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C. D. Jaidhar

National Institute of Technology Karnataka

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R. Anusha

Bapuji Institute of Engineering and Technology

M. A. Ajay Kumara

Lenoir–Rhyne University

Rajesh Nayak

Manipal Academy of Higher Education

Ravi Singh Pippal

RKDF University

Shashikala Tapaswi

Atal Bihari Vajpayee Indian Institute of Information Technology and Management

Dinesh Naik

National Institute of Technology Karnataka

S. L. Shiva Darshan

National Institute of Technology Warangal

M. Sujatha

National Institute of Technology Karnataka

Shridhar Sanshi

National Institute of Technology Karnataka

C. K. Sunil

National Institute of Technology Karnataka

Cooperative Institutions

National Institute of Technology Karnataka

Atal Bihari Vajpayee Indian Institute of Information Technology and Management

Sardar Vallabhbhai National Institute of Technology Surat

University of Petroleum and Energy Studies

Indian Institute of Technology Roorkee

Manipal Academy of Higher Education

Birla Institute of Technology and Science, Pilani - Goa Campus

Defence Institute of Advanced Technology

Amrita Vishwa Vidyapeetham

India Meteorological Department

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Information gain score computation for N-grams using multiprocessing model

S. L. Shiva Darshan M. A. Ajay Kumara C. D. Jaidhar

Currently, the Internet faces serious threat from malwares, and its propagation may cause great havoc on computers and network security solutions. Several existing anti-malware defensive solutions detect known malware accurately. However, they fail to recognize unseen malware, since most of them rely on signature-based techniques, which are easily evadable using obfuscation or polymorphism technique. Therefore, there is immediate requirement of new techniques that can detect and classify the new malwares. In this context, heuristic analysis is found to be promising, since it is capable of detecting unknown malwares and new variants of current malwares. The N-Gram extraction technique is one such heuristic method commonly used in malware detection. Previous works have witnessed that shorter length N-Grams are easier to extract. In order to identify and remove noisy N-Grams, a popular Feature Selection Technique (FST), namely, Information Gain (IG), which computes score for each N-Gram (feature) in the dataset has been used in this work. N-Grams with the highest IG score are considered as best features, while the remaining N-Grams are neglected. The IG-FST (Information Gain-Feature Selection Technique) is computational resource demanding and takes time to generate IG scores for larger N-Gram datasets, if the processing is to be accomplished in the sequential mode. To address this issue, the present work presents a multiprocessing model that computes IG scores rapidly for larger N-Gram datasets. The proposed model has been designed, implemented, and compared with the sequential mode of IG score computation. The experimental results demonstrate that the proposed multiprocessing model performance is 80% faster than the sequential model of IG score computation.

n-gram

10.1109/iseasp.2017.7976984

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An Efficient Infectious Disease Detection in Plants Using Deep Learning

Studies in computational intelligence (2024)

C. K. Sunil C. D. Jaidhar

10.1007/978-981-97-5204-1_3

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Frontal Gait Recognition based on Hierarchical Centroid Shape Descriptor and Similarity Measurement

R. Anusha C. D. Jaidhar

Gait recognition is an expanding stream in biometrics, intended to recognize individuals through the investigation of their walking pattern. This pattern is obtained from a distance, without the active participation of the people. One of the difficulties of the appearance-based gait approach is to enhance the performance of frontal gait recognition, as it carries less spatial and temporal data when compared with other view variations. As a result, to increase the performance of the frontal gait recognition, this paper presents a method which uses two-step procedure; the Hierarchical centroid Shape descriptor (HCSD) and the similarity measurement. The proposed method was assessed on the broadly used CASIA A, CASIA B, and CMU MoBo gait databases. The experimental outcomes showed that the proposed method gave promising results and outperforms certain state-of-the-art methods in terms of recognition performance.

Centroid

Similarity (geometry)

Hierarchical clustering

10.1109/icdse47409.2019.8971477

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Identifying Humans Through Gait Features

Studies in computational intelligence (2024)

R. Anusha C. D. Jaidhar

10.1007/978-981-97-5204-1_5

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An Empirical Study to Detect the Collision Rate in Similarity Hashing Algorithm Using MD5

Tushaar Gangavarapu C. D. Jaidhar

Similarity Hashing (SimHash) is a widely used locality-sensitive hashing algorithm employed in the detection of similarity, in large-scale data processing, including plagiarism detection and near-duplicate web document detection. Collision resistance is a crucial property of cryptographic hash algorithms that are used to verify the message integrity in internet security applications. A hash function is said to be collision-resistant if it is hard to find two different inputs that hash to the same output. In this paper, we present an empirical study to facilitate the detection of collision rate when SimHash is employed to check the integrity of the message. The analysis was performed using bit sequences with length varying from 2 to 32 and Message Digest 5 (MD5) as the internal hash function. Furthermore, to enable faster collision detection with more significant speedup and efficient space utilization, we parallelized the process using a distributed data-parallel approach with synchronous computation and optimum load balancing. Collision detection is desirable, owing to its applicability in digital signature systems, proof-of-work systems, and distributed content systems. Our empirical study revealed a collision rate of 0% to 0.048% in SimHash (with MD5) with the variation in the length of the bit sequence.

MD5

Locality-sensitive hashing

Speedup

Collision resistance

10.1109/icdse47409.2019.8971481

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An empirical study to estimate the stability of random forest classifier on the hybrid features recommended by filter based feature selection technique

International Journal of Machine Learning and Cybernetics (2019)

S. L. Shiva Darshan C. D. Jaidhar

Executable

10.1007/s13042-019-00978-7

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Classification of Micro-Moment-Based Anomalous Power Consumption Using Transfer Learning

Rajesh Nayak C. D. Jaidhar

The identification of unusual power usage in buildings is crucial for improving energy efficiency. Using an electrical consumption monitoring system can help with energy conservation by identifying unusual energy consumption patterns. This paper suggests a micro-moment-based methodology for detecting abnormal power use. This study makes use of a benchmark dataset called SimDataset, which is used in most of the micro-moment classification-related works. On the images created from the dataset labeled with two classes and five classes, binary and multi-class classifications have both been used. Transfer learning is used by employing pre-trained CNN models, namely DenseNet121, ResNet50V2, and Xception model. The results depicted that the DenseNet121 model has outperformed all other models by giving the best accuracy of 99% and F1-score of 0.984.

Benchmark (surveying)

Transfer of learning

Identification

10.1109/icepe57949.2023.10201605

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Automated digital forensic technique with intrusion detection systems

K. Barhate C. D. Jaidhar

In today's technology, new attacks are emerging day by day which makes the systems insecure even the system wrapped with number of security measures. Intrusion Detection System (IDS) is used to detect the intrusion. Its prime function is to detect the intrusion and respond in timely manner. In other words, IDS function is limited to detection as well as response. The IDS is unable to capture the state of the system when an intrusion is detected. Hence, it fails to preserve the evidences against the attack in original form. To maintain the completeness and reliability of evidence for later examination, new security strategy is very much needed. In this research work, automated Digital Forensic Technique with Intrusion Detection System is proposed. Once an IDS detects an intrusion, it sends an alert message to administrator followed by invoke the digital forensic tool to capture the state of the system. Captured image can be used as evidence in the court of law to prove the damage.

10.1109/iadcc.2013.6514218

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Windows malware detection based on cuckoo sandbox generated report using machine learning algorithm

S. L. Shiva Darshan M. A. Ajay Kumara C. D. Jaidhar

Malicious software or malware has grown rapidly and many anti-malware defensive solutions have failed to detect the unknown malware since most of them rely on signature-based technique. This technique can detect a malware based on a pre-defined signature, which achieves poor performance when attempting to classify unseen malware with the capability to evade detection using various code obfuscation techniques. This growing evasion capability of new and unknown malwares needs to be countered by analyzing the malware dynamically in a sandbox environment, since the sandbox provides an isolated environment for analyzing the behavior of the malware. In this paper, the malware is executed on to the cuckoo sandbox to obtain its run-time behavior. At the end of the execution, the cuckoo sandbox reports the system calls invoked by the malware during execution. However, this report is in JSON format and has to be converted to MIST format to extract the system calls. The collected system calls are structured in the form of N-Grams, which help to build the classifier by using the Information Gain (IG) as a feature selection technique. A comprehensive experiment was conducted to perceive the best fit classifier among the chosen classifiers, including the Bayesian-Logistic-Regression, SPegasos, IB1, Bagging, Part, and J48 defined within the WEKA tool. From the experimental results, the overall best performance for all the selected top N-Grams such as 200, 400, and 600 goes to SPegasos with the highest accuracy, highest True Positive Rate (TPR), and lowest False Positive Rate (FPR).

Sandbox (software development)

C4.5 algorithm

System call

Cuckoo search

10.1109/iciinfs.2016.8262998

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Citations (25)

Human gait recognition based on histogram of oriented gradients and Haralick texture descriptor

Multimedia Tools and Applications (2020)

R. Anusha C. D. Jaidhar

Texture (cosmology)

Feature (linguistics)

10.1007/s11042-019-08469-1

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Citations (24)

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