This document discusses honeypots, which are fake computer systems designed to attract hackers. Honeypots monitor the activity of hackers and collect data on their tactics. They are classified based on their level of interaction (low or high) and implementation environment (research or production). Honeypots provide advantages like detecting new hacking tools and minimizing resources needed. They also have disadvantages like limited visibility and risk of being hijacked. The document discusses practical applications of honeypots for preventing attacks, detecting intrusions, and conducting cyber forensics investigations.
Today internet security is a serious problem. For every consumer and business that is on the Internet,
viruses, worms and crackers are a few security threats. There are the obvious tools that aid information security
professionals against these problems such as anti-virus software, firewalls and intrusion detection systems, but
these systems can only react to or prevent attacks-they cannot give us information about the attacker, the tools
used or even the methods employed. Given all of these security questions honeypots are a novel approach to
network security and security research alike. It is a resource, which is intended to be attacked and compromised to
gain more information about the attacker and the used tools. It can also be deployed to attract and divert an
attacker from their real targets. Honeypots is an additional layer of security. Honeypots have the big advantage that
they do not generate false alerts as each observed traffic is suspicious, because no productive components are
running on the system. The levels of interaction determines the amount of functionality a honeypots provides that
is low and high interactions.
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.
AN ISP BASED NOTIFICATION AND DETECTION SYSTEM TO MAXIMIZE EFFICIENCY OF CLIE...IJNSA Journal
End users are increasingly vulnerable to attacks directed at web browsers which make the most of popularity of today’s web services. While organizations deploy several layers of security to protect their systems and data against unauthorised access, surveys reveal that a large fraction of end users do not utilize and/or are not familiar with any security tools. End users’ hesitation and unfamiliarity with security products contribute vastly to the number of online DDoS attacks, malware and Spam distribution. This work on progress paper proposes a design focused on the notion of increased participation of internet service providers in protecting end users. The proposed design takes advantage of three different detection tools to identify the maliciousness of a website content and alerts users through utilising Internet Content Adaptation Protocol (ICAP) by an In-Browser cross-platform messaging system. The system also incorporates the users’ online behaviour analysis to minimize the scanning intervals of malicious websites database by client honeypots. Findings from our proof of concept design and other research indicate that such a design can provide a reliable hybrid detection mechanism while introducing low delay time into user browsing experience.
Today internet security is a serious problem. For every consumer and business that is on the Internet,
viruses, worms and crackers are a few security threats. There are the obvious tools that aid information security
professionals against these problems such as anti-virus software, firewalls and intrusion detection systems, but
these systems can only react to or prevent attacks-they cannot give us information about the attacker, the tools
used or even the methods employed. Given all of these security questions honeypots are a novel approach to
network security and security research alike. It is a resource, which is intended to be attacked and compromised to
gain more information about the attacker and the used tools. It can also be deployed to attract and divert an
attacker from their real targets. Honeypots is an additional layer of security. Honeypots have the big advantage that
they do not generate false alerts as each observed traffic is suspicious, because no productive components are
running on the system. The levels of interaction determines the amount of functionality a honeypots provides that
is low and high interactions.
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.
AN ISP BASED NOTIFICATION AND DETECTION SYSTEM TO MAXIMIZE EFFICIENCY OF CLIE...IJNSA Journal
End users are increasingly vulnerable to attacks directed at web browsers which make the most of popularity of today’s web services. While organizations deploy several layers of security to protect their systems and data against unauthorised access, surveys reveal that a large fraction of end users do not utilize and/or are not familiar with any security tools. End users’ hesitation and unfamiliarity with security products contribute vastly to the number of online DDoS attacks, malware and Spam distribution. This work on progress paper proposes a design focused on the notion of increased participation of internet service providers in protecting end users. The proposed design takes advantage of three different detection tools to identify the maliciousness of a website content and alerts users through utilising Internet Content Adaptation Protocol (ICAP) by an In-Browser cross-platform messaging system. The system also incorporates the users’ online behaviour analysis to minimize the scanning intervals of malicious websites database by client honeypots. Findings from our proof of concept design and other research indicate that such a design can provide a reliable hybrid detection mechanism while introducing low delay time into user browsing experience.
The overwhelming threat may be a challenge to
general security system. Fundamentally diverse alert and threat
techniques are been researched in order to reduce deceptive
warnings. Threat Detection Systems generates huge amount of
alerts which becomes challenging to deal with them and prepare
solution. The detection System checks inbound and outbound
network activities and finds an suspicious pattern that indicate
an ongoing steps for attack. Large amount of alert may contain
false alarm therefore need of alert analysis mechanisms to offer
high level information of seriousness of threat, how dangerous
device are and which device admin has to pay more attention. To
solve this query we would make use of time and space based alert
analysis technique that provides a solution in form of attack
graph and its evaluation that provides severity of attack to
administrator.
Client Honeypot Based Drive by Download Exploit Detection and their Categoriz...IJERA Editor
Client side attacks are those which exploits the vulnerabilities in client side applications such as browsers, plug-ins etc. The remote attackers execute the malicious code in end user’s system without his knowledge. Here in this research, we propose to detect and measure the drive by download class of malware which infect the end user’s system through HTTP based propagation mechanism. The purpose of this research is to introduce a class of technology known as client honeypot through which we execute the domains in a virtual machine in more optimized manner. Those virtual machines are the controlled environment for the execution of those URLs. During the execution of the websites, the PE files dropped into the system are logged and further analyzed for categorization of malware. Further the critical analysis has been performed by applying some reverse engineering techniques to categories the class of malware and source of infections performed by the malware.
Everything you really need to know about IDS (Intrusion Detection Systems) Combining with HoneyPots. Deployment and usage techniques used in the past and today. How to setup and deploy onto any network including the cloud. Reasons why this should be used in all networks. How to bring BIG DATA down to Small Data that is easy to understand and monitor.
It’s all over the news that data breaches occur daily! I asked WHY these hackers can download terabytes of data in timespans of months without being noticed. What are these companies paying their SOC team millions of dollars for? How come all the money is going to devices to prevent breaches and little to none in detecting when they occur? Don’t people know there are only two types of companies “those that been hacked, and those that don’t know they been hacked”. What can I do to detect a breach within seconds on any network scale? I think I figured it out. In my talk you’ll learn how you and your clients can benefit by applying my exclusive techniques, which I’ve successfully deployed. So the next time you get hacked the hacker would not be able to steal all those credit cards and photos of that Halloween party.
TAXONOMY BASED INTRUSION ATTACKS AND DETECTION MANAGEMENT SCHEME IN PEER-TOPE...IJNSA Journal
A intrusion provides an unauthorized access, damage or disruption of the network. The process can understand the characteristics and nature of an intruder. The paper presents the taxonomy
consists of the specification of an intruder. Taxonomy provides the classification of intruder and provides mechanism for intruder detection. We found the algorithm for developing an intruder which can be attack at host system or network system. Here provide the mechanism for an intrusion by using the
system attribute and detection mechanism is based on knowledge and behavior of the system. Intrusiondetection mechanism using pattern based and threshold based mechanism for detecting an intruder. An intruder continuously monitored the network and host activities for detecting attack into the network and the task of intrusion-detection is also monitor the usage of such systems and detects the apparition of
insecure states.
Autonomic Anomaly Detection System in Computer Networksijsrd.com
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.
Malware Hunter: Building an Intrusion Detection System (IDS) to Neutralize Bo...Editor IJCATR
Among the various forms of malware attacks such as Denial of service, Sniffer, Buffer overflows are the most dreaded threats to computer networks. These attacks are known as botnet attacks and self-propagating in nature and act as an agent or user interface to control the computers which they attack. In the process of controlling a malware, Bot header(s) use a program to control remote systems through internet with the help of zombie systems. Botnets are collection of compromised computers (Bots) which are remotely controlled by its originator (Bot-Master) under a common Command-and-Control (C&C) structure. A server commands to the bot and botnet and receives the reports from the bot. The bots use Trojan horses and subsequently communicate with a central server using IRC. Botnet employs different techniques like Honeypot, communication protocols (e.g. HTTP and DNS) to intrude in new systems in different stages of their lifecycle. Therefore, identifying the botnets has become very challenging; because the botnets are upgrading their methods periodically for affecting the networks. Here, the focus on addressing the botnet detection problem in an Enterprise Network
This research introduces novel Solution to mitigate the malicious activities of Botnet attacks through the Principle of component analysis of each traffic data, measurement and countermeasure selection mechanism called Malware Hunter. This system is built on attack graph-based analytical models based on classification process and reconfigurable through update solutions to virtual network-based countermeasures.
Detecting Unknown Attacks Using Big Data AnalysisEditor IJMTER
Nowadays threat of previously unknown cyber-attacks are increasing because existing security
systems are not able to detect them. Previously, leaking personal information by attacking the PC or
destroying the system was very common cyber attacks . But the goal of recent hacking attacks has changed
from leaking information and destruction of services to attacking large-scale systems such as critical
infrastructures and state agencies. In the other words, existing defence technologies to counter these attacks
are based on pattern matching methods which are very limited. Because of this fact, in the event of new and
previously unknown attacks, detection rate becomes very low and false negative increases. To defend
against these unknown attacks, which cannot be detected with existing technology, a new model based on
big data analysis techniques that can extract information from a variety of sources to detect future attacks is
proposed. The expectation with this model is future Advanced Persistent Threat (APT) detection and
prevention.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Basic knowledge on Honeypot - Principles, Infrastructure and Logs monitoring. Honeypot is one more layers of depends and gathers information to analysis the attacker end.
The overwhelming threat may be a challenge to
general security system. Fundamentally diverse alert and threat
techniques are been researched in order to reduce deceptive
warnings. Threat Detection Systems generates huge amount of
alerts which becomes challenging to deal with them and prepare
solution. The detection System checks inbound and outbound
network activities and finds an suspicious pattern that indicate
an ongoing steps for attack. Large amount of alert may contain
false alarm therefore need of alert analysis mechanisms to offer
high level information of seriousness of threat, how dangerous
device are and which device admin has to pay more attention. To
solve this query we would make use of time and space based alert
analysis technique that provides a solution in form of attack
graph and its evaluation that provides severity of attack to
administrator.
Client Honeypot Based Drive by Download Exploit Detection and their Categoriz...IJERA Editor
Client side attacks are those which exploits the vulnerabilities in client side applications such as browsers, plug-ins etc. The remote attackers execute the malicious code in end user’s system without his knowledge. Here in this research, we propose to detect and measure the drive by download class of malware which infect the end user’s system through HTTP based propagation mechanism. The purpose of this research is to introduce a class of technology known as client honeypot through which we execute the domains in a virtual machine in more optimized manner. Those virtual machines are the controlled environment for the execution of those URLs. During the execution of the websites, the PE files dropped into the system are logged and further analyzed for categorization of malware. Further the critical analysis has been performed by applying some reverse engineering techniques to categories the class of malware and source of infections performed by the malware.
Everything you really need to know about IDS (Intrusion Detection Systems) Combining with HoneyPots. Deployment and usage techniques used in the past and today. How to setup and deploy onto any network including the cloud. Reasons why this should be used in all networks. How to bring BIG DATA down to Small Data that is easy to understand and monitor.
It’s all over the news that data breaches occur daily! I asked WHY these hackers can download terabytes of data in timespans of months without being noticed. What are these companies paying their SOC team millions of dollars for? How come all the money is going to devices to prevent breaches and little to none in detecting when they occur? Don’t people know there are only two types of companies “those that been hacked, and those that don’t know they been hacked”. What can I do to detect a breach within seconds on any network scale? I think I figured it out. In my talk you’ll learn how you and your clients can benefit by applying my exclusive techniques, which I’ve successfully deployed. So the next time you get hacked the hacker would not be able to steal all those credit cards and photos of that Halloween party.
TAXONOMY BASED INTRUSION ATTACKS AND DETECTION MANAGEMENT SCHEME IN PEER-TOPE...IJNSA Journal
A intrusion provides an unauthorized access, damage or disruption of the network. The process can understand the characteristics and nature of an intruder. The paper presents the taxonomy
consists of the specification of an intruder. Taxonomy provides the classification of intruder and provides mechanism for intruder detection. We found the algorithm for developing an intruder which can be attack at host system or network system. Here provide the mechanism for an intrusion by using the
system attribute and detection mechanism is based on knowledge and behavior of the system. Intrusiondetection mechanism using pattern based and threshold based mechanism for detecting an intruder. An intruder continuously monitored the network and host activities for detecting attack into the network and the task of intrusion-detection is also monitor the usage of such systems and detects the apparition of
insecure states.
Autonomic Anomaly Detection System in Computer Networksijsrd.com
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.
Malware Hunter: Building an Intrusion Detection System (IDS) to Neutralize Bo...Editor IJCATR
Among the various forms of malware attacks such as Denial of service, Sniffer, Buffer overflows are the most dreaded threats to computer networks. These attacks are known as botnet attacks and self-propagating in nature and act as an agent or user interface to control the computers which they attack. In the process of controlling a malware, Bot header(s) use a program to control remote systems through internet with the help of zombie systems. Botnets are collection of compromised computers (Bots) which are remotely controlled by its originator (Bot-Master) under a common Command-and-Control (C&C) structure. A server commands to the bot and botnet and receives the reports from the bot. The bots use Trojan horses and subsequently communicate with a central server using IRC. Botnet employs different techniques like Honeypot, communication protocols (e.g. HTTP and DNS) to intrude in new systems in different stages of their lifecycle. Therefore, identifying the botnets has become very challenging; because the botnets are upgrading their methods periodically for affecting the networks. Here, the focus on addressing the botnet detection problem in an Enterprise Network
This research introduces novel Solution to mitigate the malicious activities of Botnet attacks through the Principle of component analysis of each traffic data, measurement and countermeasure selection mechanism called Malware Hunter. This system is built on attack graph-based analytical models based on classification process and reconfigurable through update solutions to virtual network-based countermeasures.
Detecting Unknown Attacks Using Big Data AnalysisEditor IJMTER
Nowadays threat of previously unknown cyber-attacks are increasing because existing security
systems are not able to detect them. Previously, leaking personal information by attacking the PC or
destroying the system was very common cyber attacks . But the goal of recent hacking attacks has changed
from leaking information and destruction of services to attacking large-scale systems such as critical
infrastructures and state agencies. In the other words, existing defence technologies to counter these attacks
are based on pattern matching methods which are very limited. Because of this fact, in the event of new and
previously unknown attacks, detection rate becomes very low and false negative increases. To defend
against these unknown attacks, which cannot be detected with existing technology, a new model based on
big data analysis techniques that can extract information from a variety of sources to detect future attacks is
proposed. The expectation with this model is future Advanced Persistent Threat (APT) detection and
prevention.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Basic knowledge on Honeypot - Principles, Infrastructure and Logs monitoring. Honeypot is one more layers of depends and gathers information to analysis the attacker end.
Honeypot is an exciting new technology with enormous potential for the security community.It is resource which is intended to be attacked and compromised to gain more information about the attacker and his attack techniques.
The paper covers honeypot (and honeynet) basics and definitions and then outlines important implementation and setup guidelines. It also describes some of the security lessons a company can derive from running a honeypot, based on the author experience running a research honeypot. The article also provides insights on techniques of the attackers and concludes with considerations useful for answering the question “Should your organization deploy a honeynet?”
Malicious activities (malcodes) are self replicating
malware and a major security threat in a network environment.
Timely detection and system alert flags are very essential to
prevent rapid malcodes spreading in the network. The difficulty
in detecting malcodes is that they evolve over time. Despite the fact
that signature-based tools, are generally used to secure systems,
signature-based malcode detectors neglect to recognize muddled
and beforehand concealed malcode executables. Automatic signature
generation systems has likewise been use to address the issue
of malcodes, yet there are many works required for good detection.
Base on the behavior way of malcodes, a behavior approach is
required for such detection. Specifically, we require a dynamic
investigation and behavior Rule Base system that distinguishes
malcodes without erroneously block legitimate traffic or increase
false alarms. This paper proposed and discussed the approach
using Machine learning and Indicators of Compromise (IOC) to
analyze intrusion in a network, to identify the cause of the attack
and to provide future detection. This paper proposed the use of
behaviour malware analysis framework to analyze intrusion data,
apply clustering algorithm on the analyzed data and generate IOC
from the clustered data for IOCRule, which will be implemented
into Snort Intrusion Detection System (IDS) for malicious code
detection.
Day by day the internet is becoming an essential part of everyone’s life. In India from 2015 – 2020, there is an increase in internet users by 400 million users. As technology and innovation are increasing rapidly. Security is a key point to keep things in order. Security and privacy are the biggest concern in the world let it is in any field or domain. There is no big difference in cyber security the security is the biggest concern worrying about attacks which could happen anytime. So, in this paper, we are going to talk about honeypot comprehensively. The aim is to track hacker to analyze and understand hacker attacker behavior to create a secure system which is sustainable and efficient. Anoop V Kanavi | Feon Jaison "Honeypot Methods and Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38045.pdf Paper URL : https://www.ijtsrd.com/computer-science/computer-security/38045/honeypot-methods-and-applications/anoop-v-kanavi
This is a Seminar Report on a computer security mechanism named Honeypot. In this I've included Honeypot Basics, Types, Value, Implementation, Merits & Demerits, Legal issues and Future of Honeypots.
Insight Brief: Security Analytics to Identify the 12 Indicators of Compromise21CT Inc.
In this security insight brief, 21CT researchers look at the malicious network behaviors that concern organizations the most, and how to use security analytics to find them before damage is done. Understanding these 12 indicators of compromise are critical to identifying a network breach.
Traditional prevention and detection methods are being bypassed, and many organizations either don’t know what to do, or they don’t have the right resources in place to advance their security.
To keep up with highly skilled and aggressive attackers, we have to move beyond the predictable patterns of network security and static defenses that our cyber adversaries are well-attuned to.
View to learn:
--The value of defensive deception to enterprise cyber-security efforts
--Deception technologies that can help you lure and divert attackers
--Techniques and tactics for detecting and disrupting an attacker’s lateral movement
--The typical inflection point for deterring the majority of attacks
The project entitled with “Network Security System” is related to hacking attacks in computer systems over internet. In today’s world many of the computer systems and servers are not secure because of increasing the hacking attacks or hackers with growing information, so information security specialist’s requirement has gone high.
Looking to understand how hackers and other attackers use cyber technology to attack your network and your executives? This slide set provides an overview and details the anatomy of a cyber attack, and the strategies you can use to manage and mitigate risk.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
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Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
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Ananth3
1. Practical Applications of Honeypots Towards Network Protection and Monitoring
Practical Applications of Honeypots Towards Network
Protection and Monitoring
Miguel José Hernández y López
School of Business
(Unidad Académica Multidisciplinaria de Comercio y Administración – Victoria)
Universidad Autónoma de Tamaulipas
Ciudad Victoria, México
miguel@honeynet.org.mx
Carlos Francisco Lerma Reséndez, MSc
General Directorate of Technological Innovation
(Dirección General de Innovación Tecnológica)
Universidad Autónoma de Tamaulipas
Ciudad Victoria, México
cflerma@uat.edu.mx
Introduction
Any organization which considers Information Technology as the backbone of its
operational structure must be on the cutting edge of its changing processes due to
the fact that having access, on time, to reliable information represents a
fundamental advantage. The identification of the many risks to which corporate
information is exposed to is of vital importance to assure its integrity, usability and
usefulness.
Traditionally, the nature of the field of Information Security has been purely
defensive. Firewalls, intrusion detection systems and encryption are mechanisms
used defensively to protect information resources. The strategic dogmas of
Information Security consist in defending the information infrastructure as well as
possible, detect possible failures in the defensive structure and promptly react to
those failures, preferently in a proactive way. The nature of the existence and
operation of the “information enemy” is purely offensive, due to always being ready
to attack.
Honeypots have demonstrated their value as a research tool in the field of
Information Security. Many researchers and organizations, public and private, which
are part of the Information Security Community, are currently using trap-style
networks to learn the tactics, techniques and procedures used by the hacker
community to break into information vaults without authorization, which cold contain
potentially sensitive information. This paper analyzes the functions of honeypots
and their technology, which are becoming the key component in a layered system
of protection against intruders.
2. Practical Applications of Honeypots Towards Network Protection and Monitoring
Honeypots – What They Are and How They Work
Honeypots are a new technolgy with enormous potential for the Information
Technology community. The first concepts regarding them were introduced by
various icons in Information Security, such as those defined by Cliff Stoll in the
book “The Cuckoo’s Egg” and the works of Bill Cheswick, documented in the book
“An Evening With Berferd”. Since then, those concepts have been in continuous
evolution, developing in an accelerated way and becoming a powerful security tool
nowadays.
Honeypots are, in their most basic form, fake information severs strategically-
positioned in a test network, which are fed with false information disguised as files
of classified nature. In turn, these servers are initially configured in a way that is
difficult, but not impossible, to break into them by an attacker; exposing them
deliberately and making them highly attractive for a hacker in search of a target.
Finally, the server is loaded with monitoring and tracking tools so every step and
trace of activity left by a hacker can be recorded in a log, indicating those traces of
activity in a detailed way.
The main functions of a Honeypot are:
- To divert the attention of the attacker from the real network, in a way that the
main information resources are not compromised
- To capture new viruses or worms for future study
- To build attacker profiles in order to identify their preferred attack methods,
similar to criminal profiles used by law enforcement agencies in order to
identify a criminal’s modus operandi
- To identify new vulnerabilities and risks of various operating systems,
environments and programs which are not thoroughly identified at the
moment
In a more advanced context, a group of Honeypots becomes a Honeynet, thus
providing a tool that spans a wide group of possible threats which gives a systems
administrator more information for study. Moreover, it makes the attack more
fascinating for the attacker due to the fact that Honeypots can increase the
possibilities, targets and methods of attack.
Classification of Honeypots
Honeypots can be classified according to two criteria: According to their
Implementation Environment and According to their Level of Interaction. These
classification criteria eases understanding their operation and uses when it comes
3. Practical Applications of Honeypots Towards Network Protection and Monitoring
to planning an implementation of one of them inside a network or IT infrastructure.
Honeypots According to their Implementation Environment
Under this category, we can define two types of Honeypots: Production Honeypots
and Research Honeypots.
Production Honeypots: Those used to protect organizations in real production
operating environments. They are implemented in a collaterally to data networks or
IT Infrastructures and are subject to constant attacks 24/7. They’re constantly
gathering more importance due to the detection tools they provide and because of
the way they can complement network and host protection.
Research Honeypots: These Honeypots are not implemented with the objective of
protecting networks. They represent educational resources of demonstrative and
research nature whose objective is centered towards studying all sorts of attack
patterns and threats. A great deal of current attention is focused on Research
Honeypots, which are used to gather information about the intruders’ actions. The
Honeynet Project, for example, is a non-profit research organization focused in
voluntary security using Honeypots to gather information about threats in
cyberspace.
Honeypots According to their Level of Interaction
Within this classification criteria, the term “Level of Interaction” defines the range of
attack possibilities that a Honeypot allows an attacker to have. These categories
help us understand not just the type of Honeypot which a person works with, but
also help define the array of options in relation to the vulnerabilities intended for the
attacker to exploit. Those are the most important traits when it comes to starting the
construction of an attacker’s profile.
Low Interaction Honeypots: Normally, Low Interaction Honeypots work exclusively
emulating operating systems and services. The attacker’s activities is limited to the
Honeypot’s level and quality of emulation. The advantage of a Low Level Honeypot
lies upon its simplicity, due to the fact that they tend to be easy to use and maintain,
with minimum risks. For example: An emulated FTP service, listening on port 21, is
probably emulating an FTP login or will possibly support additional FTP commands
but it does not represent a target of critical importance due to the fact that it is not
possibly linked to a FTP server containing sensitive information.
Generally, the implementation process of a Low Interaction Honeypot consists of
installing any kind of operating system emulation software (i.e. VMWare
Workstation or Server), choosing the operating system and services to be
emulated, establish a monitoring strategy and let de software operate by itself in a
normal manner. This “plug-and-play” type of process makes it extremely easy to
4. Practical Applications of Honeypots Towards Network Protection and Monitoring
use a Low Interaction Honeypot. The emulated services mitigate the risk of
penetration, containing the intruder’s activities so he/she never gains access to a
real operating system that could be used to attack or damage other systems.
The main advantage of Low Interaction Honeypots lies in the fact that they only
record limited information because they are designed to capture predetermined
activity. Due to the fact that emulated services can only go as far as certain
operational thresholds, this feature limits the array of options that can be advertised
towards a potential intruder. Likewise, it is relatively simple for an attacker to detect
a Low Interaction Honeypot due to the fact that a skilled intruder can detect how
good the emulation capabilities are as long as he/she has enough time do verify
this.
Perfect examples of Low Interaction Honeypots are: Specter, Honeyd and
KFSensor.
High Interaction Honeypots: These Honeypots constitute a complex solution
because they involve the utilization of operating systems and real applications
implemented in real hardware, without using emulation software, running in a
normal way; many times directly related to services such as databases and shared
folders. I.e. If a Honeypot needs to be implemented on a real Linux system running
a FTP server, a real Linux system needs to be built on a real computer and a real
FTP server will need to be configured.
The aforementioned solution offers two advantages: Initially, there is the possibility
of capturing large amounts of information about the modus operandi of attackers
because intruders are interacting with a real system. This way, a systems
administrator is in a position to study the full extent of the attacker’s activities:
anything ranging from new rootkits, zero-days up to international IRC sessions.
Finally, High Interaction Honeypots do not assume anything about the possible
behavior the attacker will display, providing an open environment which captures
every one of the attacker’s moves and offers a wide scope of services, applications
and information vaults posing as potential targets related to those services which
we specifically want to compromise. This allows high interaction solutions to come
in contact with unexpected behaviors.
However, the latter capability also increases the risk of attackers using those
operating systems as a launch pad for attacks directed at internal systems which
are not part of a Honeypot, turning a bait into a weapon. As a result of this fact,
there is a need to implement additional technologies which prevent the attacker
from damaging non-Honeypot systems that deprives the compromised system of its
capabilities of becoming a platform to launch potential attacks.
Currently, the best example of a High Interaction Honeypot is represented by
Honeynets.
5. Practical Applications of Honeypots Towards Network Protection and Monitoring
Advantages and Disadvantages
Honeypots are an incredibly simple concept that offer very powerful advantages:
- New Tools and Tactics: They are designed to capture anything that interacts
with them, including tools or tactics never seen before, better known as “zero-
days”.
- Minimal Resources: This means that resources can be minimum and still
enough to operate a powerful platform to operate at full scale. I.e. A
computer running with a Pentium Processor with 128 Mb of RAM can easily
handle an entire B-class network.
- IPv6 Encryption: Unlike most security technologies, Honeypots also work in
IPv6 environments. The Honeypot will detect an IPv6-based attack the same
way it does with an IPv4 attack.
- Information: Honeypots can gather detailed information, unlike other security
incident analysis tools.
- Simplicity: Because of their architecture, Honeypots are conceptually simple.
There is not a reason why new algorithms, tables or signatures must be
developed or maintained.
Just like any other technology, Honeypots also have significant weaknesses
inherent to their design and functioning. This is because Honeypots do not replace
current technologies, but instead work along with other existing technologies:
- Limited Vision: They can only scan and capture activity destined to interact
directly with them. They do not capture information related to attacks
destined towards neighboring systems, unless the attacker or the threat
interacts with the Honeypot at the same time.
- Risk: Inherently, the use of any security technology implies a potential risk.
Honeypots are no different because they are also subject to risks, specifically
being hijacked and controlled by the intruder and used as a launch pad for
subsequent attacks.
Practical Applications
When used with productive purposes, Honeypots provide protection to an
organization through prevention, detection and response to an attack. When used
with research purposes, they gather information related to the context in which the
Honeypot was implemented. Some organizations study the tendencies displayed by
intrusive actions, while others shift their interest towards prediction and anticipated
6. Practical Applications of Honeypots Towards Network Protection and Monitoring
prevention.
Honeypots can help prevent attacks in various forms:
- Defense against automated attacks: These attacks are based on tools which
randomly scan entire networks, searching for vulnerable systems. If a
vulnerable system is located, these automated tools will attack and take over
the sytem (with worms which replicate inside the victim). One of the methods
to protect a system from the aforementioned attacks is to reduce the speed of
their scanning activities in order to stop them later on. Known as “Sticky
Honeypots”, these solutions monitor unutilized IP space. When systems are
analyzed, Sticky Honeypots interact with those systems and reduce the
speed of the attack. This is attained by using a variety of TCP tricks, such as
setting the Window Size to zero or constantly putting the attacker on hold.
This technique is excellent to reduce the speed or prevent the dissemination
of worms which have penetrated the internal network.
- Protection against human intruders: This concept is known as conning or
dissuasion. The idea behind this countermeasure is to confuse the attacker
and make him waste time and resources while he is interacting with the
Honeypot. As the process takes place, the attacker’s activity can be detected
and there is enough time to react and stop the attack.
- Surgical Detection Methods: Traditionally, detection has been an extremely
difficult task to carry out. Technologies like Intruder Detection Systems and
Logging Systems have been deficient for many reasons: They generate
excessive amounts of information, inflated percentages of false positives and
do not possess the ability of detecting new attacks, work in encrypted mode
nor in IPv6 environments. Honeypots excel in the field of intrusion detection
by solving many of the problems of classic detection. They reduce false
positives, capture small amounts of data of crucial importance like unknown
attacks and new methods to exploit vulnerabilities (zero-days), as well as
operating in IPv6 environments.
- Cyber-Forensics: Once a network administrator founds out that one of his/her
servers was illegally compromised, it is necessary to proceed immediately to
conduct a forensic analysis in the compromised system in order to produce
an assessment of the damages caused by the attacker. However, there are
two problems affecting incident response:
o Frequently, compromised systems cannot be disconnected from the
networkto be analyzed and;
o The amount of generated information is considerably large, in such a
way that it is very difficult to determine what the attacker really did
inside the system.
Honeypots help solve both problems due to being excellent incident analysis
7. Practical Applications of Honeypots Towards Network Protection and Monitoring
tools, which can be quickly and easily taken offline to conduct a thorough
forensic analysis without impacting daily enterprise operations. The only
activity traces stored by a Honeypot are those related to the attacker,
because they are not generated by any other user but the attacker. The
importance of Honeypots in this setting is the quick delivery of previously-
analyzed information in order to respond quickly and efficiently to an incident.
References
[1] Honeynet Project, http://www.honeynet.org
[2] Honeynet Project México, http://www.honeynet.org.mx
[3] Honeynet Project, Know Your Enemy: Learning about Security Threats (2nd
Edition), 2005
[4] Spitzner Lance, Honeypots: Tracking Hackers, 2002
[5] Dunsmore B., Brown J. and Cross M., Mission Critical!: Internet Security, 2002
[6] Man Young Rhee, Internet Security.
[7] Honeynet Project, Know your Enemy: Honeynets,
http://www.honeynet.org/papers/honeynet/index.html
[8] Philippine Honeynet Project, Honeynets Learning, 2006
http://www.philippinehoneynet.org/docs/honeynetlearning.pdf
[9] Roberti, Raquel y Bonsembiante, Fernando. Llaneros solitarios. Hackers, la
guerrilla informática.
[10] Hassan Artaila, Haidar Safab, Malek Sraja, Iyad Kuwatlya, Zaid Al-Masria, A
hybrid honeypot framework for improving intrusion detection systems in protecting
organizational networks, 2006
[11] Fabien Pouget and Thorsten Holz, A Pointillist Approach for Comparing
Honeypots
[12] HoneyD, http://www.honeyd.org
[13] The Honeynet Project. Know Your Enemy: GenII Honeynets, 2003.
http://www.honeynet.org/papers/gen2/
[14] Corrado Leita1,Marc Dacier1 and Frederic Massicotte, Automatic Handling of
Protocol Dependencies and Reaction to 0-Day Attacks with ScriptGen Based
8. Practical Applications of Honeypots Towards Network Protection and Monitoring
Honeypots
[15] Provos, N.: A virtual honeypot framework. In: Proceedings of the 12th USENIX
Security Symposium, 2004
[16] Stephan Riebach, Erwin P. Rathgeb, and Birger Toedtmann, Efficient
Deployment of Honeynets for Statistical and Forensic Analysis of Attacks from the
Internet
Authors’ Biographies
Miguel José Hernández y López <miguel@honeynet.org.mx>
is an 8th semester student of Management Information
Systems at the School of Business (Unidad Académica
Multidisciplinaria de Comercio y Administración – Victoria) at
the Universidad Autónoma de Tamaulipas, in Ciudad Victoria,
Mexico. Founding member of the Mexican Honeynet Project,
he has been a keynote speaker in several information security
and open source software events in Mexico and abroad,
including a notable participation in the 6th
Convention on Open
Source Software of the Universidad de Mendoza in Mendoza,
Argentina.
Carlos Francisco Lerma Reséndez, MSc
<cflerma@uat.edu.mx> is a Service Engineer heading the area
of IT Monitoring at the Directorate of Information Technology
and Telecommunications (Dirección de Informática y
Telecomunicaciones), a service branch of the General
Directorate of Technological Innovation (Dirección General de
Innovación Tecnológica) at Universidad Autónoma de
Tamaulipas in Ciudad Victoria, Mexico. He graduated with a
Bachelor’s Degree in Public Accounting from the School of
Business (Unidad Académica Multidisciplinaria de Comercio y Administración –
Victoria) at Universidad Autónoma de Tamaulipas and holds a Master of Science in
Telecommunications and Network Management from Syracuse University in
Syracuse, New York.