National Journals

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/165

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Start date: 2020End date: 2023

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Now showing 1 - 6 of 6
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    MALWARE FAMILY CLASSIFICATION MODEL USING USER DEFINED FEATURES AND REPRESENTATION LEARNING
    (Springer Link, 2020-11-20) Gayathri, T; Vijaya, M.S
    Malware is very dangerous for system and network user. Malware identification is essential tasks in effective detecting and preventing the computer system from being infected, protecting it from potential information loss and system compromise. Commonly, there are 25 malware families exists. Traditional malware detection and anti-virus systems fail to classify the new variants of unknown malware into their corresponding families. With development of malicious code engineering, it is possible to understand the malware variants and their features for new malware samples which carry variability and polymorphism. The detection methods can hardly detect such variants but it is significant in the cyber security field to analyze and detect large-scale malware samples more efficiently. Hence it is proposed to develop an accurate malware family classification model contemporary deep learning technique. In this paper, malware family recognition is formulated as multi classification task and appropriate solution is obtained using representation learning based on binary array of malware executable files. Six families of malware have been considered here for building the models. The feature dataset with 690 instances is applied to deep neural network to build the classifier. The experimental results, based on a dataset of 6 classes of malware families and 690 malware files trained model provides an accuracy of over 86.8% in discriminating from malware families. The techniques provide better results for classifying malware into families.
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    AN EXPLORATORY DATA ANALYSIS ON AIR QUALITY DATA OF TRIVANDRUM
    (Springer Link, 2023-05-31) Santhana Lakshmi V; Vijaya M.S
    Data analysis is the most integral part of any research. It is the process of examining the data using statistical methods to identify the hidden patterns and trends which aid in making decisions. This helps in understanding the distribution, correlation, outliers, and missing values found in the data. In this paper, data analysis is performed over the air pollutant data and the meteorological data that influences air pollution. The meteorological data for the period of 4 years of Trivandrum city was taken for the purpose of analysis. The dataset includes 26,544 instances and 23 features. Pollutant parameters such as PM2.5, PM10, CO, SO2, ozone, NOX, and NH3 are considered for analysis. Meteorological features taken for analysis include temperature, dew, humidity, wind speed, wind direction, etc. Meteorological features play a substantial role in identifying air pollution. Boxplots, heat maps, pair plots, and histograms were used to reveal the distribution and correlation between the attributes. From the analysis, it has been identified that the features like sea level pressure, PM2.5, PM10, CO, NOX, NH3, SO2, and ozone are positively correlated with air quality index whereas features like, dew, humidity, wind speed, cloud cover are negatively correlated with air quality index. The results of the data analysis assist in preparing the data for further research.
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    BOOTSTRAPPING OF FINE-TUNED SEGMENTATION AND CLASSIFICATION NETWORK FOR EPIDERMIS DISORDER CATEGORIZATION
    (Springer Link, 2023-07-25) Kalaivani A; Karpagavalli S
    As all people have been affected by various skin related illnesses, categorization of skin disorders has become prominent in recent healthcare system. To identify and categorize skin related syndromes, many transfer learning frameworks were used. Amongst, a Fine-tuned Segmentation and Classification Network (F-SegClassNet) achieved better efficacy by using the novel unified loss function. Nonetheless, it was not apt for the datasets that lack in training images. Hence in this article, Bootstrapping of F-SegClassNet (BF-SegClassNet) model is proposed which solves the imbalanced images in the training set via generating the group of pseudo balanced training batches relying on the properties of the considered skin image dataset. This model fits the distinct abilities of Deep Convolutional Neural Network (DCNN) classifier so that it is highly useful for classifying the skin disorder image dataset with a highly imbalanced image data distribution. According to the Bootstrpping, better tradeoff between simple and complex image samples is realized to make a network model that is suitable for automatic skin disorders classification. In this model, statistics across the complete training set is calculated and a new subset is produced that retains the most essential image samples. So, the skin images are segmented and categorized by this new model to identify the varieties of epidermis infections. At last, the testing outcomes exhibits BF-SegClassNet-model accomplishes the mean accuracy with 96.14% for HAM dataset which is compared to state-of-the-art models.
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    PESTICIDE RECOMMENDATION FOR DIFFERENT LEAF DISEASES AND RELATED PESTS USING MULTI-DIMENSIONAL FEATURE LEARNING DEEP CLASSIFIER
    (ProQuest, 2023-02) Jaithoon, Bibi Mohammed Saleem; Shanmugam, Karpagavalli
    In agricultural applications, the most essential task is to classify leaf diseases and their associated pests from various aspects. To achieve this, a Deep Convolutional Neural Network (DCNN) model was developed to classify the leaf diseases based on the soil and climatic features. But it needs a recommendation system to control the pesticide use for controlling the leaf diseases caused by specific pests. Hence, this paper hybridizes the Multi-dimensional Feature Learning-based DCNN (MFL-DCNN) with the Rough Set (RS) on an intuitionistic Fuzzy approximation space (RSF)-based decision support system to suggest the proper pesticides for a certain crop to be planted in a particular region. First, the leaf images are augmented by the Positional-aware Dual-Attention and Topology-Fusion with Evolutionary Generative Adversarial Network (PDATFEGAN) model. Then, the multi-dimensional data such as the created leaf images, pest, soil, weather, and pesticide data are fed to the DCNN with a softmax classifier for classifying leaf diseases and related pests. Then, the RSF-based decision model is applied, which determines the correlation between leaf disease and pests to recommend suitable pesticides. Finally, the experimental results reveal that the MFL-DCNN-RSF accomplishes a maximum efficiency than all other models for recommending pesticides to control leaf diseases and pests.
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    PROTECTING MANETS FROM BLACK AND GRAY HOLE ATTACKS THROUGH A DETAILED DETECTION SYSTEM
    (International Journal of Intelligent Engineering Systems, 2022-08) Vijigripsy, Jebaseelan; Kanchana, Kavartty Raju
    In mobile ad-hoc network (MANET), identification and mitigation of black and gray-hole attacks is a challenging task compared to the detection of other attacks. To solve this issue, a secure route discovery ad-hoc ondemand distance vector (SRD-AODV) protocol has been suggested, which verifies the nodes only during the path discovery. But, it is necessary to authenticate the nodes during data transmission since the gray-hole nodes broadcast an accurate target sequence number (TSN) during the route discovery, whereas it becomes malicious and drops the packets during the data forwarding. Hence in this article, a secure route maintenance and attack detection AODV (SRMAD-AODV) protocol is proposed for identifying and defending the black and gray-hole attacks in the data transfer stage. Initially, an attack discovery system (ADS) node is decided from the connected dominating set (CDS) method based on energy and confidence score. The CDS is a robust, distinct and localized method to identify nearby linked dominating sets of nodes in a limited range in MANETs. The selected ADS nodes forward a status packet within the size of the dominating set to retrieve the entire behavioral data. ADS nodes examine gathered behavioral data and create a blacklist in which the suspected black and gray-hole nodes are added. Then, the blacklist is forwarded to the origin node to confirm the susceptibility of nodes present in the blacklist. Once the origin node authenticates the blacklist, it broadcasts a block message to all other nodes in a path for discarding blacklist nodes from the routing path. Further, this SRMAD-AODV protocol is simulated and the findings exhibit that it realizes 5.2sec of end-to-end delay (EED) and 86 % of packet delivery ratio (PDR) in contrast to the SRD-AODV protocol.
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    INTEGRATED FRAMEWORK FOR INTRUSION DETECTION THROUGH ADVERSARIAL SAMPLING AND ENHANCED DEEP CORRELATED HIERARCHICAL NETWORK
    (IIETA, 2022-08) Deepa, Venkatraman; Radha, Narayanan
    Intrusion Detection Systems (IDSs) play a critical role in detecting malicious assaults and threats in the network system. This research work proposed a network intrusion detection technique, which combines an Adversarial Sampling and Enhanced Deep Correlated Hierarchical Network for IDS. Initially, the proposed Enhanced Generative Adversarial Networks (EGAN) method is used to raise the minority sample. A balanced dataset can be created in this way, allowing the model to completely learn the properties of minority samples while also drastically minimizing the model training time. Then, create an Enhanced Deep Correlated Hierarchical Network model by using a Bi-Directional Long Short-Term Memory (BiLSTM) to collect temporal characteristics and Cross-correlated Convolution Neural Network (CCNN) to retrieve spatial characteristics. The softmax classifier at the end of BiLSTM is used to classify intrusion data. The traditional NSL-KDD dataset is utilized for the experimentation of the proposed model.