Faculty Publications
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Publications by NITK Faculty
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Item On Human Identification Using Running Patterns: A Straightforward Approach(Springer Verlag service@springer.de, 2020) Anusha, R.; Jaidhar, C.D.Gait is a promising biometric for which various methods have been developed to recognize individuals by the pattern of their walking. Nevertheless, the possibility of identifying individuals by using their running video remains largely unexplored. This paper proposes a new and simple method that extends the feature based approach to recognize people by the way they run. In this work, 12 features were extracted from each image of a gait cycle. These are statistical, texture based and area based features. The Relief feature selection method is employed to select the most relevant features. These selected features are classified using k-NN (k-Nearest Neighbor) classifier. The experiments are carried out on KTH and Weizmann database. The obtained experimental results demonstrate the efficiency of the proposed method. © 2020, Springer Nature Switzerland AG.Item Automated multi-level malware detection system based on reconstructed semantic view of executables using machine learning techniques at VMM(Elsevier B.V., 2018) M.a, A.K.; Jaidhar, C.D.In order to fulfill the requirements like stringent timing restraints and demand on resources, Cyber–Physical System (CPS) must deploy on the virtualized environment such as cloud computing. To protect Virtual Machines (VMs) in which CPSs are functioning against malware-based attacks, malware detection and mitigation technique is emerging as a highly crucial concern. The traditional VM-based anti-malware software themselves a potential target for malware-based attack since they are easily subverted by sophisticated malware. Thus, a reliable and robust malware monitoring and detection systems are needed to detect and mitigate rapidly the malware based cyber-attacks in real time particularly for virtualized environment. The Virtual Machine Introspection (VMI) has emerged as a fine-grained out-of-VM security solution to detect malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS) by functioning at the Virtual Machine Monitor (VMM) or hypervisor. However, the reconstructed semantic details by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, extensive manual analysis is required by the existing out-of-VM security solutions. To address the foremost issue, in this paper, we propose an advanced VMM-based guest-assisted Automated Multilevel Malware Detection System (AMMDS) that leverages both VMI and Memory Forensic Analysis (MFA) techniques to predict early symptoms of malware execution by detecting stealthy hidden processes on a live guest OS. More specifically, the AMMDS system detects and classifies the actual running malicious executables from the semantically reconstructed process view of the guest OS. The two sub-components of the AMMDS are: Online Malware Detector (OMD) and Offline Malware Classifier (OFMC). The OMD recognizes whether the running processes are benign or malicious using its Local Malware Signature Database (LMSD) and online malware scanner and the OFMC classify unknown malware by adopting machine learning techniques at the hypervisor. The AMMDS has been evaluated by executing large real-world malware and benign executables on to the live guest OSs. The evaluation results achieved 100% of accuracy and zero False Positive Rate (FPR) on the 10-fold cross-validation in classifying unknown malware with maximum performance overhead of 5.8%. © 2017 Elsevier B.V.Item Windows malware detection system based on LSVC recommended hybrid features(Springer-Verlag France 22, Rue de Palestro Paris 75002, 2019) Shiva Darshan, S.L.; Jaidhar, C.D.To combat exponentially evolved modern malware, an effective Malware Detection System and precise malware classification is highly essential. In this paper, the Linear Support Vector Classification (LSVC) recommended Hybrid Features based Malware Detection System (HF-MDS) has been proposed. It uses a combination of the static and dynamic features of the Portable Executable (PE) files as hybrid features to identify unknown malware. The application program interface calls invoked by the PE files during their execution along with their correspondent category are collected and considered as dynamic features from the PE file behavioural report produced by the Cuckoo Sandbox. The PE files’ header details such as optional header, disk operating system header, and file header are treated as static features. The LSVC is used as a feature selector to choose prominent static and dynamic features from their respective Original Feature Space. The features recommended by the LSVC are highly discriminative and used as final features for the classification process. Different sets of experiments were conducted using real-world malware samples to verify the combination of static and dynamic features, which encourage the classifier to attain high accuracy. The tenfold cross-validation experimental results demonstrate that the proposed HF-MDS is proficient in precisely detecting malware and benign PE files by attaining detection accuracy of 99.743% with sequential minimal optimization classifier consisting of hybrid features. © 2018, Springer-Verlag France SAS, part of Springer Nature.Item Experimental analysis of Android malware detection based on combinations of permissions and API-calls(Springer-Verlag France 22, Rue de Palestro Paris 75002, 2019) Singh, A.K.; Jaidhar, C.D.; M.a, M.A.A.Android-based smartphones are gaining popularity, due to its cost efficiency and various applications. These smartphones provide the full experience of a computing device to its user, and usually ends up being used as a personal computer. Since the Android operating system is open-source software, many contributors are adding to its development to make the interface more attractive and tweaking the performance. In order to gain more popularity, many refined versions are being offered to customers, whose feedback will enable it to be made even more powerful and user-friendly. However, this has attracted many malicious code-writers to gain anonymous access to the user’s private data. Moreover, the malware causes an increase of resource consumption. To prevent this, various techniques are currently being used that include static analysis-based detection and dynamic analysis-based detection. But, due to the enhancement in Android malware code-writing techniques, some of these techniques are getting overwhelmed. Therefore, there is a need for an effective Android malware detection approach for which experimental studies were conducted in the present work using the static features of the Android applications such as Standard Permissions with Application Programming Interface (API) calls, Non-standard Permissions with API-calls, API-calls with Standard and Nonstandard Permissions. To select the prominent features, Feature Selection Techniques (FSTs) such as the BI-Normal Separation (BNS), Mutual Information (MI), Relevancy Score (RS), and the Kullback-Leibler (KL) were employed and their effectiveness was measured using the Linear-Support Vector Machine (L-SVM) classifier. It was observed that this classifier achieved Android malware detection accuracy of 99.6% for the combined features as recommended by the BI-Normal Separation FST. © 2019, Springer-Verlag France SAS, part of Springer Nature.Item Clothing invariant human gait recognition using modified local optimal oriented pattern binary descriptor(Springer, 2020) Anusha, R.; Jaidhar, C.D.Human gait is a behavioral characteristic which has received a large amount of consideration in recent times as a biometric identifier. The clothing variance is one of the most common covariate influences which can influence the performance of gait recognition approach in real-world scenarios. This paper proposes a gait recognition approach proficient in choosing information characteristics for individual identification under different clothing conditions. The proposed method constitutes of addressing the feature extraction technique by introducing a binary descriptor called as Modified Local Optimal Oriented Pattern (MLOOP). In the proposed approach, initially, the feature vectors such as histogram and horizontal width vector are extracted from MLOOP descriptor, and then the dimensionality of the feature vector is reduced to remove the irrelevant features. The performance of MLOOP was accessed against its predecessors. Obtained experimental results demonstrate that the MLOOP descriptor performs better than the previous binary descriptors. Furthermore, the performance analysis of the proposed approach was assessed on OU-ISIR B treadmill gait database and CASIA B gait database. Broad investigations demonstrate the viability of the proposed technique. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.Item An empirical study to estimate the stability of random forest classifier on the hybrid features recommended by filter based feature selection technique(Springer, 2020) Shiva Darshan, S.L.S.; Jaidhar, C.D.The emergence of advanced malware is a serious threat to information security. A prominent technique that identifies sophisticated malware should consider the runtime behaviour of the source file to detect malicious intent. Although the behaviour-based malware detection technique is a substantial improvement over the traditional signature-based detection technique, current malware employs code obfuscation techniques to elude detection. This paper presents the Hybrid Features-based malware detection system (HFMDS) that integrates static and dynamic features of the portable executable (PE) files to discern malware. The HFMDS is trained with prominent features advised by the filter-based feature selection technique (FST). The detection ability of the proposed HFMDS has evaluated with the random forest (RF) classifier by considering two different datasets that consist of real-world Windows malware samples. In-depth analysis is carried out to determine the optimal number of decision trees (DTs) required by the RF classifier to achieve consistent accuracy. Besides, four popular FSTs performance is also analyzed to determine which FST recommends the best features. From the experimental analysis, we can infer that increasing the number of DTs after 160 within the RF classifier does not make a significant difference in attaining better detection accuracy. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.Item Human gait recognition based on histogram of oriented gradients and Haralick texture descriptor(Springer, 2020) Anusha, R.; Jaidhar, C.D.Gait recognition is an evolving technology in the biometric domain; it aims to recognize people through an analysis of their walking pattern. One of the significant challenges of the appearance-based gait recognition system is to augment its performance by using a distinctive low-dimensional feature vector. Therefore, this study proposes the low-dimensional features that are capable of effectively capturing the spatial, gradient, and texture information in this context. These features are obtained by the computation of histogram of oriented gradients, followed by sum variance Haralick texture descriptor from nine cells of gait gradient magnitude image. Further, the performance of the proposed method is validated on five widely used gait databases. They include CASIA A gait database, CASIA B gait database, OU-ISIR D gait database, CMU MoBo database, and KTH video database. The experimental results demonstrated that the proposed approach could choose significant discriminatory features for individual identification and consequently, outperform certain state-of-the-art methods in terms of recognition performance. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.Item Applicability of machine learning in spam and phishing email filtering: review and approaches(Springer Science+Business Media B.V. editorial@springerplus.com, 2020) Gangavarapu, T.; Jaidhar, C.D.; Chanduka, B.With the influx of technological advancements and the increased simplicity in communication, especially through emails, the upsurge in the volume of unsolicited bulk emails (UBEs) has become a severe threat to global security and economy. Spam emails not only waste users’ time, but also consume a lot of network bandwidth, and may also include malware as executable files. Alternatively, phishing emails falsely claim users’ personal information to facilitate identity theft and are comparatively more dangerous. Thus, there is an intrinsic need for the development of more robust and dependable UBE filters that facilitate automatic detection of such emails. There are several countermeasures to spam and phishing, including blacklisting and content-based filtering. However, in addition to content-based features, behavior-based features are well-suited in the detection of UBEs. Machine learning models are being extensively used by leading internet service providers like Yahoo, Gmail, and Outlook, to filter and classify UBEs successfully. There are far too many options to consider, owing to the need to facilitate UBE detection and the recent advances in this domain. In this paper, we aim at elucidating on the way of extracting email content and behavior-based features, what features are appropriate in the detection of UBEs, and the selection of the most discriminating feature set. Furthermore, to accurately handle the menace of UBEs, we facilitate an exhaustive comparative study using several state-of-the-art machine learning algorithms. Our proposed models resulted in an overall accuracy of 99% in the classification of UBEs. The text is accompanied by snippets of Python code, to enable the reader to implement the approaches elucidated in this paper. © 2020, Springer Nature B.V.Item Windows malware detector using convolutional neural network based on visualization images(IEEE Computer Society, 2021) Shiva Darshan, S.L.; Jaidhar, C.D.The evolution of malware is continuing at an alarming rate, despite the efforts made towards detecting and mitigating them. Malware analysis is needed to defend against its sophisticated behaviour. However, the manual heuristic inspection is no longer effective or efficient. To cope with these critical issues, behaviour-based malware detection approaches with machine learning techniques have been widely adopted as a solution. It involves supervised classifiers to appraise their predictive performance on gaining the most relevant features from the original features' set and the trade-off between high detection rate and low computation overhead. Though machine learning-based malware detection techniques have exhibited success in detecting malware, their shallow learning architecture is still deficient in identifying sophisticated malware. Therefore, in this paper, a Convolutional Neural Network (CNN) based Windows malware detector has been proposed that uses the execution time behavioural features of the Portable Executable (PE) files to detect and classify obscure malware. The 10-fold cross-validation tests were conducted to assess the proficiency of the proposed approach. The experimental results showed that the proposed approach was effective in uncovering malware PE files by utilizing significant behavioural features suggested by the Relief Feature Selection Technique. It attained detection accuracy of 97.968 percent. © 2013 IEEE.Item Cardamom Plant Disease Detection Approach Using EfficientNetV2(Institute of Electrical and Electronics Engineers Inc., 2022) Sunil, C.K.; Jaidhar, C.D.; Patil, N.Cardamom is a queen of spices. It is indigenously grown in the evergreen forests of Karnataka, Kerala, Tamil Nadu, and the northeastern states of India. India is the third largest producer of cardamom. Plant diseases cause a catastrophic influence on food production safety; they reduce the eminence and quantum of agricultural products. Plant diseases may cause significantly high loss or no harvest in dreadful cases. Various diseases and pests affect the growth of cardamom plants at different stages and crop yields. This study concentrated on two diseases of cardamom plants, Colletotrichum Blight and Phyllosticta Leaf Spot of cardamom and three diseases of grape, Black Rot, ESCA, and Isariopsis Leaf Spot. Various methods have been proposed for plant disease detection, and deep learning has become the preferred method because of its spectacular accomplishment. In this study, U2-Net was used to remove the unwanted background of an input image by selecting multiscale features. This work proposes a cardamom plant disease detection approach using the EfficientNetV2 model. A comprehensive set of experiments was carried out to ascertain the performance of the proposed approach and compare it with other models such as EfficientNet and Convolutional Neural Network (CNN). The experimental results showed that the proposed approach achieved a detection accuracy of 98.26%. © 2013 IEEE.