Intrusion prevension
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The document discusses intrusion prevention and intrusion detection systems. It defines intrusion as unauthorized access aimed at compromising network security assets. Intrusion detection systems (IDS) monitor network traffic to detect intrusions, while intrusion prevention systems (IPS) can also block attacks in real-time. An IPS provides increased visibility beyond a firewall by using techniques like signature detection, anomaly detection, and protocol analysis to identify intrusions and threats. The document outlines challenges faced by IPS like evasion techniques, and discusses next-generation IPS features like intelligent correlation, anomaly detection, and using global threat intelligence.
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Intrusion Detection Presentation
Intrusion detection systems monitor computer networks and systems for unauthorized access or activity. There are two main types: network-based systems examine network traffic for attacks, while host-based systems check the integrity of individual systems. Methods include knowledge-based systems that detect known attacks and behavior-based systems that identify deviations from normal usage profiles. Regular auditing of systems, logs, user rights and files is needed to detect intrusions. While intrusion detection is important for security, intrusion prevention systems that can block attacks in real-time are increasingly replacing detection-only systems.
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Intrusion detection system
It's power-point presentation on software application that monitors a network or systems for malicious activity or policy violations.
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Intrusion Detection Presentation
Intrusion detection systems monitor computer networks and systems for unauthorized access or activity. There are two main types: network-based systems examine network traffic for attacks, while host-based systems check the integrity of individual systems. Methods include knowledge-based systems that detect known attacks and behavior-based systems that identify deviations from normal usage profiles. Regular auditing of systems, logs, user rights and files is needed to detect intrusions. While intrusion detection is important for security, intrusion prevention systems that can block attacks in real-time are increasingly replacing detection-only systems.
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This document discusses intrusion detection systems (IDS). An IDS monitors network or system activities for malicious activities or policy violations. It detects intrusions by analyzing information sources like network traffic, system logs, and user activities. The analysis engine identifies intrusive behaviors by comparing activities to normal profiles or thresholds. When threats are detected, the IDS responds by alerting administrators or taking actions like blocking traffic. IDS use anomaly detection or signature-based methods to classify activities as normal or intrusive.
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hey guys here comes my new implementation of my learning i.e the IDS a concept of network security go through it and add your valuable comments
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Intrusion detection
8.1 Intruders
8.2 Classes of intruders
8.3 Examples of Intrusion
8.4 Security Intrusion & Detection
8.5 Intrusion Techniques
8.6 Intrusion Detection Systems
8.7 IDS Principles
8.8 IDS Requirements
8.9 Host-Based IDS
8.10 Network-Based IDS
8.11 Intrusion Detection Exchange Format
8.12 Honeypot
Autonomic Anomaly Detection System in Computer Networks
This paper describes how you can protect your system from Intrusion, which is the method of Intrusion Prevention and Intrusion Detection .The underlying premise of our Intrusion detection system is to describe attack as instance of ontology and its first need is to detect attack. In this paper, we propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection over unlabeled HTTP traffic streams in computer networks. The framework holds potential for self-governing: self-labeling, self-updating and self-adapting. Our structure employs the Affinity Propagation (AP) algorithm to learn a subject’s behaviors through dynamical clustering of the streaming data. It automatically labels the data and adapts to normal behavior changes while identifies anomalies.
Intrusion Detection System(IDS)
This document provides an overview of intrusion detection systems (IDS). It discusses two main types of IDS - network intrusion detection systems (NIDS) which monitor network traffic to and from all devices, and host-based intrusion detection systems (HIDS) which monitor traffic to and from individual hosts. It also describes two common detection methods - misuse detection which looks for known attack patterns, and anomaly detection which identifies abnormal network traffic.
Idps
Intrusion detection systems aim to detect unauthorized access to computer systems and networks. There are three main types: anomaly-based detection identifies deviations from normal behavior profiles; signature-based detection looks for known threat patterns; and hybrid detection combines the two approaches. Intrusion detection systems are also classified based on their monitoring scope, including network-based systems that monitor network traffic and host-based systems that monitor logs and activities on individual computers. Recent research focuses on developing more effective hybrid systems and methods that can detect both known and unknown threats.
Idps technology starter v2.0
This document discusses intrusion detection and prevention systems. It defines intrusion detection as detecting inappropriate, incorrect, or anomalous activity. Intrusion detection systems (IDS) and intrusion prevention systems (IPS) can be used to determine if a network or server has experienced an unauthorized intrusion. IDS and IPS systems work by using network sensors to detect intrusions or host agents to detect intrusions on individual systems. The document discusses different IDS and IPS products and how to implement them, including in network or host modes. It also covers signature tuning and different deployment models.
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Surveying cross layer protocols in ws ns
CEDA
Agenda
Ahmed Hamed, Hussein Abd Elrahman, and Rizk Tawfik
Ain Shams University - Faculty of Engineering
2826-Apr-18
- The Cross-layer Energy-Delay Aware (CEDA) protocol aims to optimize energy consumption and end-to-end delay in WSNs.
- It allows interaction between the network layer, MAC layer, and physical layer to share information that can help optimize the protocol's objectives.
- For example, the physical layer shares link quality and energy level information with the MAC layer to help in channel allocation. The network layer considers this along with routing to minimize delay.
- Simulation
Service level management
The document discusses service level agreements (SLAs) between network service providers and customers. It covers the motivation for SLAs, defining service level parameters and objectives, developing the SLA, and ongoing management to meet the SLA. Key points include identifying meaningful, measurable parameters; setting realistic objectives; monitoring performance; and outlining consequences for not meeting objectives like restoring service or financial penalties. The goal is an agreed understanding between parties on expected network service levels.
Energy harvesting sensor nodes
The document discusses energy harvesting for sensor nodes. It describes various energy harvesting architectures and technologies that can power sensor nodes, such as solar, piezoelectric, wind, and radio frequency. It provides examples of sensor node implementations that use different energy storage solutions like batteries, supercapacitors, and tiered storage. The document also discusses implications for sensor network design, including performance adaptation techniques at the node and network levels to enable energy neutral operation of harvesting-powered sensor networks.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
Abstract- The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
Energy conservation in wireless sensor networks
The document discusses energy conservation techniques in wireless sensor networks. It begins with an introduction to wireless sensor networks and identifies power consumption as a major challenge. It then outlines the typical architecture of a wireless sensor node and examines the power breakdown across different components. The document proceeds to discuss basic approaches to energy conservation, including duty cycling, data-driven, and mobility-based techniques. It also mentions future work in integrating different approaches into a single solution and addresses questions.
Localization in wsn
The document discusses localization techniques in wireless sensor networks (WSNs). It begins with an introduction to WSNs and why GPS is not suitable for localization in these networks. It then covers taxonomy of localization methods, including target/source localization, node self-localization techniques like range-based and range-free methods. Specific techniques discussed include DV-Hop, pattern matching localization, and classifications like centralized vs distributed localization. The summary restates key points about distance estimation methods, single/multiple localization, and classifications of localization approaches.
Routing
This document discusses routing protocols in wireless sensor networks. It begins with an introduction to routing challenges in WSNs such as limited energy, processing, and storage in sensor nodes. It then covers different routing techniques including flat routing protocols like SPIN, directed diffusion, and rumor routing. Hierarchical routing protocols discussed include LEACH, PEGASIS, TEEN, and APTEEN. Finally, it briefly mentions location-based routing and the GEAR protocol.
Wsn security issues
This document discusses security issues in wireless sensor networks (WSNs). It notes that WSNs require a high level of security due to operating in hostile environments, but their limited resources pose a challenge. It outlines various WSN security requirements and categorizes common attacks based on the attacker's capabilities and the protocol stack layer targeted. Finally, it acknowledges that while some challenges have been addressed, many open problems remain due to conflicts between security, survivability, and resource constraints in WSNs.
Trusted systems
This document discusses trusted systems and the concept of a Trojan horse. It defines trusted systems as systems used to enhance security defenses against intruders and malware. It describes multilevel security as allowing multiple data classification levels, with mandatory access control enforcing that information does not flow to unauthorized users. The document also discusses how Trojan horses can provide unauthorized remote access if installed on a user's computer.
opnet
The document provides an overview of OPNET Modeler, a network simulation tool. It describes OPNET Modeler's architecture, which includes tools for model specification, data collection and simulation, and analysis. It also discusses how to locate models and components using the model library and its organization. The goal is to help users understand what problems can be solved with OPNET Modeler and how to get started using it.
Wsn security issues
This document discusses security issues in wireless sensor networks (WSNs). It notes that WSNs require a high level of security due to operating in hostile environments, but their limited resources pose a challenge. It outlines various WSN security requirements and categorizes common attacks based on the attacker's capabilities and the protocol stack layer targeted. Finally, it acknowledges that while some challenges have been addressed, many open problems remain due to conflicts between security, survivability, and resource constraints in WSNs.
Sdn pres v2-Software-defined networks
This document discusses SDN security. It outlines how SDN allows for centralized control of network flows and security policies. However, the centralized nature of SDN also introduces new threats, such as attacks on controllers or switches. Potential threats are discussed, such as DoS attacks, traffic manipulation, or vulnerabilities in controllers/applications. Mitigation techniques are proposed, such as monitoring for abnormal behavior, access control, and replication of controllers. Future work may focus on improving the security and dependability of SDN through techniques like dynamic switch association and diversity.
Digital forensics ahmed emam
Digital forensics is the application of science to solve legal problems involving digital evidence. It has emerged since the 1980s as computer crimes have grown. There are challenges to reliability such as standards, controls, and new technologies like cloud and solid state drives. Case studies demonstrate how digital evidence can solve old cases, as with the BTK killer through metadata on a word document. The field faces ongoing challenges but continued research supports its validity in courts of law.
Digital forensics.abdallah
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Cloud computing final show
This document provides an overview of cloud computing. It begins with an introduction and defines cloud computing, discussing its history and key attributes. It then covers the different cloud models including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). The document also discusses cloud security and privacy concerns, outlining various security threats and solutions. It concludes by emphasizing the importance of cloud computing and its future.
Incident handling.final
The document discusses incident handling and provides details about each step of the incident handling life cycle. It begins with an introduction on the importance of incident handling plans. It then defines what constitutes an incident and provides examples of different incident types and categories. The document outlines the key steps in the incident handling life cycle as preparation, identification, detection, analysis, containment, eradication, recovery, and follow up. For each step, it provides details on goals, definitions, and best practices.
Malewareanalysis presentation
The document discusses malware analysis and provides an overview of key topics including:
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pentration testing
Penetration testing presentation given to Dr. Ashraf Tamam by Mohamed Abd El-Azeem, Ahmed Yousef Eissa, and Ahmed Alaa El-Din. The presentation covered penetration testing and was delivered to the named professor and students.
Automatic left ventricle segmentation
This document summarizes an automatic left ventricle segmentation technique using iterative thresholding and an active contour model adapted for short-axis cardiac MRI images. It begins with background on image segmentation and its applications. Then, it reviews related work on cardiac segmentation techniques and their limitations. The proposed method segments the endocardium using iterative thresholding and the epicardium using an active contour model. It estimates blood and myocardial intensities, applies region growing to segment the endocardium in each slice, and propagates the segmentation to remaining slices. Finally, it measures left ventricle volume and compares the results to manual segmentation.
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一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理
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办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
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Null Bangalore | Pentesters Approach to AWS IAM
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
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Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
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The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
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Intrusion prevension
- 1. Intrusion Prevention Presenter : Sherif Sadek Ali 1
- 2. Introduction 2 • Network Security: consists of the provisions and policies adopted by network administrators to prevent and monitor unauthorized access, misuse, modification, or denial of network resources. • Intrusion: Actions aimed to compromise and gain unauthorized access to the security assets. • We need more than a FIREWALL.
- 3. Need for Intrusion Detection 3 • What the firewall can’t see: Signatures Based on current exploits (worm, viruses) Detect Malware, Spyware. Malicious traffic detection, traffic normalization.
- 4. Need for Intrusion Detection 4 • What the firewall can’t see: Zero day exploits (XSS, SQL Injection) Not caught by signatures. Not detected by normalization triggers. Specific to custom applications. Social engineering Verbal communication. Malicious access via legitimate credentials. Poor configuration management Misconfigurations allow simple access not detected. Increases attack vectors.
- 6. Definition: IDS 6 Intrusion Detection System (IDS) Is the ability to detect intruders in the Network. A IDS has sensors that monitor the traffic entering and leaving a firewall, and reports back to the central device for analysis. “Promiscuous monitoring mode”
- 7. Definition: IPS 7 Intrusion Prevention System (IPS) A technology that monitors network traffic to immediately react to block a malicious attack, for a quick action to block an attack. One of the major differences between a NIDS and a NIPS is its location, as it would be located “in-line” on the firewall.
- 8. Difference 8
- 9. Key Performance Metrics 9 • False Positive • True Positive • False Negative • True Negative
- 10. How it Works Detection Mechanisms • Protocol Detection • Signature Detection (Statically Based) • Profile Detection (Statistically Based) 10
- 11. Signature Detection Intrusion Patterns activities pattern matching intrusion if (src_ip == dst_ip) then “Attack” 11
- 12. Host Based IPS (HIPS) HIPS Operating System Events Network Packets Collected 12
- 13. Anomaly Detection (Adaptive) activity measurements 0 10 20 30 40 50 60 70 80 90 CPU Process Size normal profile abnormal probable intrusion (AD) Analyzes TCP/IP parameters: Normalization, Fragmentation/reassembly, Header & checksum problems With Relatively high false positive rate, anomalies can just be new normal activities. 13
- 14. Challenges How Attackers Used to do: • Overwhelm by Flooding. • Disguise by Fragmenting. • Hide by Encrypting. • Confuse by Obfuscation. 14
- 15. New Approach Behavior Detect anomalies in configuration, connections and data flow Network Know what’s there what’s vulnerable, and what’s under attack Application Identify change and enforce policy on hundreds of applications Identity Know who is doing what, with what, and where 15
- 16. Next-Generation IPS • New Hardware Design • Intelligent Correlation to the Target • Intelligent Anomaly Detection • Intelligent Application Violation • Global Network Threat Intelligence & Correlation 16
- 17. Intelligent Correlation to the Target IPS SENSOR IPS SENSOR IPS SENSOR Management CENTER IPS SENSOR Blocked Event Logged LINUX SERVER WINDOWS SERVER Linux server not vulnerableWindows server vulnerable Attack Blocked Attack Is Correlated to Targets Latest Windows attack targets Microsoft Windows Server and Linux Server. Attacks are correlated to targets. High-priority event generated for Windows Server target. 17
- 18. Intelligent Anomaly Detection IPS SENSOR IPS SENSOR IPS SENSOR Management CENTER IPS SENSOR Abnormal Behavior Logged & Alerts Triggered IT Remediates Hosts Hosts Compromised New rogue host connects internally. IPS Sensor detects new host and abnormal server behavior. Management Center triggers alerts for IT to remediate. New Asset Detected Abnormal Behavior Detected 18
- 19. Intelligent Application Violation IPS SENSOR IPS SENSOR IPS SENSOR Management CENTER IPS SENSOR P2P App Triggers Whitelist Violation Compliance Event Logged & User Identified Security team uses compliance whitelists to detect IT policy violations. Host detected using Skype. User identified and then contacted by IT. IT Contacts User 19
- 20. Global Network Threat Intelligence • Based on IP/Domain Reputation. 20
- 22. Conclusion 22 • Think, Evaluate, Review Logs, Implement Strategies. • It is impossible to achieve 100% total Security. • Security is not just a Network Appliance, It is a Concept.
- 23. Questions 23