known classification algorithms.

Pratama, R. F., Suwastika, N. A., Nugroho, M. A..  2018.  Design and Implementation Adaptive Intrusion Prevention System (IPS) for Attack Prevention in Software-Defined Network (SDN) Architecture. 2018 6th International Conference on Information and Communication Technology (ICoICT). :299-304.

Intrusion Prevention System (IPS) is a tool for securing networks from any malicious packet that could be sent from specific host. IPS can be installed on SDN network that has centralized logic architecture, so that IPS doesnt need to be installed on lots of nodes instead it has to be installed alongside the controller as center of logic network. IPS still has a flaw and that is the block duration would remain the same no matter how often a specific host attacks. For this reason, writer would like to make a system that not only integrates IPS on the SDN, but also designs an adaptive IPS by utilizing a fuzzy logic that can decide how long blocks are based on the frequency variable and type of attacks. From the results of tests that have been done, SDN network that has been equipped with adaptive IPS has the ability to detect attacks and can block the attacker host with the duration based on the frequency and type of attacks. The final result obtained is to make the SDN network safer by adding 0.228 milliseconds as the execute time required for the fuzzy algorithm in one process.

Sowah, R., Ofoli, A., Koumadi, K., Osae, G., Nortey, G., Bempong, A. M., Agyarkwa, B., Apeadu, K. O..  2018.  Design and Implementation of a Fire Detection andControl System with Enhanced Security and Safety for Automobiles Using Neuro-Fuzzy Logic. 2018 IEEE 7th International Conference on Adaptive Science Technology (ICAST). :1-8.

Automobiles provide comfort and mobility to owners. While they make life more meaningful they also pose challenges and risks in their safety and security mechanisms. Some modern automobiles are equipped with anti-theft systems and enhanced safety measures to safeguard its drivers. But at times, these mechanisms for safety and secured operation of automobiles are insufficient due to various mechanisms used by intruders and car thieves to defeat them. Drunk drivers cause accidents on our roads and thus the need to safeguard the driver when he is intoxicated and render the car to be incapable of being driven. These issues merit an integrated approach to safety and security of automobiles. In the light of these challenges, an integrated microcontroller-based hardware and software system for safety and security of automobiles to be fixed into existing vehicle architecture, was designed, developed and deployed. The system submodules are: (1) Two-step ignition for automobiles, namely: (a) biometric ignition and (b) alcohol detection with engine control, (2) Global Positioning System (GPS) based vehicle tracking and (3) Multisensor-based fire detection using neuro-fuzzy logic. All submodules of the system were implemented using one microcontroller, the Arduino Mega 2560, as the central control unit. The microcontroller was programmed using C++11. The developed system performed quite well with the tests performed on it. Given the right conditions, the alcohol detection subsystem operated with a 92% efficiency. The biometric ignition subsystem operated with about 80% efficiency. The fire detection subsystem operated with a 95% efficiency in locations registered with the neuro-fuzzy system. The vehicle tracking subsystem operated with an efficiency of 90%.

Nadeem, Humaira, Rabbani, Imran Mujaddid, Aslam, Muhammad, M, Martinez Enriquez A..  2018.  KNN-Fuzzy Classification for Cloud Service Selection. Proceedings of the 2Nd International Conference on Future Networks and Distributed Systems. :66:1-66:8.

Cloud computing is an emerging technology that provides services to its users via Internet. It also allows sharing of resources there by reducing cost, money and space. With the popularity of cloud and its advantages, the trend of information industry shifting towards cloud services is increasing tremendously. Different cloud service providers are there on internet to provide services to the users. These services provided have certain parameters to provide better usage. It is difficult for the users to select a cloud service that is best suited to their requirements. Our proposed approach is based on data mining classification technique with fuzzy logic. Proposed algorithm uses cloud service design factors (security, agility and assurance etc.) and international standards to suggest the cloud service. The main objective of this research is to enable the end cloud users to choose best service as per their requirements and meeting international standards. We test our system with major cloud provider Google, Microsoft and Amazon.

Kotenko, Igor, Ageev, Sergey, Saenko, Igor.  2018.  Implementation of Intelligent Agents for Network Traffic and Security Risk Analysis in Cyber-Physical Systems. Proceedings of the 11th International Conference on Security of Information and Networks. :22:1-22:4.

The paper offers an approach for implementation of intelligent agents intended for network traffic and security risk analysis in cyber-physical systems. The agents are based on the algorithm of pseudo-gradient adaptive anomaly detection and fuzzy logical inference. The suggested algorithm operates in real time. The fuzzy logical inference is used for regulation of algorithm parameters. The variants of the implementation are proposed. The experimental assessment of the approach confirms its high speed and adequate accuracy for network traffic analysis.

Shirsat, S. D..  2018.  Demonstrating Different Phishing Attacks Using Fuzzy Logic. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). :57-61.

Phishing has increased tremendously over last few years and it has become a serious threat to global security and economy. Existing literature dealing with the problem of phishing is scarce. Phishing is a deception technique that uses a combination of technology and social engineering to acquire sensitive information such as online banking passwords, credit card or bank account details [2]. Phishing can be done through emails and websites to collect confidential information. Phishers design fraudulent websites which look similar to the legitimate websites and lure the user to visit the malicious website. Therefore, the users must be aware of malicious websites to protect their sensitive data [1]. But it is very difficult to distinguish between legitimate and fake website especially for nontechnical users [4]. Moreover, phishing sites are growing rapidly. The aim of this paper is to demonstrate phishing detection using fuzzy logic and interpreting results using different defuzzification methods.

Pillutla, H., Arjunan, A..  2018.  A Brief Review of Fuzzy Logic and Its Usage Towards Counter-Security Issues. 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). :1-6.

Nowadays, most of the world's population has become much dependent on computers for banking, healthcare, shopping, and telecommunication. Security has now become a basic norm for computers and its resources since it has become inherently insecure. Security issues like Denial of Service attacks, TCP SYN Flooding attacks, Packet Dropping attacks and Distributed Denial of Service attacks are some of the methods by which unauthorized users make the resource unavailable to authorized users. There are several security mechanisms like Intrusion Detection System, Anomaly detection and Trust model by which we can be able to identify and counter the abuse of computer resources by unauthorized users. This paper presents a survey of several security mechanisms which have been implemented using Fuzzy logic. Fuzzy logic is one of the rapidly developing technologies, which is used in a sophisticated control system. Fuzzy logic deals with the degree of truth rather than the Boolean logic, which carries the values of either true or false. So instead of providing only two values, we will be able to define intermediate values.

Naik, N., Jenkins, P., Kerby, B., Sloane, J., Yang, L..  2018.  Fuzzy Logic Aided Intelligent Threat Detection in Cisco Adaptive Security Appliance 5500 Series Firewalls. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1-8.

Cisco Adaptive Security Appliance (ASA) 5500 Series Firewall is amongst the most popular and technically advanced for securing organisational networks and systems. One of its most valuable features is its threat detection function which is available on every version of the firewall running a software version of 8.0(2) or higher. Threat detection operates at layers 3 and 4 to determine a baseline for network traffic, analysing packet drop statistics and generating threat reports based on traffic patterns. Despite producing a large volume of statistical information relating to several security events, further effort is required to mine and visually report more significant information and conclude the security status of the network. There are several commercial off-the-shelf tools available to undertake this task, however, they are expensive and may require a cloud subscription. Furthermore, if the information transmitted over the network is sensitive or requires confidentiality, the involvement of a third party or a third-party tool may place organisational security at risk. Therefore, this paper presents a fuzzy logic aided intelligent threat detection solution, which is a cost-free, intuitive and comprehensible solution, enhancing and simplifying the threat detection process for all. In particular, it employs a fuzzy reasoning system based on the threat detection statistics, and presents results/threats through a developed dashboard user interface, for ease of understanding for administrators and users. The paper further demonstrates the successful utilisation of a fuzzy reasoning system for selected and prioritised security events in basic threat detection, although it can be extended to encompass more complex situations, such as complete basic threat detection, advanced threat detection, scanning threat detection, and customised feature based threat detection.

Douzi, S., Benchaji, I., ElOuahidi, B..  2018.  Hybrid Approach for Intrusion Detection Using Fuzzy Association Rules. 2018 2nd Cyber Security in Networking Conference (CSNet). :1-3.

Rapid development of internet and network technologies has led to considerable increase in number of attacks. Intrusion detection system is one of the important ways to achieve high security in computer networks. However, it have curse of dimensionality which tends to increase time complexity and decrease resource utilization. To improve the ability of detecting anomaly intrusions, a combined algorithm is proposed based on Weighted Fuzzy C-Mean Clustering Algorithm (WFCM) and Fuzzy logic. Decision making is performed in two stages. In the first stage, WFCM algorithm is applied to reduce the input data space. The reduced dataset is then fed to Fuzzy Logic scheme to build the fuzzy sets, membership function and the rules that decide whether an instance represents an anomaly or not.

Alibadi, S. H., Sadkhan, S. B..  2018.  A Proposed Security Evaluation Method for Bluetooth E0Based on Fuzzy Logic. 2018 International Conference on Advanced Science and Engineering (ICOASE). :324–329.

The security level is very important in Bluetooth, because the network or devices using secure communication, are susceptible to many attacks against the transmitted data received through eavesdropping. The cryptosystem designers needs to know the complexity of the designed Bluetooth E0. And what the advantages given by any development performed on any known Bluetooth E0Encryption method. The most important criteria can be used in evaluation method is considered as an important aspect. This paper introduce a proposed fuzzy logic technique to evaluate the complexity of Bluetooth E0Encryption system by choosing two parameters, which are entropy and correlation rate, as inputs to proposed fuzzy logic based Evaluator, which can be applied with MATLAB system.

Ghafoor, K. Z., Kong, L., Sadiq, A. S., Doukha, Z., Shareef, F. M..  2018.  Trust-aware routing protocol for mobile crowdsensing environments. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :82–87.
Link quality, trust management and energy efficiency are considered as main factors that affect the performance and lifetime of Mobile CrowdSensing (MCS). Routing packets toward the sink node can be a daunting task if aforementioned factors are considered. Correspondingly, routing packets by considering only shortest path or residual energy lead to suboptimal data forwarding. To this end, we propose a Fuzzy logic based Routing (FR) solution that incorporates social behaviour of human beings, link quality, and node quality to make the optimal routing decision. FR leverages friendship mechanism for trust management, Signal to Noise Ratio (SNR) to assure good link quality node selection, and residual energy for long lasting sensor lifetime. Extensive simulations show that the FR solution outperforms the existing approaches in terms of network lifetime and packet delivery ratio.
Vorobiev, E. G., Petrenko, S. A., Kovaleva, I. V., Abrosimov, I. K..  2017.  Analysis of computer security incidents using fuzzy logic. 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM). :369–371.

The work proposes and justifies a processing algorithm of computer security incidents based on the author's signatures of cyberattacks. Attention is also paid to the design pattern SOPKA based on the Russian ViPNet technology. Recommendations are made regarding the establishment of the corporate segment SOPKA, which meets the requirements of Presidential Decree of January 15, 2013 number 31c “On the establishment of the state system of detection, prevention and elimination of the consequences of cyber-attacks on information resources of the Russian Federation” and “Concept of the state system of detection, prevention and elimination of the consequences of cyber-attacks on information resources of the Russian Federation” approved by the President of the Russian Federation on December 12, 2014, No K 1274.

Demkiv, L., Lozynskyy, A., Lozynskyy, O., Demkiv, I..  2017.  A new approach to dynamical system's fuzzy controller synthesis: Application of the unstable subsystem. 2017 International Conference on Modern Electrical and Energy Systems (MEES). :84–87.

A general approach to the synthesis of the conditionally unstable fuzzy controller is introduced in this paper. This approach allows tuning the output signal of the system for both fast and smooth transient. Fuzzy logic allows combining the properties of several strategies of system tuning dependent on the state of the system. The utilization of instability allows achieving faster transient when the error of the system output is beyond the predefined value. Later the system roots are smoothly moved to the left-hand side of the complex s-plane due to the change of the membership function values. The results of the proposed approaches are compared with the results obtained using traditional methods of controller synthesis.

Rahman, Fatin Hamadah, Au, Thien Wan, Newaz, S. H. Shah, Suhaili, Wida Susanty.  2017.  Trustworthiness in Fog: A Fuzzy Approach. Proceedings of the 2017 VI International Conference on Network, Communication and Computing. :207–211.

Trust management issue in cloud domain has been a persistent research topic discussed among scholars. Similar issue is bound to occur in the surfacing fog domain. Although fog and cloud are relatively similar, evaluating trust in fog domain is more challenging than in cloud. Fog's high mobility support, distributive nature, and closer distance to end user means that they are likely to operate in vulnerable environments. Unlike cloud, fog has little to no human intervention, and lack of redundancy. Hence, it could experience downtime at any given time. Thus it is harder to trust fogs given their unpredictable status. These distinguishing factors, combined with the existing factors used for trust evaluation in cloud can be used as metrics to evaluate trust in fog. This paper discusses a use case of a campus scenario with several fog servers, and the metrics used in evaluating the trustworthiness of the fog servers. While fuzzy logic method is used to evaluate the trust, the contribution of this study is the identification of fuzzy logic configurations that could alter the trust value of a fog.

Abdulqadder, I. H., Zou, D., Aziz, I. T., Yuan, B..  2017.  Modeling software defined security using multi-level security mechanism for SDN environment. 2017 IEEE 17th International Conference on Communication Technology (ICCT). :1342–1346.

Software Defined Networking (SDN) support several administrators for quicker access of resources due to its manageability, cost-effectiveness and adaptability. Even though SDN is beneficial it also exists with security based challenges due to many vulnerable threats. Participation of such threats increases their impact and risk level. In this paper a multi-level security mechanism is proposed over SDN architecture design. In each level the flow packet is analyzed using different metric and finally it reaches a secure controller for processing. Benign flow packets are differentiated from non-benign flow by means of the packet features. Initially routers verify user, secondly policies are verified by using dual-fuzzy logic design and thirdly controllers are authenticated using signature based authentication before assigning flow packets. This work aims to enhance entire security of developed SDN environment. SDN architecture is implemented in OMNeT++ simulation tool that supports OpenFlow switches and controllers. Finally experimental results show better performances in following performance metrics as throughput, time consumption and jitter.

Velásquez, E. P., Correa, J. C..  2017.  Methodology (N2FMEA) for the detection of risks associated with the components of an underwater system. OCEANS 2017 - Anchorage. :1–4.

This paper combines FMEA and n2 approaches in order to create a methodology to determine risks associated with the components of an underwater system. This methodology is based on defining the risk level related to each one of the components and interfaces that belong to a complex underwater system. As far as the authors know, this approach has not been reported before. The resulting information from the mentioned procedures is combined to find the system's critical elements and interfaces that are most affected by each failure mode. Finally, a calculation is performed to determine the severity level of each failure mode based on the system's critical elements.

Mhamdi, L., Njima, C. B., Dhouibi, H., Hassani, M..  2017.  Using timed automata and fuzzy logic for diagnosis of multiple faults in DES. 2017 International Conference on Control, Automation and Diagnosis (ICCAD). :457–463.
This paper proposes a design method of a support tool for detection and diagnosis of failures in discrete event systems (DES). The design of this diagnoser goes through three phases: an identification phase and finding paths and temporal parameters of the model describing the two modes of normal and faulty operation, a detection phase provided by the comparison and monitoring time operation and a location phase based on the combination of the temporal evolution of the parameters and thresholds exceeded technique. Our contribution lays in the application of this technique in the presence of faults arising simultaneously, sensors and actuators. The validation of the proposed approach is illustrated in a filling system through a simulation.
Jahan, Thanveer, Narsimha, G., Rao, C. V. Guru.  2016.  Multiplicative Data Perturbation Using Fuzzy Logic in Preserving Privacy. Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies. :38:1–38:5.

In Data mining is the method of extracting the knowledge from huge amount of data and interesting patterns. With the rapid increase of data storage, cloud and service-based computing, the risk of misuse of data has become a major concern. Protecting sensitive information present in the data is crucial and critical. Data perturbation plays an important role in privacy preserving data mining. The major challenge of privacy preserving is to concentrate on factors to achieve privacy guarantee and data utility. We propose a data perturbation method that perturbs the data using fuzzy logic and random rotation. It also describes aspects of comparable level of quality over perturbed data and original data. The comparisons are illustrated on different multivariate datasets. Experimental study has proved the model is better in achieving privacy guarantee of data, as well as data utility.

Ragmani, Awatif, El Omri, Amina, Abghour, Noreddine, Moussaid, Khalid, Rida, Mohammed.  2016.  An Improved Scheduling Strategy in Cloud Computing Using Fuzzy Logic. Proceedings of the International Conference on Big Data and Advanced Wireless Technologies. :22:1–22:9.

Within few years, Cloud computing has emerged as the most promising IT business model. Thanks to its various technical and financial advantages, Cloud computing continues to convince every day new users coming from scientific and industrial sectors. To satisfy the various users' requirements, Cloud providers must maximize the performance of their IT resources to ensure the best service at the lowest cost. The performance optimization efforts in the Cloud can be achieved at different levels and aspects. In the present paper, we propose to introduce a fuzzy logic process in scheduling strategy for public Cloud in order to improve the response time, processing time and total cost. In fact, fuzzy logic has proven his ability to solve the problem of optimization in several fields such as data mining, image processing, networking and much more.

Dhand, Pooja, Mittal, Sumit.  2016.  Smart Handoff Framework for Next Generation Heterogeneous Networks in Smart Cities. Proceedings of the International Conference on Advances in Information Communication Technology & Computing. :75:1–75:7.

Over the last few decades, accessibility scenarios have undergone a drastic change. Today the way people access information and resources is quite different from the age when internet was not evolved. The evolution of the Internet has made remarkable, epoch-making changes and has become the backbone of smart city. The vision of smart city revolves around seamless connectivity. Constant connectivity can provide uninterrupted services to users such as e-governance, e-banking, e-marketing, e-shopping, e-payment and communication through social media. And to provide uninterrupted services to such applications to citizens is our prime concern. So this paper focuses on smart handoff framework for next generation heterogeneous networks in smart cities to provide all time connectivity to anyone, anyhow and anywhere. To achieve this, three strategies have been proposed for handoff initialization phase-Mobile controlled, user controlled and network controlled handoff initialization. Each strategy considers a different set of parameters. Results show that additional parameters with RSSI and adaptive threshold and hysteresis solve ping-pong and corner effect problems in smart city.

Djellali, Choukri, Adda, Mehdi.  2016.  A New Scalable Aggregation Scheme for Fuzzy Clustering Taking Unstructured Textual Resources As a Case. Proceedings of the 20th International Database Engineering & Applications Symposium. :199–204.

The performance of clustering is a crucial challenge, especially for pattern recognition. The models aggregation has a positive impact on the efficiency of Data clustering. This technique is used to obtain more cluttered decision boundaries by aggregating the resulting clustering models. In this paper, we study an aggregation scheme to improve the stability and accuracy of clustering, which allows to find a reliable and robust clustering model. We demonstrate the advantages of our aggregation method by running Fuzzy C-Means (FCM) clustering on Reuters-21578 corpus. Experimental studies showed that our scheme optimized the bias-variance on the selected model and achieved enhanced clustering for unstructured textual resources.

Yingying, Xu, Chao, Liu, Tao, Tang.  2016.  Research on Risk Assessment of CTCS Based on Fuzzy Reasoning and Analytic Hierarchy Process. Proceedings of the 2016 International Conference on Intelligent Information Processing. :31:1–31:7.

In this paper, we describe the formatting guidelines for ACM SIG Proceedings. In order to assure safety of Chinese Train Control System (CTCS), it is necessary to ensure the operational risk is acceptable throughout its life-cycle, which requires a pragmatic risk assessment required for effective risk control. Many risk assessment techniques currently used in railway domain are qualitative, and rely on the experience of experts, which unavoidably brings in subjective judgements. This paper presents a method that combines fuzzy reasoning and analytic hierarchy process approach to quantify the experiences of experts to get the scores of risk parameters. Fuzzy reasoning is used to obtain the risk of system hazard, analytic hierarchy process approach is used to determine the risk level (RL) and its membership of the system. This method helps safety analyst to calculate overall collective risk level of system. A case study of risk assessment of CTCS system is used to demonstrate this method can give quantitative result of collective risks without much information from experts, but can support the risk assessment with risk level and its membership, which are more valuable to guide the further risk management.

Singh, Tanya, Verma, Seema, Kulshrestha, Vartika, Katiyar, Sumeet.  2016.  Intrusion Detection System Using Genetic Algorithm for Cloud. Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies. :115:1–115:6.

Cloud and its transactions have emerged as a major challenge. This paper aims to come up with an efficient and best possible way to transfer data in cloud computing environment. This goal is achieved with the help of Soft Computing Techniques. Of the various techniques such as fuzzy logic, genetic algorithm or neural network, the paper proposes an effective method of intrusion detection using genetic algorithm. The selection of the optimized path for the data transmission proved to be effective method in cloud computing environment. Network path optimization increases data transmission speed making intrusion in network nearly impossible. Intruders are forced to act quickly for intruding the network which is quite a tough task for them in such high speed data transmission network.