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Kumar, Sudesh
- Analyzing the Effect of Adding Noise on Compressed Textual Data
Abstract Views :206 |
PDF Views:2
Authors
Affiliations
1 Thapar University, Patiala, IN
2 CSED, Thapar University, Patiala, IN
1 Thapar University, Patiala, IN
2 CSED, Thapar University, Patiala, IN
Source
Data Mining and Knowledge Engineering, Vol 1, No 3 (2009), Pagination: 93-96Abstract
Compression is one of the techniques for better utilization of storage devices, resulting in saving of storage space.This paper addresses compression by using the technique called Normalized Compression Distance (NCD). The Normalized Compression Distance is based on algorithmic complexity developed by Kolmogorov, called Normalized Information Distance.Normalized Compression Distance can be used to cluster objects of any kind, such as music, texts, or gene sequences (microarray classification). The NCD between two binary strings is defined in terms of compressed sizes of the two strings and of their concatenation; it is designed to be an effective approximation of the non computable but universal Kolmogorov distance between two strings. This paper studies the influence of noise on the normalized compression distance, a measure based on the use of compressors to compute the degree of similarity of two files. This influence is approximated by a first order differential equation which gives rise to a complex effect, which explains the fact that the NCD may give values greater than 1. Finally, the analyzing the effect of adding noise on compressed textual data and findings are that NCD performs well even in the presence of quite high noise levels by using CompLearn Toolkit.Keywords
Kolmogorov Complexity, Normalized Information Distance, Normalized Compression Distance, Compression.- Feature Selection for Text Clustering and Classification
Abstract Views :159 |
PDF Views:2
Authors
Affiliations
1 Thapar University, Patiala, IN
2 CSED, Thapar University, Patiala, IN
1 Thapar University, Patiala, IN
2 CSED, Thapar University, Patiala, IN
Source
Data Mining and Knowledge Engineering, Vol 1, No 3 (2009), Pagination: 97-101Abstract
The quality of the data is one of the most important factors influencing the performance of any classification or clustering algorithm. The attributes defining the feature space of a given data set can often be inadequate, which make it difficult to discover useful information or desired output. However, even when the original attributes are individually inadequate, it is often possible to combine such attributes in order to construct new ones with greater predictive power. Feature selection, as a preprocessing step to machine learning, has been very effective in reducing dimensionality, removing irrelevant data, and noise from data to improving result comprehensibility. This paper addresses the task of feature selection for clustering and classification. Here we give a comparative study of variety of classification methods, including Naive Bayes, J48 etc.Keywords
Classification, Clustering, Feature Selection, Machine Learning.- Digital Seismic Network:To Map Himalayan Orogen and Seismic Hazard
Abstract Views :261 |
PDF Views:66
Authors
D. Srinagesh
1,
Prantik Mandal
1,
R. Vijaya Raghavan
1,
Sandeep Gupta
1,
G. Suresh
1,
D. Srinivas
1,
Satish Saha
1,
M. Sekhar
1,
K. Sivaram
1,
Sudesh Kumar
1,
P. Solomon Raju
1,
A. N. S. Sarma
1,
Y. V. V. S. B. Murthy
1,
N. K. Borah
1,
B. Naresh
1,
B. N. V. Prasad
1,
V. M. Tiwari
1
Affiliations
1 CSIR-National Geophysical Research Institute, Hyderabad 500 007, IN
1 CSIR-National Geophysical Research Institute, Hyderabad 500 007, IN
Source
Current Science, Vol 116, No 4 (2019), Pagination: 518-519Abstract
According to the Gutenberg–Richter law1, at least one earthquake of magnitude greater than 7 occurs every month along the seismically active belts in the world. Earthquakes are the manifestation of fault slip at depths, thus, there is no direct method to measure or observe them. However, seismometers can record ground velocity or acceleration caused by the occurrence of an earthquake when a fault slip occurs at depth. Therefore, setting up a seismic network is inevitable to understand the physics of earthquake processes, thereby, mitigating earthquake hazard.References
- Gutenberg, B. and Richter, C. F., Ann. Geofis., 1956, 9, 1–15.
- Ambraseys, N. N. and Jackson, D., Curr. Sci., 2003, 84, 570–582.
- Gupta, H. and Gahalaut, V. K., Gondwana Res., 2014, 25, 204–213.
- Ader, T. et al., J. Geophys. Res., 2012, 117, 23–40.
- Bilham, R., Nature Geosci., 2015, 8, 582– 584.
- Review on Machine Learning Based Malware Detection
Abstract Views :108 |
PDF Views:0
Authors
Lubna Javaid
1,
Sudesh Kumar
2
Affiliations
1 Student, SoCSE, SMVDU, Katra, IN
2 Assistant Professor, SoCSE, SMVDU, Katra, IN
1 Student, SoCSE, SMVDU, Katra, IN
2 Assistant Professor, SoCSE, SMVDU, Katra, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 72-79Abstract
Malware detection using machine learning has gained significant attention in recent years due to the increasing number of malware attacks. With the increasing use of mobile devices, the need for effective malware detection techniques has become even more critical. Machine learning has emerged as a promising approach for detecting malware, as it can learn to identify patterns in large datasets and classify them as either benign or malicious. Previous research in this area has mainly focused on the detection of Android malware using static and dynamic analysis techniques. This review paper examines the efficiency of machine learning for malware identification, with a focus on the latest research in the field. The paper presents an analysis of the various machine learning algorithms used for identification of malware, their strengths and limitations, and the evaluation metrics used for measuring the performance of these methods. Overall, this review paper provides insights into the novelty in machine learning-based malware identification and highlights the need for further research in this field to build more potent and effective techniques for detecting unknown or zero-day attacks.Keywords
Malware Detection, Machine Learning, Benign, Malicious Files.References
- D. Arp, M. Spreitzenbarth, M. Hübner, H. Gascon, and K. Rieck, ‘DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket’, 2014. [Online]. Available: http://dx.doi.org/doi-info-to-be-provided-later
- J. Li et al., ‘Networked human motion capture system based on quaternion navigation’, in BodyNets International Conference on Body Area Networks, 2017. doi: 10.1145/0000000.0000000.
- J. Velasco-Mata, V. Gonzalez-Castro, E. F. Fernandez, and E. Alegre, ‘Efficient Detection of Botnet Traffic by Features Selection and Decision Trees’, IEEE Access, vol. 9, pp. 120567–120579, 2021, doi: 10.1109/ACCESS.2021.3108222.
- N. Martins, J. M. Cruz, T. Cruz, and P. Henriques Abreu, ‘Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review’, IEEE Access, vol. 8. Institute of Electrical and Electronics Engineers Inc., pp. 35403–35419, 2020. doi: 10.1109/ACCESS.2020.2974752.
- E. Odat and Q. M. Yaseen, ‘A Novel Machine Learning Approach for Android Malware Detection Based on the Co-Existence of Features’, IEEE Access, vol. 11, pp. 15471–15484, 2023, doi: 10.1109/ACCESS.2023.3244656.
- K. Shaukat, S. Luo, V. Varadharajan, I. A. Hameed, and M. Xu, ‘A Survey on Machine Learning Techniques for Cyber Security in the Last Decade’, IEEE Access, vol. 8, pp. 222310–222354, 2020, doi: 10.1109/ACCESS.2020.3041951.
- R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, and S. Venkatraman, ‘Robust Intelligent Malware Detection Using Deep Learning’, IEEE Access, vol. 7, pp. 46717–46738, 2019, doi: 10.1109/ACCESS.2019.2906934.
- T.-L. Wan et al., ‘Efficient Detection and Classification of Internet-of-Things Malware Based on Byte Sequences from Executable Files’, IEEE Open Journal of the Computer Society, vol. 1, pp. 262–275, Oct. 2020, doi: 10.1109/ojcs.2020.3033974.
- H. Yang, S. Li, X. Wu, H. Lu, and W. Han, ‘A Novel Solutions for Malicious Code Detection and Family Clustering Based on Machine Learning’, IEEE Access, vol. 7, pp. 148853–148860, 2019, doi: 10.1109/ACCESS.2019.2946482.
- A. Mahindru and A. L. Sangal, ‘SemiDroid: a behavioral malware detector based on unsupervised machine learning techniques using feature selection approaches’, International Journal of Machine Learning and Cybernetics, vol. 12, no. 5, pp. 1369–1411, May 2021, doi: 10.1007/s13042-020-01238-9.
- V. Kouliaridis and G. Kambourakis, ‘A comprehensive survey on machine learning techniques for android malware detection’, Information (Switzerland), vol. 12, no. 5, 2021, doi: 10.3390/info12050185.