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JPD1424 A System for Denial-of-Service Attack Detection Based on Multivariat...
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This document summarizes Amin Dastanpour's research comparing the use of genetic algorithms to optimize artificial neural networks and support vector machines for intrusion detection systems. The research aims to improve detection rates using machine learning algorithms with fewer features. It applies genetic algorithms to select optimal features for neural networks and support vector machines, achieving 100% detection rates using only 18 features for neural networks and 24 features for support vector machines. This outperforms other algorithms from related work and demonstrates the effectiveness of genetic algorithms for optimization.
This document discusses multisensor data fusion for defense applications. It describes data fusion as integrating data from multiple sensors to provide a more complete picture than from individual sensors alone. Some key defense applications discussed include surveillance, intelligence analysis, and missile guidance systems. The document also provides an example of using a Kalman filter for multisensor data fusion to estimate the state of a moving target tracked by multiple sensors.
IMPROVEMENT OF FALSE REPORT DETECTION PERFORMANCE BASED ON INVALID DATA DETEC...IJCNCJournal
This document summarizes a research paper that proposes a method to improve the detection of false reports in wireless sensor networks using machine learning. The method trains a neural network model using data from a forest fire simulation based on cellular automata. This allows the neural network to analyze report contents and predict situations occurring in the field to help identify false reports, without relying solely on message authentication codes which can be compromised during attacks. The document provides background on related work in security protocols for wireless sensor networks, artificial neural networks, and cellular automata models for simulating forest fire spread.
DETECTION OF MALICIOUS EXECUTABLES USING RULE BASED CLASSIFICATION ALGORITHMSAAKANKSHA JAIN
Slide present statistical mining of Malicious-Executable dataset collected from various antivirus log-files and other sources.
Further classifications of malicious code as per their impact on user's system & distinguishes threats on the muse in their connected severity.
Implementation of the algorithms JRIP ,PART and RIDOR in additional economical manner to acquire a level of accuracy to the classification results.
This document discusses detecting malware using n-grams and machine learning algorithms. It analyzes executable files to extract n-gram sequences from the opcode, creates a feature vector table (FVT) of n-grams and their frequencies. This FVT is used to train and test machine learning classifiers like J48, SVM, and Random Forest. Dimensionality reduction using PCA is also applied before classification. The models are evaluated based on metrics like accuracy, misclassification rate, and precision on n-gram datasets of different sizes. Random Forest performs best with over 95% accuracy on 2-grams.
JPD1424 A System for Denial-of-Service Attack Detection Based on Multivariat...chennaijp
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This document summarizes Amin Dastanpour's research comparing the use of genetic algorithms to optimize artificial neural networks and support vector machines for intrusion detection systems. The research aims to improve detection rates using machine learning algorithms with fewer features. It applies genetic algorithms to select optimal features for neural networks and support vector machines, achieving 100% detection rates using only 18 features for neural networks and 24 features for support vector machines. This outperforms other algorithms from related work and demonstrates the effectiveness of genetic algorithms for optimization.
This document discusses multisensor data fusion for defense applications. It describes data fusion as integrating data from multiple sensors to provide a more complete picture than from individual sensors alone. Some key defense applications discussed include surveillance, intelligence analysis, and missile guidance systems. The document also provides an example of using a Kalman filter for multisensor data fusion to estimate the state of a moving target tracked by multiple sensors.
23 9754 assessment paper id 0023 (ed l)2IAESIJEECS
This paper presents a risk assessment method for assessing the cyber security of power systems in view of the role of protection systems. This paper examines the collision of transmission and bus line protection systems positioned in substations on the cyber-physical performance of the power systems. The projected method simulates the physical feedback of power systems to hateful attacks on protection system settings and parameters. The relationship between protection device settings, protection logic, and circuit breaker logic is analyzed. The expected load reduction (ELC) indicator is used in this paper to determine potential losses in the system due to cyber attacks. The Monte Carlo simulation is used to calculate ELC’s account to assess the capabilities of the attackers and bus arrangements are changed. The influence of the projected risk assessment method is illustrated by the use of the 9-bus system and the IEEE-68 bus system.
Approaches to integrated malware detection and avoidanceUltraUploader
This document discusses approaches to integrated malware detection and avoidance. It summarizes the implementation of a mandatory virus protection policy within the Generalized Framework for Access Control (GFAC) on Linux. This includes integrating on-access scanning rules in the kernel to scan files and network sockets. It also discusses challenges with application-level code like macro viruses, as they are harder to scan at the file or socket level due to file formatting and potential encryption. Scanning at the application layer may be needed to fully address these threats.
Multi sensor data fusion system for enhanced analysis of deterioration in con...Sayed Abulhasan Quadri
This document proposes a multi-sensor data fusion system to enhance the analysis of concrete deterioration due to alkali-aggregate reaction (AAR). The system uses different sensor types like acoustic, electro-mechanical, optical, and embedded sensors to collect internal and external damage data. Feature extraction and a decentralized Kalman filter are used to fuse the heterogeneous sensor data. An artificial neural network then characterizes and quantifies the damage levels. The study expects to improve accuracy over single sensor systems and establish correlations between surface damage, internal damage, and gel concentration levels causing structural deterioration.
A Survey of Fault Tolerance Methods in Wireless Sensor NetworksIRJET Journal
This document summarizes and analyzes various fault tolerance mechanisms for wireless sensor networks. It discusses mobile agent mechanisms, relay node mechanisms, and handover mechanisms. The document analyzes several existing fault tolerance methods, including Bayesian network models, probabilistic combinatorial optimization, dynamic power level adjustment, and integrated fault tolerance frameworks. Overall, the document provides an overview of important fault tolerance issues in wireless sensor networks and different approaches that have been proposed to address faults and improve reliability.
Artificial neural network for misuse detectionSajan Sahu
Manoj Kumar Gantayat presented on using artificial neural networks for intrusion detection. He discussed how neural networks can be applied to misuse detection in intrusion detection systems. Specifically, he covered three neural network models (Perceptron, Backpropagation, hybrid), components of intrusion detection systems, advantages of neural networks in analyzing incomplete or distorted data, and challenges like needing accurate training data. He concluded the early results of neural network intrusion detection systems show promise for refinement and full-scale demonstration systems.
This document provides an overview of wireless sensor networks. It discusses applications of sensor networks such as environmental monitoring, health monitoring, and military surveillance. It also covers factors that influence sensor network design like fault tolerance, scalability, and power consumption. Additionally, it describes the communication architecture of sensor networks which includes layers like the physical layer, data link layer, network layer, and application layer. The document concludes that sensor networks will become more integral to daily life as new applications are created.
Short-term Load Forecasting based on Neural network and Local RegressionJieJie Bao
The document discusses short-term load forecasting using neural networks and local regression models. It finds that a combination model using local regression followed by a neural network to refine the results provides the best accuracy, achieving an error rate of 2.70% for day-ahead forecasts. This multi-step approach first uses local regression to identify meaningful factors like the influence of temperature, then trains a neural network using these factors to further improve the forecasts.
The slides of the talk I gave on April 2011 in Paris at the IEEE Symposium on Computational Intelligence Applications in Smart Grid (http://ieee-ssci.org/2011/ciasg-2011).
1) The document describes a final semester project analyzing agricultural sector data using hybrid algorithms and machine learning techniques.
2) It involves collecting cost and capital logs, applying algorithms like genetic, fuzzy logic, and neural networks to generate mean cost values and predict commodity prices.
3) Validation techniques like internal and external clustering are used to improve the analysis and resulting prediction, which is subject to change with new data but provides an accurate forecast.
Internet ttraffic monitering anomalous behiviour detectionGyan Prakash
This document discusses a methodology for monitoring internet traffic and detecting anomalous behavior. It begins by noting the challenges of understanding vast quantities of internet traffic data due to the diversity of applications and services. Recent cyber attacks have made it important to develop techniques to analyze communication patterns in traffic data for network security purposes.
The proposed methodology uses data mining and entropy-based techniques to build behavior profiles of internet backbone traffic. It involves clustering traffic based on communication patterns, automatically classifying behaviors, and modeling structures for analysis. The methodology is validated using data sets from internet core links. It aims to automatically discover significant behaviors, provide interpretations, and quickly identify anomalous events like scanning or denial of service attacks.
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This document discusses detecting false data injection attacks using k-means clustering. It begins with an abstract that describes implementing detection of inside attacks in a sub-network using cameras. When an outside person pauses the camera for a specific amount of time, the server can detect this as an inside attack and notify the administrator. The document then reviews related work on cyber attacks against power grids and state estimation. It proposes a system using cameras to monitor for inside attackers pausing cameras. When this occurs, the server will detect an inside attack and inform the administrator. The key algorithm discussed is k-means clustering to classify sensor data and detect attacks.
An intrusion detection algorithm for amiIJCI JOURNAL
Nowadays, using the smart metering devices for energy users to manage a wide variety of subscribers,
reading devices for measuring, billing, disconnection and connection of subscribers’ connection
management is an important issue. The performance of these intelligent systems is based on information
transfer in the context of information technology, so reported data from network should be managed to
avoid the malicious activities that including the issues that could affect the quality of service the system. In
this paper for control of the reported data and to ensure the veracity of the obtained information, using
intrusion detection system is proposed based on the support vector machine and principle component
analysis (PCA) to recognize and identify the intrusions and attacks in the smart grid. Here, the operation of
intrusion detection systems for different kernel of SVM when using support vector machine (SVM) and PCA
simultaneously is studied. To evaluate the algorithm, based on data KDD99, numerical simulation is done
on five different kernels for an intrusion detection system using support vector machine with PCA
simultaneously. Also comparison analysis is investigated for presented intrusion detection algorithm in
terms of time - response, rate of increase network efficiency and increase system error and differences in
the use or lack of use PCA. The results indicate that correct detection rate and the rate of attack error
detection have best value when PCA is used, and when the core of algorithm is radial type, in SVM
algorithm reduces the time for data analysis and enhances performance of intrusion detection.
IRJET- False Data Injection Attacks in Insider AttackIRJET Journal
This document discusses false data injection attacks and insider threats in computer networks. It proposes a system that uses cameras to detect when an unauthorized external person enters a restricted area of a network for an extended period of time. When this suspicious activity is detected, the system will notify administrators about a potential insider attack. The system aims to increase network security by monitoring for insider threats and detecting unauthorized access. It analyzes existing research on false data injection attacks and vulnerabilities in state estimation systems. The proposed architecture and algorithms, such as Bloom filters and AES encryption, are designed to securely detect and report insider threats in computer networks.
Safeguard the Automatic Generation Control using Game Theory TechniqueIRJET Journal
This document discusses using game theory techniques to safeguard the automatic generation control (AGC) in smart grids from false data injection attacks. It first provides background on AGC and how false data can affect its performance and potentially cause blackouts. It then discusses using a game theory model to represent the interactions between attackers injecting false data and defenders protecting the system. The risks of different attack events are calculated and fed into the game model. Dynamic programming is used to determine optimal defense strategies based on resource constraints. Simulation results show the approach can minimize risks to the AGC under different attack scenarios.
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This document summarizes a research paper that models the reliability of a cyber-physical system (CPS) with intrusion detection and response systems. It describes the reference CPS model, which includes mobile sensor nodes and a control unit. It also outlines the security failure models, attack models including persistent, random and insidious attackers, and the host-level and system-level intrusion detection techniques used. These include behavior rule specification and vector similarity specification for host detection, and majority voting for system detection. Parameters for the detection accuracy are defined. The goal is to maximize CPS lifetime by setting detection and response strengths to balance energy usage and intrusion tolerance.
IRJET- An Intrusion Detection Framework based on Binary Classifiers Optimized...IRJET Journal
This document proposes an intrusion detection framework that uses multiple binary classifiers optimized by a genetic algorithm. It analyzes decision trees, naive Bayes, and support vector machines to classify network connections as normal or attacks based on the NSL-KDD dataset. The classifiers are aggregated and a genetic algorithm is used to generate high-quality solutions. Experimental results show that the proposed method achieves 99% accuracy in intrusion detection, outperforming single classification techniques. The goal is to develop an application that can efficiently process network data and identify intrusion risks.
Cyber-Defensive Architecture for Networked Industrial Control SystemsIJEACS
This paper deals with the inevitable consequence of the convenience and efficiency we benefit from the open, networked control system operation of safety-critical applications: vulnerability to such system from cyber-attacks. Even with numerous metrics and methods for intrusion detection and mitigation strategy, a complete detection and deterrence of internal code flaws and outside cyber-attacks has not been found and would not be found anytime soon. Considering the ever incompleteness of detection and prevention and the impact and consequence of mal-functions of the safety-critical operations caused by cyber incidents, this paper proposes a new computer control system architecture which assures resiliency even under compromised situations. The proposed architecture is centered on diversification of hardware systems and unidirectional communication from the proposed system in alerting suspicious activities to upper layers. This paper details the architectural structure of the proposed cyber defensive computer control system architecture for power substation applications and its validation in lab experimentation and on a cybersecurity testbed.
This document proposes a novel standalone implementation of a multi-deep neural network (MDNN) controller for DC-DC converters that is resilient to sensor attacks. The MDNN controller combines a deep neural network (DNN) controller and an error detection network (EDN) to detect and mitigate false data injection attacks at the sensor level. The MDNN controller is tested in MATLAB simulations under various disturbances and attack scenarios, demonstrating its effectiveness in maintaining closed-loop control of the DC-DC converter while detecting and mitigating sensor attacks. This approach eliminates the need for traditional proportional-integral controllers and provides a model-free methodology to securely and robustly control the converter against sensor attacks and system variations.
An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based...ijsrd.com
This document presents an analytical model to analyze the tradeoff between data aggregation and latency in cluster-based wireless sensor networks. The model develops equations to calculate the energy savings from data aggregation due to reduced transmissions and the latency incurred due to aggregating data at cluster heads. It then defines a cost function as a weighted sum of these two factors. The goal is to use this cost function to determine optimal design thresholds for wireless sensor network deployments based on their lifetime and latency requirements. The model is evaluated through MATLAB simulations to validate its effectiveness in analyzing the aggregation-latency tradeoff.
IRJET- A Secured Method of Data Aggregation for Wireless Sensor Networks in t...IRJET Journal
This document summarizes a research paper that proposes using iterative filtering (IF) algorithms to securely aggregate sensor data in wireless sensor networks in the presence of collusion attacks. Collusion attacks occur when nodes secretly or illegally agree to corrupt transmitted data, causing a mismatch in data aggregation. The paper aims to implement IF algorithms to avoid collusion attacks. IF algorithms work by repeatedly applying a function, using the output of one iteration as the input for the next. This helps maximize the likelihood of inferring accurate data from partially observed systems. The document outlines the methodology, benefits of IF algorithms, and basic steps of how they are implemented.
DDOS ATTACKS DETECTION USING DYNAMIC ENTROPY INSOFTWARE-DEFINED NETWORK PRACT...IJCNCJournal
This document discusses a study that proposes a dynamic entropy-based method for detecting DDoS attacks in SDN environments. The study introduces using dynamic threshold values that change over time based on the entropy value variability of network traffic windows, to help predict system state and detect new attacks more accurately compared to static thresholds. The study also evaluates the proposed method in a practical SDN testbed environment, not just in simulations, and finds it can rapidly detect DDoS attacks with high accuracy.
DDoS Attacks Detection using Dynamic Entropy in Software-Defined Network Prac...IJCNCJournal
Software-Defined Network (SDN) is an innovative network architecture with the goal of providing the flexibility and simplicity in network operation and management through a centralized controller. These features help SDN to easily adapt tothe expansion of networkrequirements, but it is also a weakness when it comes to security. With centralized architecture, SDN is vulnerable to cyber-attacks, especially Distributed Denial of Service (DDoS) attack. DDoS is a popular attack type which consumes all network resources and causes congestion in the entire network. In this research, we will introduce a DDoS detection model based on the statistical method with a dynamic threshold value that changes over time. Along with the simulation result, we build a practical SDN model to apply our method, the results show that our method can detectD DoS attacks rapidly with high accuracy.
A robust algorithm based on a failure sensitive matrix for fault diagnosis of...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
This document discusses energy theft in Advanced Metering Infrastructure (AMI) systems. It describes how AMI systems are vulnerable to energy theft through various attacks, such as interrupting meter measurements, tampering with stored demand data, and modifying communications between meters and utilities. It presents an attack tree to categorize different goals and techniques for energy theft. The document also analyzes vulnerabilities in a studied AMI system, such as insufficient physical protections, unsecured optical communications, and failures to authenticate endpoints and detect replay attacks. It argues these vulnerabilities stem from design assumptions around the physical limitations of meters, insecure near-field communication, lack of firmware integrity protection, and an untrusted communications backhaul and endpoints.
This document presents a study on a proposed distributed attack detection algorithm using experimental and simulation analysis. The key points are:
1) The algorithm detects distributed denial of service attacks in wireless sensor networks using detector nodes that monitor traffic and reconstruct patterns to identify attacks.
2) Performance is affected by algorithmic parameters like time epoch length and number of detector nodes, and network parameters like node density and energy.
3) Simulation experiments quantify the attack detection rate, false positive/negative rates, and node energy utilization under variations in these parameters.
A Survey On Intrusion Detection SystemsMary Calkins
This document provides a survey of intrusion detection systems. It discusses the two main types of intrusion detection systems: signature-based systems and anomaly-based systems. Signature-based systems rely on pattern matching to detect known attacks, while anomaly-based systems build a model of normal behavior and detect deviations from that model, allowing them to find unknown attacks. The document then reviews several anomaly detection techniques, including statistical models like mean/standard deviation and Markov processes, machine learning approaches like neural networks and support vector machines, and knowledge-based systems. It provides examples of specific anomaly detection systems and algorithms that have been implemented.
IRJET-Electrical Power Robbery Detection and Transformer Fault Detection IRJET Journal
This document describes an electrical power theft detection and transformer fault detection system. The system uses remote data transmission via GSM to identify unauthorized tapping on power distribution lines and monitor the condition of distribution transformers. Sensors measure transformer parameters like currents, voltages, temperatures and oil conditions. A microcontroller analyzes the data and sends alerts via SMS if power theft or transformer faults are detected. This provides a low-cost way to remotely monitor transformers and detect power theft locations without human intervention.
FUZZY LOGIC APPROACH FOR FAULT DIAGNOSIS OF THREE PHASE TRANSMISSION LINEJournal For Research
This document summarizes a journal article that proposes using fuzzy logic to diagnose faults on three-phase transmission lines. It begins with an abstract of the journal article, which describes using fuzzy logic as an intelligent technique to quickly and accurately identify the type of fault that occurs on a transmission system. It then provides background on transmission line faults, fault types, and challenges with transmission line protection. The document outlines the proposed fuzzy logic approach, including defining fault types as fuzzy sets and developing if-then rules to relate transmission line voltages and currents to faults. Simulation results are presented showing the fuzzy logic approach can identify different fault types based on the current responses. The conclusion is that the proposed fuzzy logic method allows for fast and reliable fault detection on transmission
Similar to JPJ1439 On False Data-Injection Attacks against Power System State Estimation: Modeling and Countermeasures (20)
JPEEE1440 Cascaded Two-Level Inverter-Based Multilevel STATCOM for High-Pow...chennaijp
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JPN1423 Stars a Statistical Traffic Patternchennaijp
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JPN1422 Defending Against Collaborative Attacks by Malicious Nodes in MANETs...chennaijp
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JPN1420 Joint Routing and Medium Access Control in Fixed Random Access Wire...chennaijp
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JPN1418 PSR: A Lightweight Proactive Source Routing Protocol For Mobile Ad H...chennaijp
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JPN1417 AASR: An Authenticated Anonymous Secure Routing Protocol for MANETs ...chennaijp
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JPN1416 Sleep Scheduling for Geographic Routing in Duty-Cycled Mobile Sensor...chennaijp
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JPN1415 R3E: Reliable Reactive Routing Enhancement for Wireless Sensor Netw...chennaijp
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JPN1411 Secure Continuous Aggregation in Wireless Sensor Networkschennaijp
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JPN1414 Distributed Deployment Algorithms for Improved Coverage in a Networ...chennaijp
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JPN1413 An Energy-Balanced Routing Method Based on Forward-Aware Factor for...chennaijp
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JPN1412 Transmission-Efficient Clustering Method for Wireless Sensor Networ...chennaijp
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JPN1410 Secure and Efficient Data Transmission for Cluster-Based Wireless Se...chennaijp
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JPN1409 Neighbor Table Based Shortcut Tree Routing in ZigBee Wireless Networkschennaijp
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JPN1408 Hop-by-Hop Message Authentication and Source Privacy in Wireless Sen...chennaijp
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JPN1406 Snapshot and Continuous Data Collection in Probabilistic Wireless S...chennaijp
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JPN1405 RBTP: Low-Power Mobile Discovery Protocol through Recursive Binary T...chennaijp
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JPN1404 Optimal Multicast Capacity and Delay Tradeoffs in MANETschennaijp
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JPM1410 Images as Occlusions of Textures: A Framework for Segmentationchennaijp
This document proposes a new mathematical and algorithmic framework for unsupervised image segmentation. It models images as occlusions of random textures, called textures, and shows that local histograms can segment such images. The framework draws on nonnegative matrix factorization and image deconvolution. Results on synthetic and real histology images show promise. Existing segmentation methods make assumptions that often fail on complex tissues, while proposed framework proves local histograms of texture-occluded images combine the textures' value distributions, allowing segmentation.
JPM1407 Exposing Digital Image Forgeries by Illumination Color Classificationchennaijp
This document summarizes a research paper that proposes a new method for detecting digital image forgeries by analyzing inconsistencies in the color of illumination across image regions. Existing illumination-based forgery detection methods have limitations like requiring manual interaction or not handling specular regions well. The proposed method extracts texture and edge-based features from illuminant estimates of similar image regions using physics and statistics-based models. These features are then classified using a machine learning approach to detect forgeries with minimal user interaction. The method achieved detection rates of 86% on a benchmark dataset and 83% on images collected from the internet.
Social media management system project report.pdfKamal Acharya
The project "Social Media Platform in Object-Oriented Modeling" aims to design
and model a robust and scalable social media platform using object-oriented
modeling principles. In the age of digital communication, social media platforms
have become indispensable for connecting people, sharing content, and fostering
online communities. However, their complex nature requires meticulous planning
and organization.This project addresses the challenge of creating a feature-rich and
user-friendly social media platform by applying key object-oriented modeling
concepts. It entails the identification and definition of essential objects such as
"User," "Post," "Comment," and "Notification," each encapsulating specific
attributes and behaviors. Relationships between these objects, such as friendships,
content interactions, and notifications, are meticulously established.The project
emphasizes encapsulation to maintain data integrity, inheritance for shared behaviors
among objects, and polymorphism for flexible content handling. Use case diagrams
depict user interactions, while sequence diagrams showcase the flow of interactions
during critical scenarios. Class diagrams provide an overarching view of the system's
architecture, including classes, attributes, and methods .By undertaking this project,
we aim to create a modular, maintainable, and user-centric social media platform that
adheres to best practices in object-oriented modeling. Such a platform will offer users
a seamless and secure online social experience while facilitating future enhancements
and adaptability to changing user needs.
Exploring Deep Learning Models for Image Recognition: A Comparative Reviewsipij
Image recognition, which comes under Artificial Intelligence (AI) is a critical aspect of computer vision,
enabling computers or other computing devices to identify and categorize objects within images. Among
numerous fields of life, food processing is an important area, in which image processing plays a vital role,
both for producers and consumers. This study focuses on the binary classification of strawberries, where
images are sorted into one of two categories. We Utilized a dataset of strawberry images for this study; we
aim to determine the effectiveness of different models in identifying whether an image contains
strawberries. This research has practical applications in fields such as agriculture and quality control. We
compared various popular deep learning models, including MobileNetV2, Convolutional Neural Networks
(CNN), and DenseNet121, for binary classification of strawberry images. The accuracy achieved by
MobileNetV2 is 96.7%, CNN is 99.8%, and DenseNet121 is 93.6%. Through rigorous testing and analysis,
our results demonstrate that CNN outperforms the other models in this task. In the future, the deep
learning models can be evaluated on a richer and larger number of images (datasets) for better/improved
results.
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Understanding Cybersecurity Breaches: Causes, Consequences, and PreventionBert Blevins
Cybersecurity breaches are a growing threat in today’s interconnected digital landscape, affecting individuals, businesses, and governments alike. These breaches compromise sensitive information and erode trust in online services and systems. Understanding the causes, consequences, and prevention strategies of cybersecurity breaches is crucial to protect against these pervasive risks.
Cybersecurity breaches refer to unauthorized access, manipulation, or destruction of digital information or systems. They can occur through various means such as malware, phishing attacks, insider threats, and vulnerabilities in software or hardware. Once a breach happens, cybercriminals can exploit the compromised data for financial gain, espionage, or sabotage. Causes of breaches include software and hardware vulnerabilities, phishing attacks, insider threats, weak passwords, and a lack of security awareness.
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Conservation of those heritages, ancient marvels,a nd history was in dire need, so we proposed the Conservation of Taksar through economic regeneration because the lack of economy was the main reason for the people to leave the settlement and the reason for the overall declination.
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JPJ1439 On False Data-Injection Attacks against Power System State Estimation: Modeling and Countermeasures
1. On False Data-Injection Attacks against Power System State
Estimation: Modeling and Countermeasures
ABSTRACT:
It is critical for a power system to estimate its operation state based on meter
measurements in the field and the configuration of power grid networks. Recent
studies show that the adversary can bypass the existing bad data detection
schemes,posing dangerous threats to the operation of power grid systems.
Nevertheless, two critical issues remain open: 1) how can an adversary choose the
meters to compromise to cause the most significant deviation of the system state
estimation, and 2) how can a system operator defend against such attacks? To
address these issues, we first study the problem of finding the optimal attack
strategy—i.e., a data-injection attacking strategy that selects a set of meters to
manipulate so as to cause the maximum damage. We formalize the problem and
develop efficient algorithms to identify the optimal meter set. We implement and
test our attack strategy on various IEEE standard bus systems, and demonstrate its
superiority over a baseline strategy of random selections. To defend against false
data-injection attacks, we propose a protection-based defense and a detection-based
defense, respectively. For the protection-based defense, we identify and
2. protect critical sensors and make the system more resilient to attacks. For the
detection-based defense, we develop the spatial-based and temporal-based
detection schemes to accurately identify data-injection attacks.
EXISTING SYSTEM:
State estimation has been widely used by Energy Management Systems (EMS) at
the control center to ensure that the power grid is running in desired states. It
provides the estimation of system states in real time based on meter measurements
in the field. The meter measurements are collected by the Supervisory Control and
Data Acquisition (SCADA) Systems and processed by a state estimator to filter the
measurement noise and to detect gross errors. The results of state estimation are
then used by applications at the control center, for purposes such as contingency
analysis, optimal power flow, economic dispatch, and others.
One can see that state estimation plays a critical role in the stability of power grid
systems. Meter measurements collected via the SCADA system contain not only
measurement noise due to the finite accuracy of meters and communication media,
but also errors caused by various issues for example, meters with faulty connection
and calibration.
3. To reduce the impact of noise and errors, power system researchers have
developed numerous methods to process meter measurements after the state
estimation process. The essential goal of these methods is to leverage the
redundancy of multiple measurements to identify and remove anomalies. While
most existing techniques for protecting power grid systems were designed to
ensure system reliability (i.e.,against random failures), recently there have been
growing concerns in smart grid initiatives on the protection against malicious cyber
attacks . There are growing concerns in the smart grid on protection against
malicious cyber threats and the operation and control of smart grid depend on a
complex cyberspace of computers, software, and communication technologies.
Because the measurement component supported by smart equipment (e.g., smart
meters and sensors) plays an important role, it can be a target for attacks. As those
measuring devices may be connected through open network interfaces and lacking
tamper-resistance hardware increases the possibility of being compromised by the
adversary.
DISADVANTAGES OF EXISTING SYSTEM:
1. The adversary can inject false measurement reports to the controller. This
causes the controller to estimate wrong system states, posing dangerous threats to
the operation of the power grid system.
4. 2. If the controller to estimates wrong system states, posing dangerous threats to
the operation of the power grid system.
PROPOSED SYSTEM:
In this paper, we study a novel problem of defending against false data-injection
attacks from the system operator’s point of view. Because most adversaries are
limited in the amount of resources they possess, we first consider a least-effort
attack model—i.e., the objective of the adversary is to identify the minimum
number of meters that one has to manipulate to change a predetermined number of
state variables (so as to launch a false data-injection attack accordingly).
We prove the NP-hardness of this problem by reduction from the minimum sub
additive join problem. To address this problem in a practical setting, we develop a
linear transformation-based approach, which finds the optimal solution through the
matrix transformation. Nevertheless, the computation complexity of the matrix
transformation grows exponentially with the size of the power network. To address
this issue, we develop a heuristic yet extremely efficient approach. Specifically,
through the analysis of the H matrix, for a set of bus state variables, the adversary
needs to compromise less meters when the buses are connected to one another with
the largest degrees and connected to the least number of buses beyond its area.
5. Based on this insight, we divide the network into a number of overlapping areas.
The linear transformation or brute-force search (BF) can be used to identify the
optimal set of meters for individual small areas and then derive the set of meters
for the whole network.
We have implemented our proposed heuristic-based approach on power system
state manipulation on various IEEE standard buses. Our extensive experimental
data validate the feasibility and effectiveness of the developed approach.
ADVANTAGES OF PROPOSED SYSTEM:
1. The spatial-based detection algorithm is able to recognize at least 95 percent of
the false data-injection attacks once the attack changes more than 6 percent of the
state variable values
2. The temporal-based detection algorithm can identify the compromised meters
that send manipulated measurements quickly.
6. SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
System : Pentium IV 2.4 GHz.
Hard Disk : 40 GB.
Floppy Drive : 1.44 Mb.
Monitor : 15 VGA Colour.
Mouse : Logitech.
Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
Operating system : Windows XP/7.
Coding Language : JAVA/J2EE
IDE : Netbeans 7.4
Database : MYSQL
7. REFERENCE:
Qingyu Yang, Member, IEEE, Jie Yang, Wei Yu, Dou An,Nan Zhang, and Wei
Zhao, Fellow, IEEE”On False Data-Injection Attacks against Power System
State Estimation: Modeling and Countermeasures”IEEE TRANSACTIONS
ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 25,NO. 3,MARCH
2014.