An Approach to Mitigate Malware Attacks Using Netfilter's Hybrid Frame in Firewall Security

2018 ◽  
Vol 9 (1) ◽  
pp. 32-61 ◽  
Author(s):  
Nivedita Nahar ◽  
Prerna Dewan ◽  
Rakesh Kumar
Keyword(s):  
Source Code ◽  
Research Work ◽  
Hybrid Approach ◽  
Optimal Level ◽  
Malicious Code ◽  
Traffic Load ◽  
Activity Detection ◽  
Web Content ◽  
Packet Filtering ◽  
Malicious Activity

With the steady advancements in the technology, the network security is really important these days to protect information from attackers. In this research, the main focus is on designing strong firewall filtering rules so that detection of malicious code is achieved to an optimal level. A proposed framework is introduced to improve the performance parameters such as Server response time, Web content analysis, Bandwidth, and the performance of the Network traffic load. This research work defines a new set of IPtable rules achieved by modifying the kernel source code. This is done using OpenBSD kernel source code, which results in the formation of a mini-firewall. Therefore, a new hybrid approach is proposed by adding packet filtering rules and SNORT technology in mini-firewall for malicious activity detection. It is an efficient and practical technique which will be helpful to mitigate the malware attacks and secure LAMP server. Experimental analysis has been done to conclude that around 70-75% malicious activity can be reduced by using the proposed technique.

An Energy Efficient Spectrum Aware Routing for Cognitive Radio Networks (CRNs)

2020 ◽  
Vol 10 ◽  
Author(s):  
Dileep Reddy Bolla ◽  
Jijesh J J ◽  
Mahaveer Penna ◽  
Shiva Shankar
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Energy Efficient ◽  
Research Work ◽  
Spectrum Management ◽  
Residual Energy ◽  
Radio Networks ◽  
Traffic Load ◽  
The Impact

Back Ground/ Aims:: Now-a-days in the Wireless Communications some of the spectrum bands are underutilized or unutilized; the spectrum can be utilized properly by using the Cognitive Radio Techniques using the Spectrum Sensing mechanisms. Objectives:: The prime objective of the research work carried out is to achieve the energy efficiency and to use the spectrum effectively by using the spectrum management concept and achieve better throughput, end to end delay etc., Methods:: The detection of the spectrum hole plays a vital role in the routing of Cognitive Radio Networks (CRNs). While detecting the spectrum holes and the routing, sensing is impacted by the hidden node issues and exposed node issues. The impact of sensing is improved by incorporating the Cooperative Spectrum Sensing (CSS) techniques. Along with these issues the spectrum resources changes time to time in the routing. Results:: All the issues are addressed with An Energy Efficient Spectrum aware Routing (EESR) protocol which improves the timeslot and the routing schemes. The overall network life time is improved with the aid of residual energy concepts and the overall network performance is improved. Conclusion:: The proposed protocol (EESR) is an integrated system with spectrum management and the routing is successfully established to communication in the network and further traffic load is observed to be balanced in the protocol based on the residual energy in a node and further it improves the Network Lifetime of the Overall Network and the Individual CR user, along with this the performance of the proposed protocol outperforms the conventional state of art routing protocols.

scholarly journals Recommendation Systems for Education: Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1611
Author(s):  
María Cora Urdaneta-Ponte ◽  
Amaia Mendez-Zorrilla ◽  
Ibon Oleagordia-Ruiz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Formal Education ◽  
Research Work ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Educational Resources ◽  
Future Research ◽  
Collaborative Approach ◽  
Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

Voice activity detection using optimal window overlapping especially over health-care infrastructure

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shilpa Sharma ◽  
Punam Rattan ◽  
Anurag Sharma ◽  
Mohammad Shabaz
Keyword(s):  
Health Care ◽  
Health Care Professionals ◽  
Research Work ◽  
Voice Activity Detection ◽  
Support Vector ◽  
Activity Detection ◽  
Content Type ◽  
Health Care Infrastructure ◽  
Voice Activity ◽  
Optimal Window

Purpose This paper aims to introduce recently an unregulated unsupervised algorithm focused on voice activity detection by data clustering maximum margin, i.e. support vector machine. The algorithm for clustering K-mean used to solve speech behaviour detection issues was later applied, the application, therefore, did not permit the identification of voice detection. This is critical in demands for speech recognition. Design/methodology/approach Here, the authors find a voice activity detection detector based on a report provided by a K-mean algorithm that permits sliding window detection of voice and noise. However, first, it needs an initial detection pause. The machine initialized by the algorithm will work on health-care infrastructure and provides a platform for health-care professionals to detect the clear voice of patients. Findings Timely usage discussion on many histories of NOISEX-92 var reveals the average non-speech and the average signal-to-noise ratios hit concentrations which are higher than modern voice activity detection. Originality/value Research work is original.

scholarly journals Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series

2021 ◽  
Author(s):  
Sidra Mehtab ◽  
Jaydip Sen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Research Work ◽  
Hybrid Approach ◽  
Forecast Horizon ◽  
Stock Price Prediction ◽  
Price Prediction

Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modelled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of four years: 2015 – 2018. Based on the NIFTY data during 2015 – 2018, we build various predictive models using machine learning approaches, and then use those models to predict the “Close” value of NIFTY 50 for the year 2019, with a forecast horizon of one week, i.e., five days. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual “Close” values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network (CNN) with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.

Temporal pattern-based malicious activity detection in SCADA systems

2021 ◽  
Vol 102 ◽  
pp. 102153
Author(s):  
Amit Shlomo ◽  
Meir Kalech ◽  
Robert Moskovitch
Keyword(s):  
Temporal Pattern ◽  
Activity Detection ◽  
Malicious Activity ◽  
Scada Systems

scholarly journals LHDiff: A Language-Independent Hybrid Approach for Tracking Source Code Lines

2013 ◽  
Author(s):  
Muhammad Asaduzzaman ◽  
Chanchal K. Roy ◽  
Kevin A. Schneider ◽  
Massimiliano Di Penta
Keyword(s):  
Source Code ◽  
Hybrid Approach

Blind Automatic Malicious Activity Detection in Honeypot Data

2011 ◽  
Author(s):  
Bernardo David ◽  
João Costa ◽  
Anderson Nascimento ◽  
Marcelo Holtz ◽  
Dino Amaral ◽  
...  
Keyword(s):  
Activity Detection ◽  
Malicious Activity

Neuro Fuzzy Modeling of Laser Beam Cutting Process

2011 ◽  
Vol 110-116 ◽  
pp. 4109-4117 ◽  
Author(s):  
Arun Kumar Pandey ◽  
Avanish Kumar Dubey
Keyword(s):  
Laser Beam ◽  
Research Work ◽  
Hybrid Approach ◽  
Fuzzy Model ◽  
Kerf Width ◽  
Cutting Process ◽  
Neuro Fuzzy ◽  
Reliable Model ◽  
Artificial Neural Network Ann ◽  
Experimental Values

Laser Beam Cutting (LBC) being a complex cutting process needs a reliable model for prediction of the process performance. This research work presents a modeling study of LBC process. A hybrid approach of Artificial Neural Network (ANN) and Fuzzy Logic (FL) has been used for developing the Kerf width model. The developed Neuro Fuzzy model of Kerf width has also been compared with Response Surface Methodology (RSM) based model and it has been found that the values of Kerf width predicted by the Neuro Fuzzy Model are more closer to the experimental values.

scholarly journals A Hybrid Approach for Noise Reduction in Acoustic Signal of Machining Process Using Neural Networks and ARMA Model

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8023
Author(s):  
Tayyab Zafar ◽  
Khurram Kamal ◽  
Senthan Mathavan ◽  
Ghulam Hussain ◽  
Mohammed Alkahtani ◽  
...  
Keyword(s):  
Neural Network ◽  
Background Noise ◽  
Manufacturing Systems ◽  
Signal Reconstruction ◽  
Research Work ◽  
Hybrid Approach ◽  
Arma Model ◽  
Machining Process ◽  
Depth Of Cut ◽  
Milling Machine

Intelligent machining has become an important part of manufacturing systems because of the increased demand for productivity. Tool condition monitoring is an integral part of these systems. Airborne acoustic emission from the machining process is a vital indicator of tool health, however, it is highly affected by background noise. Reducing the background noise helps in developing a low-cost system. In this research work, a feedforward neural network is used as an adaptive filter to reduce the background noise. Acoustic signals from four different machines in the background are acquired and are introduced to a machining signal at different speeds and feed-rates at a constant depth of cut. These four machines are a three-axis milling machine, a four-axis mini-milling machine, a variable speed DC motor, and a grinding machine. The backpropagation neural network shows an accuracy of 75.82% in classifying the background noise. To reconstruct the filtered signal, a novel autoregressive moving average (ARMA)-based algorithm is proposed. An average increase of 71.3% in signal-to-noise ratio (SNR) is found before and after signal reconstruction. The proposed technique shows promising results for signal reconstruction for the machining process.

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