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检索条件"机构=MTech Computer Science and Engineering School of Computing Science and Engineering"

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Post-Exploitation Insights: Dynamic Analysis of C2 Frameworks

Post-Exploitation Insights: Dynamic Analysis of C2 Framework...

引用

Sustainable Expert Systems (ICSES), International Conference on

作者： Subhiksha Suresh Rao Sahil Mishra Samarth Seshadri Kavitha C. R Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Recognizing and reducing the risks presented by malevolent actors in the realm of cyber security requires an awareness of post exploitation techniques. However, deThe study investigates post operational challenges through a dynamic analysis of command-and-control frameworks (C2) fenders are finding it difficult to keep up with the rapid development of C2 frameworks. Using the MITRE ATT&CK framework as a guide, this study explores a popular C2 server: Metasploit. Through extensive research, deployment, and exploitation in a controlled environment, the strengths and weaknesses of each framework are assessed. Key metrics such as ease of installation, performance, security features, and community support are carefully researched to provide comprehensive insights. In addition, the project explores future frontiers by identifying potential modules that can enhance the capabilities of the selected C2 framework. By offering a detailed analysis, this study helps to understand the importance of the C2 frameworks in cyber security and provides valuable insights for security practitioners and researchers.

关键词： Command and control systems Measurement Automation Production Aerodynamics Servers computer crime Protection Expert systems Payloads

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Bottom-Up Parsers: A Survey

Bottom-Up Parsers: A Survey

引用

International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Appala Hari Narayana Karthik B Venkat Vinod Kumar Bejji Bharadwaj Sree Sai Karthikeya Meena Belwal Department of Computer Science and Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

This study offers a thorough analysis of bottom-up parsing techniques, necessary for the creation and execution of compilers. Parsers such as SLR (Simple LR), LR (Canonical LR), LR(k) and LALR (Look-Ahead LR) are bottom-up and known for their efficiency and robustness in constructing parse trees from root to leaves during syntax analysis. This paper follows the evolution throughout history, theoretical bases, and real-world uses of these parsers. We examine the formal characteristics, contrast different bottom-up parsing methods, and investigate advanced subjects like error handling and optimization techniques. Furthermore, there is a review of recent developments and upcoming research paths in bottom-up parsing to provide a comprehensive grasp of their significance in syntactic analysis.

关键词： Surveys Reviews Semantics Syntactics Robustness Grammar Error correction History Information technology Optimization

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Reflectance Recovery Guided Learning of Illumination-Invariant Features for Person Re-Identification 18th

Reflectance Recovery Guided Learning of Illumination-Invari...

引用

18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Chen, Xianbiao Xie, Xiaohua School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9789819610679

In real-world scenarios, the changes of illumination are noticeable and can significantly impact the performance of re-identification (ReID) algorithms. However, the existing person ReID methods predominantly concentrate on tackling challenges in scenarios with minimal illumination fluctuations. The key to tackling this issue is to extract illumination-invariant features. So we propose a joint learning framework which combines the recovery of reflectance map and extraction of illumination-invariant feature. By sharing parameters, the proposed module, which can be seamlessly detached during the inference phase, contributes to a reduction in inference computation time. We also introduce an adversarial learning mechanism, utilizing illumination category and person identity to facilitate the extraction of features that are invariant to illumination. Besides, due to the lack of person ReID dataset containing images under diverse lighting conditions, we construct a real-world dataset called ICReID in which images have drastic illumination changes. Extensive experiments demonstrate the effectiveness of the proposed method, which achieves significant performance on both synthetic and real datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Automated Classification and Segmentation of Brain Tumor Images Using Backboned ResUNet Framework

Automated Classification and Segmentation of Brain Tumor Ima...

引用

Mobile computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Prasanalakshmi B Cuddapah Anitha Balamanigandan R Mahaveerakannan R N.V. Ratnakishor Gade Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

One of the most challenging issues in computer imaging is the automated segmentation of brain tumors using Magnetic Resonance Images (MRI). Several approaches are explored using Deep Neural Networks in image segmentation since they perform well in automated tumor segmentation using tumor scans. Due to the complexity., training deeper neural networks usually takes a long time and many processing resources. An automated approach for classifying and segmenting images of Brain tumor MRI is proposed. As a hybrid transfer learning approach., modified ResNet50 identifies the class of MRI images as Benign or malignant. After identifying the Benign images., the tumor's location is segmented using a hybrid network involving ResUNet with the Xception network as Backbone. This combination approach is chosen after its comparative analysis with other backbones. Various combination approaches are carried out., and performance metrics like Jaccard, Dice., and Tversky coefficient are done to prove the ResUnet backboned with Xception plays a better role concerning accuracy and speed. The proposed approach was tested on the dataset Cancer Imaging Archive to validate its efficiency. The proposed model has an average dice similarity coefficient score of 0.94., outperforming the conventional transfer learning training method with all the layers.

关键词： Training Measurement Image segmentation Accuracy Magnetic resonance imaging Transfer learning Brain tumors computer architecture Brain modeling Residual neural networks

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GSM-Powered Gas Leak Detection: A Modern Approach

GSM-Powered Gas Leak Detection: A Modern Approach

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Manju Khanna Sibyala Hemanth Sai Chakka Subrahmanya Sresti Vellala Bhavaharnath Sharma Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Global systems for mobile communications (GSM) Powered Gas Leakage Detection system aims to prevent gas leakages, especially from inflammable sources like LPG, which poses great risk. This presents a gas leakage detection system utilizing Arduino and Global systems for mobile communications (GSM) modem. Here, the system uses MQ-9 sensor to detect the gas leakage and triggers an alarm which is comprised of a buzzer and activates the safety measures like turning on the exhaust fan. And also, the Arduino UNO board initiates a GSM modem which sends Short Message Service (SMS) alerts to pre-specified mobile numbers, thus helps in prevention of dangerous situations. This approach helps to minimize potential gas leakage accidents, protecting lives and properties by ensuring quick response and proper precautionary measures.

关键词： GSM Protocols Prevention and mitigation Modems Message services Turning Real-time systems Safety Resource management Accidents

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Tweeting the Blues: Leveraging NLP and Classification Models for Depression Detection

Tweeting the Blues: Leveraging NLP and Classification Models...

引用

Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Spandana Chereddy K Geetha A G Sreedevi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

Depression is a global mental health problem while Twitter is a valuable medium on which content indicating emotional affect is generated. Our research draws upon NLP to analyze tweets by the application of Naive Bayes, Support Vector Machines, and Logistic Regression classifiers. The first objective will be the tweets’ classification into positive and negative sentiment categories with a secondary goal to explore the possibility of the detection of community characteristics including gender, age, and location. We investigate the tweet content by using NLP in an attempt to pinpoint the sensing patterns and the department that can reliably be used to detect depression. Results of the experiments proved comprehensive model accuracy with Support Vector Machines taking a better position on the matter of correctly classifying depression tweets. Especially, Support Vector Machines gave an accuracy of 85%, and values for precision, recall, and f1 were 0.89, 0.63, and 0.63, respectively. Therefore, the remaining ones for attaining higher accuracy and recall are difficult to achieve in models that are depressed but there is still the necessity for more data generation. This research thus clears the way for NLP in the diagnosis of mental health by providing vital assessment data that can be used by psychologists, researchers, health authorities, and policymakers to develop targeted intervention strategies and advancement of public health goals.

关键词： Support vector machines Analytical models Logistic regression Social networking (online) Computational modeling Mental health Depression

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SecureNet: A PySpark-Based Approach to Enhanced Network Intrusion Detection Using Machine Learning and Feature engineering

SecureNet: A PySpark-Based Approach to Enhanced Network Intr...

引用

Intelligent Technologies (CONIT), International Conference on

作者： B. Akhileswar Reddy G. Someswara Reddy K. Lokesh Manju Venugopalan Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Cyber-attacks are increasing day to day in this world of AI and Internet. A ML model to detect the network attack based on the network parameters is of great use. The proposed model uses a new and complete method to make intrusion detection in computer networks more accurate. Using PySpark’s shared computing power, machine learning rules are put in the network data. It helps to make a strong first step of sorting things out. To optimize and streamline the feature space, two distinct techniques are employed sequentially; Chi-Square Selector for selecting important features and Principal Component Analysis to reduce features. These methods are used one after the other to improve how well machine learning models work. This makes them more accurate and flexible for finding unauthorized attacks on computer systems. It adds important value to the world of cybersecurity and Big Data analysis. From the experiments. The model which reported the best performance is Multi-Layer Perceptron with 0.993 accuracy.

关键词： Accuracy Computational modeling Text categorization Network intrusion detection Machine learning Feature extraction Internet

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Convolutional Neural Network Based Age Estimation using Diverse Facial Datasets

Convolutional Neural Network Based Age Estimation using Dive...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Sidda Siri Chandana N Vithyatharshana Y Ghnana Prasoona Yedhoti Thrinayani C Jyotsna Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Determining an individual’s age accurately is important in many contexts, including forensic investigations, medical diagnostics, and security applications. Traditional age estimation techniques rely on manual evaluation, which can be laborious and error prone. Convolutional neural networks (CNN) have been utilized more and more for image-based age estimation since the development of deep learning. The Objective is to create a CNN-based age estimation algorithm that can precisely estimate a person’s age from facial photos. The proposed study used the UTKFace dataset and Facial Image dataset, which included 30,000+ facial images of people from various ethnic backgrounds, aged 0 to 116 and Facial pictures, a public dataset, which is a compilation of digital portraits of people. Five convolutional layers, five pooling layers, and two fully linked layers make up the deep CNN model that has been developed and trained in this project. The study’s findings show how well CNN-based deep learning models may be used to determine an individual’s age from face photos. For the model’s training, the UTKFace dataset and Facial Images dataset supplied a wide-ranging and sizable combined dataset. The model performed better than more established age estimation techniques.

关键词： Deep learning Training Computational modeling Forensics Estimation Manuals Convolutional neural networks Security Medical diagnosis Faces

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Dissecting Digital Dangers: A MITRE-Aligned Analysis of Two Prominent Malware Instances

Dissecting Digital Dangers: A MITRE-Aligned Analysis of Two ...

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International Conference on computing and Networking Technology (ICCNT)

作者： Subhiksha Suresh Rao Richa Vivek Savant Sahil Mishra B Sreevidya M Rajesh Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The increasing complexity of malware requires a more thorough comprehension of how they operate. Using the MITRE ATT&CK framework, this project delves into the TrickBot and AsyncRAT malwares, offering a thorough examination of their tactics, techniques, and procedures (TTPs) across the cyber kill chain. This study uncovers trends in how these malicious software gain entry, carry out tasks, remain active, and extract information from a network by correlating TTPs with ATT&CK methods. Key analyses indicate that TrickBot utilizes elevated entropy values to avoid being detected, incorporates modular structures to enhance its capabilities, and utilizes spear-phishing and network spreading to achieve broad infection. Asyncrat is found to employ sophisticated anti-analysis methods, reflective code loading, and achieves persistence by altering the system registry while avoiding sandbox environments. Additionally, it establishes command and control (C2) connections to maintain long-term access over compromised systems. The observations uncovered in this research emphasize the importance of having strong cybersecurity policies and comprehensive threat intelligence to combat changing threats effectively. Understanding the complex behaviours and evasion methods of these malwares is crucial for creating specific defences and improving security overall.

关键词： Command and control systems Prevention and mitigation Loading Market research Malware Entropy Trojan horses Servers IP networks Data mining

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Shallow-Sec: Malware Detection in Real-Time Devices using Feature Weightage and Shallow Learning Models

Shallow-Sec: Malware Detection in Real-Time Devices using Fe...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Panaganti Priyanka Udhayakumar K. Deepak Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

In the domain of mobile security, particularly in APK (Android Application Package) malware detection, ensuring robust threat identification is critical for safeguarding user devices and sensitive data. This study addresses the need for a reliable Mobile Intrusion Detection System (MIDS) by harnessing advanced machine learning techniques. We advocate for employing tested models such as Logistic Regression, Decision Tree, and XGBClassifier, alongside the top-performing Random Forest (RF) model, which achieved an impressive accuracy of ninety-eight-point six percent. To enhance model performance, we utilize feature selection techniques like Recursive Feature Elimination (RFE) and hyperparameter optimization. The datasets chosen for training and evaluation, including real-world malicious and benign APKs, yield accuracies around ninety five percent. Furthermore, diverse APK datasets, including benchmarks and real-time streams, are used for rigorous testing. By leveraging optimized models and features, our study ensures exceptional performance in detecting various forms of APK malware, thus fortifying the reliability and efficacy of MIDS in real-world scenarios.

关键词： Performance evaluation Training Accuracy Intrusion detection Feature extraction Real-time systems Malware Reliability Random forests Streams

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