Robust Malware Detection using Residual Attention Network

Jan 19, 20211 like89 views

In this paper, we explore the use of an attention based mechanism known as Residual Attention for malware detection and compare this with existing CNN based methods and conventional Machine Learning algorithms with the help of GIST features. The proposed method outperformed traditional malware detection methods which use Machine Learning and CNN based Deep Learning algorithms, by demonstrating an accuracy of 99.25%. This paper has been accepted in the International Conference of Consumer Electronics (ICCE 2021).

Robust Malware Detection using Residual Attention Network

An efficient Intrusion Detection System has to be given high priority while connecting systems with a network to prevent the system before an attack happens. It is a big challenge to the network security group to prevent the system from a variable types of new attacks as technology is growing in parallel. In this paper, an efficient model to detect Intrusion is proposed to predict attacks with high accuracy and less false-negative rate by deriving custom features UNSW-CF by using the benchmark intrusion dataset UNSW-NB15. To reduce the learning complexity, Custom Features are derived and then Significant Features are constructed by applying meta-heuristic FPA (Flower Pollination algorithm) and MRMR (Minimal Redundancy and Maximum Redundancy) which reduces learning time and also increases prediction accuracy. ENC (ElasicNet Classifier), KRRC (Kernel Ridge Regression Classifier), IGBC (Improved Gradient Boosting Classifier) is employed to classify the attacks in the datasets UNSW-CF, UNSW and recorded that UNSW-CF with derived custom features using IGBC integrated with FPA provided high accuracy of 97.38% and a low error rate of 2.16%. Also, the sensitivity and specificity rate for IGB attains a high rate of 97.32% and 97.50% respectively.

Forecasting number of vulnerabilities using long short-term neural memory net...IJECEIAES

Cyber-attacks are launched through the exploitation of some existing vulnerabilities in the software, hardware, system and/or network. Machine learning algorithms can be used to forecast the number of post release vulnerabilities. Traditional neural networks work like a black box approach; hence it is unclear how reasoning is used in utilizing past data points in inferring the subsequent data points. However, the long short-term memory network (LSTM), a variant of the recurrent neural network, is able to address this limitation by introducing a lot of loops in its network to retain and utilize past data points for future calculations. Moving on from the previous finding, we further enhance the results to predict the number of vulnerabilities by developing a time series-based sequential model using a long short-term memory neural network. Specifically, this study developed a supervised machine learning based on the non-linear sequential time series forecasting model with a long short-term memory neural network to predict the number of vulnerabilities for three vendors having the highest number of vulnerabilities published in the national vulnerability database (NVD), namely microsoft, IBM and oracle. Our proposed model outperforms the existing models with a prediction result root mean squared error (RMSE) of as low as 0.072.

A NOVEL INTRUSION DETECTION MODEL FOR MOBILE AD-HOC NETWORKS USING CP-KNNIJCNCJournal

Mobile ad-hoc network security problems are the subject of in depth analysis. A group of mobile nodes area unit connected to a set wired backbone. In MANET, the node themselves implement the network management in a very cooperative fashion. All the nodes area unit accountable to create a constellation that is dynamically, modification it and conjointly the absence of any clear network boundaries. We tend to project a completely unique intrusion detection model for mobile ad-hoc network victimization. CP-KNN (Conformal Prediction K-Nearest Neighbor) algorithmic rule is to classify the audit knowledge for anomaly detection. The non-conformity score worth is employed to cut back the classification period of time for multi level iteration. It is effectively notice anomalies with high true positive rate, low false positive rate and high confidence that the progressive of assorted anomaly detection ways. Additionally it is interfered by “noisy” knowledge (unclean data), the projected technique is strong, effective and conjointly it retains its smart detection performance and to avoid the abnormal activity.

Pipeline anomaly detectionGauravBiswas9

False colouringGauravBiswas9

This document discusses using deep learning techniques like convolutional neural networks, autoencoders, and variational autoencoders to perform false coloring of satellite images. The techniques are implemented in Python using frameworks like Keras and TensorFlow. CNNs achieved 92% accuracy, autoencoders achieved 90% accuracy, and variational autoencoders achieved 92% accuracy in converting grayscale satellite images to color images. The codes for implementing each technique are also included.

IDS IN TELECOMMUNICATION NETWORK USING PCAIJCNCJournal

This document summarizes a research paper that proposes using principal component analysis (PCA) as a dimension reduction technique for intrusion detection systems (IDS). The paper applies PCA to reduce the number of features from 41 to either 6 or 10 features for the NSL-KDD dataset. One reduced feature set is used to develop a network IDS with high detection success and rate, while the other is used for a host IDS also with good detection success and very high detection rate. The paper outlines the process of applying PCA for IDS, including performing PCA on training data to identify principal components, then using those components to map new online data and detect intrusions based on deviation thresholds.

L026070074ijceronline

The document is a research paper that studies using a neural network model for fingerprint recognition. It discusses how fingerprint recognition is an important technique for security and restricting intruders. The paper proposes using an artificial neural network with backpropagation training to recognize fingerprints. It describes collecting fingerprint images, classifying them, enhancing the images, and training the neural network to match images and recognize fingerprints with high accuracy. The methodology, implementation, and results of using a backpropagation neural network for fingerprint recognition are analyzed.

Deep hypersphere embedding for real-time face recognitionTELKOMNIKA JOURNAL

With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.

Hybrid Technique for Detection of Denial of Service (DOS) Attack in Wireless ...Eswar Publications

Wireless Sensor Network (WSNs) are deployed at aggressive environments which are vulnerable to various security attacks such as Wormholes, Denial of Attacks and Sybil Attacks. There are various intrusion detection techniques that are used to identify attacks in a network with high accuracy level. This paper has focused on Denial of Service attack, since it is the most common attack that affects the environment severely. Therefore a new hybrid technique combining Hidden Markov Model with Ant Colony Optimization (HMM+ACO) has been proposed that gives improved performance than the other techniques.

Biometric presentation attack detectionGautam Saxena

The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and ii) an in-depth analysis of several state-of-the art techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks).

IRJET - Explicit Content Detection using Faster R-CNN and SSD Mobilenet V2IRJET Journal

This document compares two object detection models, Faster R-CNN and SSD MobileNet v2, for detecting explicit content in images. Faster R-CNN uses a region proposal network to identify regions of interest, which are then classified and bounded. SSD MobileNet combines the Single Shot Detector framework with the efficient MobileNet architecture using depthwise separable convolutions. The document evaluates these models in terms of speed, accuracy, and model size for explicit content detection.

IRJET - Study on the Effects of Increase in the Depth of the Feature Extracto...IRJET Journal

This document discusses a study on the effects of increasing the depth of the feature extractor for recognizing handwritten digits in a convolutional neural network (CNN). Specifically, it analyzes the performance of a CNN model on the Modified National Institute of Standards and Technology (MNIST) dataset with variations in the number of filters used in deeper layers of the proposed model. The study finds that increasing the number of filters in the convolutional layers improves the accuracy of the model for classifying handwritten digits.

Evaluation of network intrusion detection using markov chainIJCI JOURNAL

Day today life internet threat has been increased significantly. There is a need to develop model in order to maintain security of system. The most effective techniques are Intrusion Detection System (IDS).The purpose of intrusion system through the security devices detect and deal with it. In this paper, a mathematical approach is used effectively to predict and detect intrusion in the network. Here we discuss about two algorithms ‘K-Means + Apriori’, a method which classify normal and abnormal activities in computer network. In K-Means process, it partitions the training set into K-clusters using Euclidean distance and introduce an outlier factor, then it build Apriori Algorithm to prune the data by removing infrequent data in the database. Based on defined state the degree of incoming data is evaluated through the experiment using sample DARPA2000 dataset, and achieves high detection performance in level of attack in stages.

Image Captioning Generator using Deep Machine Learningijtsrd

Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p

An approach for ids by combining svm and ant colony algorithmeSAT Journals

This summarizes a research paper that proposes a new approach called CSVAC (Combined Support Vector with Ant Colony) for intrusion detection. The approach combines two algorithms, Support Vector Machine (SVM) and Ant Colony Optimization (ACO), to classify network data as normal or abnormal. SVM is used to generate a separating hyperplane and find support vectors, while ACO performs clustering around the support vectors. The clusters are added to the SVM training set and it is retrained in an iterative process until the detection rate exceeds a threshold. The paper evaluates this approach on the standard KDD99 dataset and finds it achieves superior results to other algorithms in terms of accuracy and efficiency.

An approach for ids by combining svm and ant colony algorithmeSAT Publishing House

IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology

A survey of Network Intrusion Detection using soft computing Techniqueijsrd.com

with the impending era of internet, the network security has become the key foundation for lot of financial and business application. Intrusion detection is one of the looms to resolve the problem of network security. An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been misused. Here we propose a new approach by utilizing neuro fuzzy and support vector machine with fuzzy genetic algorithm for higher rate of detection.

VIRTUAL ARCHITECTURE AND ENERGYEFFICIENT ROUTING PROTOCOLS FOR 3D WIRELESS SE...ijwmn

This paper proposes a virtual architecture for three-dimensional (3D) wireless sensor networks (WSNs), a dynamic coordinate system, and a scalable energy-efficient training protocol for collections of nodes deployed in the space that are initially anonymous, asynchronous, and unaware of their initial location. The 3D WSNs considered comprise massively deployed tiny energy-constrained commodity sensors and one or more sink nodes that provide an interface to the outside world. The proposed architecture is a generalization of a two-dimensional virtual architecture previously proposed in the literature, in which a flexible and intuitive coordinate system is imposed onto the deployment area and the anonymous nodes are partitioned into clusters where data can be gathered from the environment and synthesized under local control. The architecture solves the hidden sensors problem that occurs because of irregularities in rugged deployment areas or environments containing buildings by training the network of nodes arbitrarily dispersed in the 3D space. In addition, we derive two simple and energy-efficient routing protocols, respectively for dense and sparse networks, based on the proposed dynamic coordinate system. They are used to minimize the power expended in collecting and routing data to the sink node, thus increasing the lifetime of the network.

Black Box Model based Self Healing Solution for Stuck at Faults in Digital Ci...IJECEIAES

The paper proposes a design strategy to retain the true nature of the output in the event of occurrence of stuck at faults at the interconnect levels of digital circuits. The procedure endeavours to design a combinational architecture which includes attributes to identify stuck at faults present in the intermediate lines and involves a healing mechanism to redress the same. The simulated fault injection procedure introduces both single as well as multiple stuck-at faults at the interconnect levels of a two level combinational circuit in accordance with the directives of a control signal. The inherent heal facility attached to the formulation enables to reach out the fault free output even in the presence of faults. The Modelsim based simulation results obtained for the Circuit Under Test [CUT] implemented using a Read Only Memory [ROM], proclaim the ability of the system to survive itself from the influence of faults. The comparison made with the traditional Triple Modular Redundancy [TMR] exhibits the superiority of the scheme in terms of fault coverage and area overhead.

Deep-learning based single object tracker for night surveillance IJECEIAES

This document summarizes a research paper that proposes a deep learning-based single object tracker for night surveillance video. The tracker uses pre-trained convolutional neural networks coupled with fully connected layers. The network is trained online during tracking to model changes in the target object's appearance as it moves. The paper tests different hyperparameters for the online learning, including optimization algorithms, learning rates, and ratios of positive and negative training samples, to determine the optimal setup for nighttime tracking. It evaluates the tracker on 14 night surveillance videos and finds that using the Adam optimizer with a learning rate of 0.00075 and a 2:1 ratio of positive to negative samples achieves the best accuracy.

IRJET- Implementation of Artificial Intelligence Methods to Curb Cyber Assaul...IRJET Journal

This document discusses how artificial intelligence methods can help curb cyber assaults. It reviews various AI techniques including expert systems, artificial neural networks, and intelligent agents that have been implemented or could potentially be implemented for cyber security purposes. For example, expert systems have been used to analyze risk levels on e-commerce sites and identify system vulnerabilities. Artificial neural networks have been applied for intrusion detection and classification of attacks. Intelligent agents are well-suited for combating cyber crimes due to their mobility, flexibility, and cooperative nature. The document concludes that while AI is already being used in various ways for cyber security, hackers may also start using AI techniques, presenting new challenges going forward.

A Novel Classification via Clustering Method for Anomaly Based Network Intrus...IDES Editor

Intrusion detection in the internet is an active area of research. Intruders can be classified into two types, namely; external intruders who are unauthorized users of the computers they attack, and internal intruders, who have permission to access the system but with some restrictions. The aim of this paper is to present a methodology to recognize attacks during the normal activities in a system. A novel classification via sequential information bottleneck (sIB) clustering algorithm has been proposed to build an efficient anomaly based network intrusion detection model. We have compared our proposed method with other clustering algorithms like X-Means, Farthest First, Filtered clusters, DBSCAN, K-Means, and EM (Expectation-Maximization) clustering in order to find the suitability of our proposed algorithm. A subset of KDDCup 1999 intrusion detection benchmark dataset has been used for the experiment. Results show that the proposed method is efficient in terms of detection accuracy, low false positive rate in comparison to the other existing methods.

IRJET- A Study of Different Convolution Neural Network Architectures for Huma...IRJET Journal

This document compares the performance of different convolutional neural network architectures for human facial emotion detection, including ResNet, VGG19, MobileNet, DenseNet, and the authors' proposed 17-layer sequential model. It finds that the authors' model achieves the highest accuracy (69%) and lowest loss, outperforming the other models. The document also proposes an application of this facial emotion detection system to remotely monitor patients' emotions in hospitals using cameras and alert medical staff if fearful or shocked expressions are detected.

n-Tier Modelling of Robust Key management for Secure Data Aggregation in Wire...IJECEIAES

Security problems in Wireless Sensor Network (WSN) have been researched from more than a decade. There are various security approaches being evolving towards resisting various forms of attack using different methodologies. After reviewing the existing security approaches, it can be concluded that such security approaches are highly attack-specific and doesnt address various associated issues in WSN. It is essential for security approach to be computationally lightweight. Therefore, this paper presents a novel analytical modelling that is based on n-tier approach with a target to generate an optimized secret key that could ensure higher degree of security during the process of data aggregation in WSN. The study outcome shows that proposed system is computationally lightweight with good performance on reduced delay and reduced energy consumption. It also exhibits enhanced response time and good data delivery performance to balance the need of security and data forwarding performance in WSN.

92 97Editor IJARCET

1) The document discusses VLSI architecture and implementation for 3D neural network based image compression. It proposes developing new hardware architectures optimized for area, power, and speed for implementing 3D neural networks for image compression. 2) A block diagram is presented showing the overall process of image acquisition, preprocessing, compression using a 3D neural network, and encoding for transmission. 3) The proposed 3D neural network architecture uses multiple hidden layers with lower dimensions than the input and output layers to perform compression and decompression. The network is trained using backpropagation.

Ha3312361238IJERA Editor

International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.

Visualize network anomaly detection by using k means clustering algorithmIJCNCJournal

With the ever increasing amount of new attacks in today’s world the amount of data will keep increasing, and because of the base-rate fallacy the amount of false alarms will also increase. Another problem with detection of attacks is that they usually isn’t detected until after the attack has taken place, this makes defending against attacks hard and can easily lead to disclosure of sensitive information. In this paper we choose K-means algorithm with the Kdd Cup 1999 network data set to evaluate the performance of an unsupervised learning method for anomaly detection. The results of the evaluation showed that a high detection rate can be achieve while maintaining a low false alarm rate .This paper presents the result of using k-means clustering by applying Cluster 3.0 tool and visualized this result by using TreeView visualization tool .

IRJET- A Survey on Medical Image Interpretation for Predicting PneumoniaIRJET Journal

This document summarizes research on using machine learning and deep learning techniques to interpret medical images and predict pneumonia. It first discusses how medical image analysis is an active field for machine learning. It then reviews several related studies on using convolutional neural networks (CNNs) and transfer learning to classify chest x-rays and detect pneumonia. Specifically, it examines research on developing CNN models for pneumonia classification and using pre-trained CNN architectures like VGG16, VGG19, and ResNet with transfer learning. The document concludes that computer-aided diagnosis systems using deep learning can provide accurate predictions to assist radiologists in pneumonia diagnosis from chest x-rays.

Machine learning based augmented reality for improved learning application th...IJECEIAES

Detection of objects and their location in an image are important elements of current research in computer vision. In May 2020, Meta released its state-ofthe-art object-detection model based on a transformer architecture called detection transformer (DETR). There are several object-detection models such as region-based convolutional neural network (R-CNN), you only look once (YOLO) and single shot detectors (SSD), but none have used a transformer to accomplish this task. These models mentioned earlier, use all sorts of hyperparameters and layers. However, the advantages of using a transformer pattern make the architecture simple and easy to implement. In this paper, we determine the name of a chemical experiment through two steps: firstly, by building a DETR model, trained on a customized dataset, and then integrate it into an augmented reality mobile application. By detecting the objects used during the realization of an experiment, we can predict the name of the experiment using a multi-class classification approach. The combination of various computer vision techniques with augmented reality is indeed promising and offers a better user experience.

IRJET-Multiclass Classification Method Based On Deep Learning For Leaf Identi...IRJET Journal

This document discusses a multiclass classification method using deep learning for leaf identification to help farmers. It proposes using a convolutional neural network (CNN) model for feature extraction and classification of leaf images. The CNN model is trained on labeled leaf image data and can then be used to classify new unlabeled leaf images. The method involves preprocessing leaf images, extracting features using the CNN model, and classifying the leaves into different plant categories. The researchers tested their method on 13 plant leaf categories and 4 disease categories, achieving 95.25% accuracy. They conclude CNNs are well-suited for leaf identification and classification tasks due to their ability to handle large image datasets.

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