International Journal of Computer Network and Information Security最新文献

{"title":"Detection of DDOS Attacks on Cloud Computing Environment Using Altered Convolutional Deep Belief Networks","authors":"S. Sureshkumar, G. K. D. Prasanna Venkatesan, R. Santhosh","doi":"10.5815/ijcnis.2023.05.06","DOIUrl":"https://doi.org/10.5815/ijcnis.2023.05.06","url":null,"abstract":"The primary benefits of Clouds are that they can elastically scale to meet variable demands and provide corresponding environments for computing. Cloud infrastructures require highest levels of protections from DDoS (Distributed Denial-of-Services). Attacks from DDoSs need to be handled as they jeopardize availability of networks. These attacks are becoming very complex and are evolving at rapid rates making it complex to counter them. Hence, this paper proposes GKDPCAs (Gaussian kernel density peak clustering techniques) and ACDBNs (Altered Convolution Deep Belief Networks) to handle these attacks. DPCAs (density peak clustering algorithms) are used to partition training sets into numerous subgroups with comparable characteristics, which help in minimizing the size of training sets and imbalances in samples. Subset of ACDBNs get trained in each subgroup where FSs (feature selections) of this work are executed using SFOs (Sun-flower Optimizations) which evaluate the integrity of reduced feature subsets. The proposed framework has superior results in its experimental findings while working with NSL-KDD and CICIDS2017 datasets. The resulting overall accuracies, recalls, precisions, and F1-scoresare better than other known classification algorithms. The framework also outperforms other IDTs (intrusion detection techniques) in terms of accuracies, detection rates, and false positive rates.","PeriodicalId":36488,"journal":{"name":"International Journal of Computer Network and Information Security","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135196200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flow-aware Segment Routing in SDN-enabled Data Center Networks","authors":"Bommareddy Lokesh, Narendran Rajagopalan","doi":"10.5815/ijcnis.2023.05.05","DOIUrl":"https://doi.org/10.5815/ijcnis.2023.05.05","url":null,"abstract":"The underlying objective of segment routing is to avoid maintenance of the per-flow state at forwarding devices. Segment routing (SR) enables the network devices to minimize their forwarding table size by generalizing the forwarding rules and making them applicable to multiple flows. In existing works, optimizing the trade-off between segment length and the number of co-flows sharing the segment is considered the key to determining optimal segment endpoints. However, the flow characteristics like the lifetime of flows, and dynamically altering routing paths are critical and impact the performance of SR. Ideally, network flows considered for SR are expected to persist for a longer duration and adhere to static routing paths. But our analysis of flow characteristics at a typical data center reveals that the majority of flows are short-lived. Also, network flows are subject to alter their routing paths frequently for several reasons. Considering short-lived flows and flows that dynamically alter their routing paths may lead to choosing unstable segment endpoints. Hence, it is necessary to study the flow characteristics for determining more stable segment endpoints. In this paper, the authors implemented the SR technique considering the flow characteristics at an SDN-enabled data center and the results show a significant improvement with respect to the stability of segment endpoints.","PeriodicalId":36488,"journal":{"name":"International Journal of Computer Network and Information Security","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135196203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ford Fulkerson and Newey West Regression Based Dynamic Load Balancing in Cloud Computing for Data Communication","authors":"Prabhakara B. K., Chandrakant Naikodi, Suresh L.","doi":"10.5815/ijcnis.2023.05.08","DOIUrl":"https://doi.org/10.5815/ijcnis.2023.05.08","url":null,"abstract":"In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.","PeriodicalId":36488,"journal":{"name":"International Journal of Computer Network and Information Security","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135196195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"D2D Communication Using Distributive Deep Learning with Coot Bird Optimization Algorithm","authors":"Nethravathi H. M., Akhila S., Vinayakumar Ravi","doi":"10.5815/ijcnis.2023.05.01","DOIUrl":"https://doi.org/10.5815/ijcnis.2023.05.01","url":null,"abstract":"D2D (Device-to-device) communication has a major role in communication technology with resource and power allocation being a major attribute of the network. The existing method for D2D communication has several problems like slow convergence, low accuracy, etc. To overcome these, a D2D communication using distributed deep learning with a coot bird optimization algorithm has been proposed. In this work, D2D communication is combined with the Coot Bird Optimization algorithm to enhance the performance of distributed deep learning. Reducing the interference of eNB with the use of deep learning can achieve near-optimal throughput. Distributed deep learning trains the devices as a group and it works independently to reduce the training time of the devices. This model confirms the independent resource allocation with optimized power value and the least Bit Error Rate for D2D communication while sustaining the quality of services. The model is finally trained and tested successfully and is found to work for power allocation with an accuracy of 99.34%, giving the best fitness of 80%, the worst fitness value of 46%, mean value of 6.76 and 0.55 STD value showing better performance compared to the existing works.","PeriodicalId":36488,"journal":{"name":"International Journal of Computer Network and Information Security","volume":"298 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135196202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Analysis of 5G New Radio LDPC over Different Multipath Fading Channel Models","authors":"Mohammed Hussein Ali, Ghanim A. Al-Rubaye","doi":"10.5815/ijcnis.2023.04.01","DOIUrl":"https://doi.org/10.5815/ijcnis.2023.04.01","url":null,"abstract":"The creation and developing of a wireless network communication that is fast, secure, dependable, and cost-effective enough to suit the needs of the modern world is a difficult undertaking. Channel coding schemes must be chosen carefully to ensure timely and error-free data transfer in a noisy and fading channel. To ensure that the data received matches the data transmitted, channel coding is an essential part of the communication system's architecture. NR LDPC (New Radio Low Density Parity Check) code has been recommended for the fifth-generation (5G) to achieve the need for more internet traffic capacity in mobile communications and to provide both high coding gain and low energy consumption. This research presents NR-LDPC for data transmission over two different multipath fading channel models, such as Nakagami-m and Rayleigh in AWGN. The BER performance of the NR-LDPC code using two kinds of rate-compatible base graphs has been examined for the QAM-OFDM (Quadrature Amplitude Modulation-Orthogonal Frequency Division Multiplexing) system and compared to the uncoded QAM-OFDM system. The BER performance obtained via Monte Carlo simulation demonstrates that the LDPC works efficiently with two different kinds of channel models: those that do not fade and those that fade and achieves significant BER improvements with high coding gain. It makes sense to use LDPC codes in 5G because they are more efficient for long data transmissions, and the key to a good code is an effective decoding algorithm. The results demonstrated a coding gain improvement of up to 15 dB at 10-3 BER.","PeriodicalId":36488,"journal":{"name":"International Journal of Computer Network and Information Security","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42477823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliable Data Delivery Using Fuzzy Reinforcement Learning in Wireless Sensor Networks","authors":"Sateesh Gudla, Kuda Nageswara Rao","doi":"10.5815/ijcnis.2023.04.09","DOIUrl":"https://doi.org/10.5815/ijcnis.2023.04.09","url":null,"abstract":"Wireless sensor networks (WSNs) has been envisioned as a potential paradigm in sensing technologies. Achieving energy efficiency in a wireless sensor network is challenging since sensor nodes have confined energy. Due to the multi-hop communication, sensor nodes spend much energy re-transmitting dropped packets. Packet loss may be minimized by finding efficient routing paths. In this research, a routing using fuzzy logic and reinforcement learning procedure is designed for WSNs to determine energy-efficient paths; to achieve reliable data delivery. Using the node’s characteristics, the reward is determined via fuzzy logic. For this paper, we employ reinforcement learning to improve the rewards, computed by considering the quality of the link, available free buffer of node, and residual energy. Further, simulation efforts have been made to illustrate the proposed mechanism’s efficacy in energy consumption, delivery delay of the packets, number of transmissions, and lifespan.","PeriodicalId":36488,"journal":{"name":"International Journal of Computer Network and Information Security","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48930078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Encryption Using Binary Key Sequences in Chaotic Cryptosystem","authors":"Vishwas C. G. M., R. Kunte, Varun Yarehalli Chandrappa","doi":"10.5815/ijcnis.2023.04.05","DOIUrl":"https://doi.org/10.5815/ijcnis.2023.04.05","url":null,"abstract":"Transmission of images on the network is considered insecure which has security-related issues. In this paper, to provide security to digital images, an encryption system that uses four chaotic maps for binary key sequence generation is proposed. The system consists of stages namely, confusion, generation of random binary chaotic key sequence, pseudo-random number generation (RN), and diffusion. Keys for encryption are chosen randomly based on a pseudo random generator from the selected chaotic maps by the linear feedback shift register (LFSR). The algorithm achieves good results in terms of NPCR, UACI, and entropy values. The developed cryptosystem resists differential attacks, is sensitive to minor alterations in the keys, and has a large key space.","PeriodicalId":36488,"journal":{"name":"International Journal of Computer Network and Information Security","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44087756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on FormSpring in Textual Modality","authors":"Sahana V., Anil Kumar K. M., Abdulbasit A. Darem","doi":"10.5815/ijcnis.2023.04.04","DOIUrl":"https://doi.org/10.5815/ijcnis.2023.04.04","url":null,"abstract":"Social media usage has increased tremendously with the rise of the internet and it has evolved into the most powerful networking platform of the twenty-first century. However, a number of undesirable phenomena are associated with increased use of social networking, such as cyberbullying (CB), cybercrime, online abuse and online trolling. Especially for children and women, cyberbullying can have severe psychological and physical effects, even leading to self-harm or suicide. Because of its significant detrimental social impact, the detection of CB text or messages on social media has attracted more research work. To mitigate CB, we have proposed an automated cyberbullying detection model that detects and classifies cyberbullying content as either bullying or non-bullying (binary classification model), creating a more secure social media experience. The proposed model uses Natural Language Processing (NLP) techniques and Machine Learning (ML) approaches to assess cyberbullying contents. Our main goal is to assess different machine learning algorithms for their performance in cyberbullying detection based on a labelled dataset from Formspring [1]. Nine popular machine learning classifiers namely Bootstrap Aggregation or Bagging, Stochastic Gradient Descent (SGD), Random Forest (RF), Decision Tree (DT), Linear Support Vector Classifier (Linear SVC), Logistic Regression (LR), Adaptive Boosting (AdaBoost), Multinomial Naive Bayes (MNB) and K-Nearest Neighbour (KNN) are considered for the work. In addition, we have experimented with a feature extraction method namely CountVectorizer to obtain features that aid for better classification. The results show that the classification accuracy of AdaBoost classifier is 86.52% which is found better than all other machine learning algorithms used in this study. The proposed work demonstrates the effectiveness of machine learning algorithms in automatic cyberbullying detection as against the very intense and time-consuming approaches for the same problem, thereby by facilitating easy incorporation of an effective approach as tools across different platforms enabling people to use social media safely.","PeriodicalId":36488,"journal":{"name":"International Journal of Computer Network and Information Security","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135747552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secure Mobile Agent Migration Using Lagrange Interpolation and Fast Fourier Transformation","authors":"P. Kumar, N. Singhal, D. Pandey, A. Vatsa","doi":"10.5815/ijcnis.2023.04.07","DOIUrl":"https://doi.org/10.5815/ijcnis.2023.04.07","url":null,"abstract":"Mobile agent is a processing unit works on the behalf of host computer. Mobile agent with intelligence provides a new computing prototype that is totally different from conventional prototype. Mobile agents are automatically itinerating from one host Computer to another host computer and execute assigned task on the behalf of user in heterogeneous environment under own control. Because mobile agents roam around distributed networks automatically, the security of the agents and platforms is a major concern. The number of mobile agents-based software applications has increased dramatically over the past year. It has also enhanced the security risks associated with such applications. Most protection systems in the mobile agent paradigm focus on platform security and provide few guidelines for mobile agent security, which is still a challenging topic. There is a risk to information carries by mobile agents from the malicious mobile agents who can modify and steal the confidential information. In this paper proposed multilevel authentication framework of mobile agents and platform based on Lagrange interpolation and fast Fourier transformation (LIFFT). In this frame work ‘n’ number of mobile agent have two level of security first level key used authentication and second level of key used for execution of mobile agents.","PeriodicalId":36488,"journal":{"name":"International Journal of Computer Network and Information Security","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48898608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Method of Restoring Parameters of Mobile Agents in a Unified Dynamic Environment Considering Similarity Coefficients","authors":"V. Zavgorodnii, Nadiya Braykovsʹka, O. Yarovyi, A. Zavgorodnya, Viacheslav Liskin, Oleg Mukhin","doi":"10.5815/ijcnis.2023.04.03","DOIUrl":"https://doi.org/10.5815/ijcnis.2023.04.03","url":null,"abstract":"We described the placing of the mobile agents in a dynamic environment. It formed a dynamic environment for a complex of dynamically changing mobile agents. We described a two-level system of connections at the global and local levels of interaction. Such a scheme allows you to support the survivability of a dynamic environment. We presented the model of placement of mobile agents in a dynamic environment, which forms the basis of the trajectory of interaction between mobile agents, enabling us to combine the dynamic environment into a unified dynamic environment. The proposed method for mobile agents parameters restoring in a unified dynamic environment, taking into account the similarity coefficients, allows restore 80% parameters of an inaccessible mobile agent. This indicates the high efficiency of the objects description in the form of a unified dynamic environment in order to maintain its integrity.","PeriodicalId":36488,"journal":{"name":"International Journal of Computer Network and Information Security","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48827792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}