Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Classification of protein sequences by means of an ensemble classifier with an improved feature selection strategy(Springer Verlag, 2018) Sriram, A.; Sanapala, M.; Patel, R.; Patil, N.With decreasing cost of biological sequencing, the influx of new sequences into biological databases such as NCBI, SwissProt, UniProt is increasing at an ever-growing pace. Annotating these newly sequenced proteins will aid in ground breaking discoveries for developing novel drugs and potential therapies for diseases. Previous work in this field has harnessed the high computational power of modern machines to achieve good prediction quality but at the cost of high dimensionality. To address this disparity, we propose a novel word segmentation-based feature selection strategy to classify protein sequences using a highly condensed feature set. Using an incremental classifier selection strategy was seen to yield better results than all existing methods. The antioxidant protein data curated in the previous work was used in order to facilitate a level ground for evaluation and comparison of results. The proposed method was found to outperform all existing works on this data with an accuracy of 95%. © Springer Nature Singapore Pte Ltd. 2018.Item DNS tunneling detection using machine learning and cache miss properties(Institute of Electrical and Electronics Engineers Inc., 2021) Chowdhary, A.; Bhowmik, M.; Rudra, B.In a DNS Tunneling attack, data or other useful information is embedded within a DNS query and exfiltrated. Such attacks are difficult to detect because DNS is a fundamental protocol and blocking legitimate domain names can lead to an unpleasant experience for the users. Thus, detecting whether the DNS query is exfiltrating data or not is a challenging task. Mimicking genuine queries by the attacker makes this even more difficult. This research work presents two different methods for detecting the DNS Tunneling query and later they are combined to build a DNS Tunneling Attack Detector that can inform the client about a potential attack going on in real time. The first method uses cache misses in a DNS cache server and the second method utilizes machine learning techniques to classify a given DNS query. Overall, with around 93% accuracy of certain Machine Learning classifiers on classifying on a per packet basis along with extra validation from the cache-miss approach, a detector has been developed to accurately report DNS tunneling traffic © 2021 IEEE.Item Process Logo: An Approach for Control-Flow Visualization of Information System Process in Process Mining(Springer Science and Business Media Deutschland GmbH, 2022) Manoj Kumar, M.V.; Bs, B.S.; Sneha, H.R.; Thomas, L.; Annappa, B.; Vishnu Srinivasa Murthy, Y.V.S.This paper proposes a new technique named “Process Logo†for visualizing the causal relationship between the activities of a process (Control flow). Traditional process mining algorithms rely on representing the activity as a sequence of operations modeled using nodes and edges, as the number of activities increases, the representation of the entire control flow becomes quite tedious. Process logo is a compact yet highly informative method for visually representing the process model. It visually summarizes the number of activities, sequence of execution, relative significance, and dependency between activities. It uses a dynamic programming method—sequence alignment and clustering approach with Levenshtein measure as a distance measure. The proposed method is evaluated on the synthetic event log, the experimental result is promising. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item MatchVNE: A Stable Virtual Network Embedding Strategy Based on Matching Theory(Institute of Electrical and Electronics Engineers Inc., 2023) Keerthan Kumar, T.G.K.; Srivastava, A.; Satpathy, A.; Addya, S.K.; Koolagudi, S.G.Network virtualization (NV) can provide greater flexibility, better control, and improved quality of service (QoS) for the existing Internet architecture by enabling heterogeneous virtual network requests (VNRs) to share the substrate network (SN) resources. The efficient assignment of the SN resources catering to the demands of virtual machines (VMs) and virtual links (VLs) of the VNRs is known as virtual network embedding (VNE) and is proven to be NP-Hard. Deviating from the literature, this paper proposes a framework MatchVNE that is focused on maximizing the revenue-to-cost ratio of VNRs by considering a blend of system and topological attributes that better capture the inherent dependencies among the VMs. MatchVNE performs a stable VM embedding using the deferred acceptance algorithm (DAA). The preference of the VMs and servers are generated using a hybrid entropy, and the technique for order of preference by similarity to ideal solution (TOPSIS) based ranking strategy for VMs and servers. The attribute weights are determined using entropy, whereas the server and VM ranking are obtained via TOPSIS. The shortest path, VL-embedding, follows VM-embedding. The simulation results show that MatchVNE outperforms the baselines by achieving a 23% boost in the average revenue-to-cost-ratio and 44% improvement in the average acceptance ratio. © 2023 IEEE.Item A Comparison of Different Signal Processing Techniques for Upper Limb Muscle Activity Onset Detection using Surface Electromyography(Institute of Electrical and Electronics Engineers Inc., 2023) Koppolu, P.K.; Chemmangat, K.This work presents the use of real-time experimental Surface Electromyography (sEMG) signals to determine muscle activity of upper limb by detecting the exact onset and offset timings. Various muscle activity detection methods were evaluated, such as Sample Entropy (SEn), Permutation Entropy (PEn), Amplitude Aware Permutation Entropy (AAPEn), and Integrated Profile (IP). The performance of these methods was compared, and it was found that IP detects muscle activity quickly and requires less computation for real-time implementation as compared to other methods. © 2023 IEEE.Item Apartment Waste Disposal Sustainable Facility Location Using ENTROPY Method(Springer Science and Business Media Deutschland GmbH, 2023) Vadivel, S.M.; Palanivelu, S.; Sequeira, A.H.; Chandana, V.Global organizations are concerned about sustainability. The Triple Bottom Line (TBL) strategy, which focuses on the environment, society, and economy, has been extensively discussed in the literature to address sustainability. Apartment garbage is inherently dangerous and contagious for the environment and society, making its appropriate disposal a crucial duty for waste management companies. Therefore, in the current study, the criteria connected to the sustainability’s TBL, including other criteria, have been discovered via literature analysis and field survey for the placement of urban apartment waste disposal facility in order to make it sustainable and economical. Additionally, ENTROPY was employed to conduct this study. As a result, the current work contributes theoretically, in terms of criteria, as well as methodologically, to the choice of a location for a waste disposal plant that is sustainable. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Malware Detection in Android Applications Using Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, M.P.; Khan, H.K.Android is a widely used smartphone operating system. Because of its open-source architecture, it is becoming increasingly important in our lives. Android applications are now commonly used in several devices like smartphones, smart tv, etc. Due to many different applications and fundamental features, users often trust Android to protect data. However, research has shown that Android is prone to security issues such as malware. Android malware detection is a hot research topic and requires immediate attention and resolution. This research examines the numerous factors of the Android Application Package (APK) and presents a machine learning-based model for detecting malware in Android applications. Experimental analysis of the proposed model using a standard dataset shows that it can be a viable solution in the future. © 2023 IEEE.Item Valuation of Trash Management in Railway Compartment Using ENTROPY – A MCDM Method(Springer Science and Business Media Deutschland GmbH, 2024) Vadivel, S.M.; Eswaran, A.; Praveena, L.; Shetty, D.S.; Abhinav, A.Indian train routes are the most fashionable open-space project in India, but they have a starting problem with prodigal waste association frame. This is a problem with dry and wet garbage being transported inside of moving trains, along road tracks, and over involved rail courses. Typically enough, India has 7000 pilgrim trains that carry 23 million people. In India, the 16000 km square area surrounding the train course track has not been destroyed in any way. According to estimates, 6289 tonnes of plastic enter India’s rail rails on a regular basis. Other than the usual impurity, this enormous season of garbage is causing terrible clinical problems. We have done empirical research while travelling from Chennai to Mangalore regarding this issue. This study effectively and comprehensively deals with the enormous dry and wet garbage disposal within the moving train. In addition to helping to maintain the trains and rail course tracks clean, this will assist with the assessment dustbins disposal inside of trains in terms of passengers perspective. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.