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.
This is final presentation of IT security project. In this project tested terminal server security and built the system. Project consist of :
*Build the system
*Try to break
*Detect
*Prevent
So, project is implemented fully and all requirement are done.
Learn ethical hacking at your own Platform with live classes , Ppt and various types of pdf. we also provided Udemy premium courses and hacking tools tooo. Kindly visit
https://www.gflixacademy.com
An IDS (Intrusion detection system) is a device or software application that monitors network or system
activities for malicious activities or policy violations and produces reports to a management station. IDS
come in a variety of “flavors” and approach the goal of detecting suspicious traffic in different ways.
There are network based (NIDS) and host based (HIDS) intrusion detection systems. Some systems may
attempt to stop an intrusion attempt but this is neither required nor expected of a monitoring system.
IPS (Intrusion Prevention System) is definitely the next level of security technology with its capability to
provide security at all system levels from the operating system kernel to network data packets. It
provides policies and rules for network traffic along with an IDS for alerting system or network
administrators to suspicious traffic, but allows the administrator to provide the action upon being
alerted. Where IDS informs of a potential attack, an IPS makes attempts to stop it. Another huge leap
over IDS, is that IPS has the capability of being able to prevent known intrusion signatures, but also
some unknown attacks due to its database of generic attack behaviours. Thought of as a combination of
IDS and an application layer firewall for protection, IPS is generally considered to be the "next
generation" of IDS.
A Honey Pot is an intrusion (unwanted) detection technique used to study hacker movement and interested to help better system defences against later attacks usually made up of a virtual machine that sits on a network or single client.
ids&ips technique is used to capture logs,sessions,port no,trojans,and malicious activity on the networkand servers.here u can get detailed about ids and ips techniques
Comment détecter des virus inconnus en utilisant des « honey pots » et d’autr...Hackfest Communication
La détection des nouvelles variantes doit se faire extrêmement rapidement car ils apparaissent maintenant au rythme de 1 toutes les 1,5 secondes. Nous ne pouvons pas nous fier juste à la soumission des fichiers suspects par nos clients ou nos partenaires. Nous avons donc du développer un vaste réseau de sondes (honey pots) et développer des nouvelles façons de trouver le malware. Nous allons discuter des différentes techniques et de leur efficacité dans le monde réel.
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.
This is final presentation of IT security project. In this project tested terminal server security and built the system. Project consist of :
*Build the system
*Try to break
*Detect
*Prevent
So, project is implemented fully and all requirement are done.
Learn ethical hacking at your own Platform with live classes , Ppt and various types of pdf. we also provided Udemy premium courses and hacking tools tooo. Kindly visit
https://www.gflixacademy.com
An IDS (Intrusion detection system) is a device or software application that monitors network or system
activities for malicious activities or policy violations and produces reports to a management station. IDS
come in a variety of “flavors” and approach the goal of detecting suspicious traffic in different ways.
There are network based (NIDS) and host based (HIDS) intrusion detection systems. Some systems may
attempt to stop an intrusion attempt but this is neither required nor expected of a monitoring system.
IPS (Intrusion Prevention System) is definitely the next level of security technology with its capability to
provide security at all system levels from the operating system kernel to network data packets. It
provides policies and rules for network traffic along with an IDS for alerting system or network
administrators to suspicious traffic, but allows the administrator to provide the action upon being
alerted. Where IDS informs of a potential attack, an IPS makes attempts to stop it. Another huge leap
over IDS, is that IPS has the capability of being able to prevent known intrusion signatures, but also
some unknown attacks due to its database of generic attack behaviours. Thought of as a combination of
IDS and an application layer firewall for protection, IPS is generally considered to be the "next
generation" of IDS.
A Honey Pot is an intrusion (unwanted) detection technique used to study hacker movement and interested to help better system defences against later attacks usually made up of a virtual machine that sits on a network or single client.
ids&ips technique is used to capture logs,sessions,port no,trojans,and malicious activity on the networkand servers.here u can get detailed about ids and ips techniques
Comment détecter des virus inconnus en utilisant des « honey pots » et d’autr...Hackfest Communication
La détection des nouvelles variantes doit se faire extrêmement rapidement car ils apparaissent maintenant au rythme de 1 toutes les 1,5 secondes. Nous ne pouvons pas nous fier juste à la soumission des fichiers suspects par nos clients ou nos partenaires. Nous avons donc du développer un vaste réseau de sondes (honey pots) et développer des nouvelles façons de trouver le malware. Nous allons discuter des différentes techniques et de leur efficacité dans le monde réel.
Basic knowledge on Honeypot - Principles, Infrastructure and Logs monitoring. Honeypot is one more layers of depends and gathers information to analysis the attacker end.
In this research work an Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) will be implemented to detect and prevent critical networks infrastructure from cyber-attacks. To strengthen network security and improve the network's active defense intrusion detection capabilities, this project will consist of intrusion detection system using honey token based encrypted pointers and intrusion prevention system which based on the mixed interactive honeypot. The Intrusion Detection System (IDS) is based on the novel approach of Honey Token based Encrypted Pointers. This honey token inside the frame will serve as a trap for the attacker. All nodes operating within the working domain of critical infrastructure network are divided into four different pools. This division is based per their computational power and level of vulnerability. These pools are provided with different levels of security measures within the network. IDS use different number of Honey Tokens (HT) per frame for every different pool e.g. Pool-A contains 4 HT/frame, Pool-B contains 3 HT/frame, Pool-C contains 2 HT/frame and Pool-D contain 1 HT/frame. Moreover, every pool uses different types of encryption schemes (AES-128,192,256). Our critical infrastructure network of 64 nodes is under the umbrella of unified security provided by this single Network Intrusion Detection System (NIDS). After the design phase of IDS, we analyze the performance of IDS in terms of True Positives (TP) and False Negatives (FN). Finally, we test these IDS through Network Penetration Testing (NPT) phase. The detection rate depends on the number of honey tokens per frame. Our proposed IDS are a scalable solution and it can be implemented for any number of nodes in critical infrastructure network. However, in case of Intrusion Prevention System (IPS) we use Virtual honeypot technology which is the best active prevention technology among all honeypot technologies. By using the original operating system and virtual technology, the honeypot lures attackers in a pre-arranged manner, analyzes and audits various attacking behavior, tracks the attack source, obtains evidence, and finds effective solutions.
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.
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?”
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
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.
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.
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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/
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
Connector Corner: Automate dynamic content and events by pushing a button
Honeypots
1. Honeypots-the new era security tools
Gayatri Vidya Parishad College of Engineering
Ananth Kumar.G
(ananth_h16@yahoo.com)
Swetha.B
(swetha_ishita@yahoo.co.in)
Abstract:
The need to observe, analyze and understand
malicious behaviors is a well known requirement in
risk management, the protection of an environment
can't be done without the understanding of the risks
that weaken it. Security is to be provided to the
network or system to protect it form unauthorized
access to sensitive data. Security has several
different aspects: access, data, protocols,
information and transactions. All security systems
address two or more of these categories. Honeypots
are an exciting new technology with enormous
potential for the security community.
Honeypot can be defined as, an information
system resource whose value lies in unauthorized
or illicit use of that resource. A honeypot is a trap
set to detect, deflect or in some manner counteract
attempts at unauthorized use of information
systems. These are closely monitored network
decoys serving several purposes: they can distract
adversaries from valuable machines on a network,
they can provide early warning about new attack
and exploitation trends and they allow in-depth
examination of adversaries during and after
exploitation of a honeypot. A honeypot system is
designed to allure attackers. In general it consists of
a computer, data or a network site that appears to be
part of a network site that appears to be part of a
network but which is actually isolated and
protected, and which seems to contain information
or a resource that would be of value to attackers.
Honeypots can carry risks to network, and must be
handled carefully. If they are not properly walled
off, an attacker can use them to break into a system.
These are highly flexible security tools with
different applications for security. They have
multiple uses like prevention, detection and
information gathering. This paper analyzes the
importance of honeypots in providing security to a
network.
Introduction:
Honeypots all share the same concept: a security
resource that should not have any production or
authorized activity. In other words, deployment of
honeypots in a network should not affect critical
network services and applications. A honeypot is a
security resource whose value lies in being probed,
attacked, or compromised. . Theoretically, a
honeypot should see no traffic because it has no
legitimate activity. This means any interaction with
a honeypot is most likely unauthorized or malicious
activity. Any connection attempts to a honeypot are
most likely a probe, attack, or compromise. While
this concept sounds very simple, it is this very
simplicity that give honeypots their tremendous
advantages.
Types of Honeypots: Based on the level of
involvement, honeypots may be classified as:
1. Low-interaction Honeypots
2. High-interaction Honeypots
1
2. Honeypots come in many shapes and sizes, making
them difficult to get a grasp of. To help us better
understand honeypots and all the different types, we
break them down into two general categories, low-
interaction and high-interaction honeypots. These
categories help us understand what type of
honeypot you are dealing with, its strengths, and
weaknesses. Interaction defines the level of activity
a honeypot allows an attacker.
Low-interaction Honeypots
Low-interaction honeypots have limited interaction;
they normally work by emulating services and
operating systems. Attacker activity is limited to the
level of emulation by the honeypot. For example, an
emulated FTP service listening on port 21 may just
emulate a FTP login, or it may support a variety of
additional FTP commands. The advantages of a
low-interaction honeypot are their simplicity. These
honeypots tend to be easier to deploy and maintain,
with minimal risk.
Usually they involve installing software, selecting
the operating systems and services you want to
emulate and monitor, and letting the honeypot go
from there. This plug and play approach makes
deploying them very easy for most organizations.
Also, the emulated services mitigate risk by
containing the attacker's activity, the attacker never
has access to an operating system to attack or harm
others. The main disadvantages with low interaction
honeypots is that they log only limited information
and are designed to capture known activity. The
emulated services can only do so much. Also, its
easier for an attacker to detect a low-interaction
honeypot, no matter how good the emulation is,
skilled attacker can eventually detect their presence.
Examples of low-interaction honeypots include
Specter, Honeyd, and KFSensor.
Honeyd, a Low-interaction honeypot
Honeyd is a low-interaction honeypot. Developed
by Niels Provos, Honeyd is Open Source and
designed to run primarily on UNIX systems (though
it has been ported to Windows). Honeyd works on
the concept of monitoring unused IP space.
Anytime it sees a connection attempt to an unused
IP, it intercepts the connection and then interacts
with the attacker, pretending to be the victim. By
default, Honeyd detects and logs any connection to
any UDP or TCP port. In addition, you can
configure emulated services to monitor specific
ports, such as an emulated FTP server monitoring
TCP port 21. When an attacker connects to the
emulated service, not only does the honeypot detect
and log the activity, but it captures all of the
attacker's interaction with the emulated service. In
the case of the emulated FTP server, we can
potentially capture the attacker's login and
password, the commands they issue, and perhaps
even learn what they are looking for or their
identity. It all depends on the level of emulation by
the honeypot. Most emulated services work the
same way. They expect a specific type of behavior,
and then are programmed to react in a
predetermined way. If attack A does this, then react
this way. If attack B does this, then respond this
way. The limitation is if the attacker does
something that the emulation does not expect, then
it does not know how to respond. Most low-
interaction honeypots, including Honeyd, simply
generate an error message. You can see what
commands the emulated FTP server for Honeyd
supports by review the source code.
Some honeypots, such as Honeyd, can not only
emulate services, but emulate actual operating
systems. In other words, Honeyd can appear to the
attacker to be a Cisco router, WinXP web server, or
Linux DNS server. There are several advantages to
emulating different operating systems. First, the
honeypot can better blend in with existing networks
if the honeypot has the same appearance and
behavior of production systems. Second, you can
target specific attackers by providing systems and
services they often target, or systems and services
you want to learn about. There are two elements to
emulating operating systems. The first is with the
emulated services. When an attacker connects to an
emulated service, you can have that service behave
2
3. like and appear to be a specific OS. For example, if
you have a service emulating a web server, and you
want your honeypot to appear to be a Win2000
server, then you would emulate the behavior of a
IIS web server. For Linux, you would emulate the
behavior of an Apache web server. Most honeypots
emulate OS' in this manner. Some sophisticated
honeypots take this emulation one step farther (as
Honeyd does). Not only do they emulate at the
service level, but at the IP stack level. If someone
uses active fingerprinting measures to determine the
OS type of your honeypot most honeypots respond
with the IP stack of whatever OS the honeypot is
installed on. Honeyd spoof the replies, making not
only the emulated services, but emulated IP stacks
behave as the operating systems would. The level of
emulation and sophistication depends on what
honeypot technology you chose to use.
High-interaction honeypot
High-interaction honeypots are different; they are
usually complex solutions as they involve real
operating systems and applications. Nothing is
emulated, we give attackers the real thing. If you
want a Linux honeypot running an FTP server, you
build a real Linux system running a real FTP server.
The advantages with such a solution are two fold.
First, you can capture extensive amounts of
information. By giving attackers real systems to
interact with, you can learn the full extent of their
behavior, everything from new root kits to
international IRC sessions. The second advantage is
high-interaction honeypots make no assumptions on
how an attacker will behave. Instead, they provide
an open environment that captures all activity. This
allows high-interaction solutions to learn behavior
we would not expect. An excellent example of this
is how a Honeynet captured encoded back door
commands on a non-standard IP protocol. However,
this also increases the risk of the prevention
technologies. This gives the attacker the flexibility
to interact with the honeypot as attackers can use
this real operating system to attack non-honeypot
systems. As result, additional technologies have to
be implement that prevent the attacker from
harming other non-honeypot systems. In general,
high-interaction honeypots can do everything low-
interaction honeypots can do and much more.
However, they can be more complex to deploy and
maintain. Examples of high-interaction honeypots
include Symantec Decoy Server and Honeynets.
Honeynets, a High-interaction honeypot
Honeynets are a prime example of high-interaction
honeypot. Honeynets are not a product, they are not
a software solution that you install on a computer.
Instead, Honeyents are an architecture, an entire
network of computers designed to attacked. The
idea is to have an architecture that creates a highly
controlled network, one where all activity is
controlled and captured. Within this network we
place our intended victims, real computers running
real applications. The bad guys find, attack, and
break into these systems on their own initiative.
When they do, they do not realize they are within a
Honeynet. All of their activity, from encrypted SSH
sessions to emails and files uploads, are captured
without them knowing it. This is done by inserting
kernel modules on the victim systems that capture
all of the attacker's actions. At the same time, the
Honeynet controls the attacker's activity. Honeynets
do this using a Honeywall gateway. This gateway
allows inbound traffic to the victim systems, but
controls the outbound traffic using intrusion victim
systems, but prevents the attacker from harming
other non-Honeynet computers.
Architecture of Honeypots
3
4. Honeypots can be classified based on
their deployment and based on their
level of involvement. Based on the
deployment, honeypots may be
classified as:
1. Production Honeypots
2. Research Honeypots
Production honeypots are easy to use,
capture only limited information, and are
used primarily by companies or
corporations; Production honeypots are
placed inside the production network
with other production servers by
organization to improve their overall
state of security. Normally, production
honeypots are low-interaction honeypots
which are easier to deploy. They give
less information about the attacks or
attackers than research honeypots do.
The purpose of a production honeypot is
to help mitigate risk in an organization.
The honeypot adds value to the security
measures of an organization.
Research honeypots are run by a
volunteer, non-profit research
4
Low-interaction
Solution emulates operating
systems and services.
High-interaction
No emulation, real
operating systems
and services are
provided.
• Easy to install and
deploy. Usually
requires simply
installing and
configuring
software on a
computer.
• Minimal risk, as the
emulated services
control what
attackers can and
cannot do.
• Captures limited
amounts of
information, mainly
transactional data
and some limited
interaction.
• Can capture
far more
information,
including new
tools,
communicatio
ns, or attacker
keystrokes.
• Can be
complex to
install or
deploy
(commercial
versions tend
to be much
simpler).
• Increased
risk, as
attackers are
provided real
operating
systems to
interact with
5. organization or an educational institution
to gather information about the motives
and tactics of the Blackhat community
targeting different networks. These
honeypots do not add direct value to a
specific organization. Instead they are
used to research the threats organizations
face, and to learn how to better protect
against those threats. Think of them as
'counter-intelligence': their job is to gain
information on the attackers. This
information is then used to protect
against those threats. Research
honeypots are complex to deploy and
maintain, capture extensive information,
and are used primarily by research,
military, or government organizations.
Value of Honeypots
Honeypots can help prevent attacks in
several ways. The first is against
automated attacks, such as worms or
auto-rooters. These attacks are based on
tools that randomly scan entire networks
looking for vulnerable systems. If
vulnerable systems are found, these
automated tools will then attack and take
over the system (with worms self-
replicating, copying themselves to the
victim). One way that honeypots can
help defend against such attacks is
slowing their scanning down, potentially
even stopping them. Called sticky
honeypots, these solutions monitor
unused IP space. When probed by such
scanning activity, these honeypots
interact with and slow the attacker down.
They do this using a variety of TCP
tricks, such as a Windows size of zero,
putting the attacker into a holding
pattern. This is excellent for slowing
down or preventing the spread of a
worm that has penetrated your internal
organization. One such example of a
sticky honeypot is LaBrea Tarpit. Sticky
honeypots are most often low-interaction
solutions (you can almost call them 'no-
interaction solutions', as they slow the
attacker down to a crawl :). Honeypots
can also protect your organization from
human attackers. The concept is
deception or deterrence. The idea is to
confuse an attacker, to make him waste
his time and resources interacting with
honeypots. Meanwhile, your
organization has detected the attacker’s
activity and has the time to respond and
stop the attacker. This can be even taken
one step farther. If an attacker knows
your organization is using honeypots,
but does not know which systems are
honeypots and which systems are
legitimate computers, they may be
concerned about being caught by
honeypots and decided not to attack your
organizations. Thus the honeypot deters
the attacker. An example of a honeypot
designed to do this is Deception Toolkit,
a low-interaction honeypot.
The second way honeypots can help
protect an organization is through
detection. Detection is critical, its
purpose is to identify a failure or
breakdown in prevention. Regardless of
how secure an organization is, there will
always be failures, if for no other
reasons then humans are involved in the
process. By detecting an attacker, you
can quickly react to them, stopping or
mitigating the damage they do.
Traditionally, detection has proven
extremely difficult to do. Technologies
such as IDS sensors and systems logs
haven proven ineffective for several
reasons. They generate far too much
data, large percentage of false positives,
inability to detect new attacks, and the
inability to work in encrypted or IPv6
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6. environments. Honeypots excel at
detection, addressing many of these
problems of traditional detection.
Honeypots reduce false positives by
capturing small data sets of high value,
capture unknown attacks such as new
exploits or polymorphic shell code, and
work in encrypted and IPv6
environments. In general, low-
interaction honeypots make the best
solutions for detection. They are easier
to deploy and maintain then high-
interaction honeypots and have reduced
risk.
The third and final way a honeypot can
help protect an organization is in
reponse. Once an organization has
detected a failure, how do they respond?
This can often be one of the greatest
challenges an organization faces. There
is often little information on who the
attacker is, how they got in, or how
much damage they have done. In these
situations detailed information on the
attacker's activity are critical. There are
two problems compounding incidence
response. First, often the very systems
compromised cannot be taken offline to
analyze. Production systems, such as an
organization's mail server, are so critical
that even though its been hacked,
security professionals may not be able to
take the system down and do a proper
forensic analysis. Instead, they are
limited to analyze the live system while
still providing production services. This
cripples the ability to analyze what
happened, how much damage the
attacker has done, and even if the
attacker has broken into other systems.
The other problem is even if the system
is pulled offline, there is so much data
pollution it can be very difficult to
determine what the bad guy did. By data
pollution, I mean there has been so much
activity (user's logging in, mail accounts
read, files written to databases, etc) it
can be difficult to determine what is
normal day-to-day activity, and what is
the attacker. Honeypots can help address
both problems. Honeypots make an
excellent incident response tool, as they
can quickly and easily be taken offline
for a full forensic analysis, without
impacting day-to-day business
operations. Also, the only activity a
honeypot captures is unauthorized or
malicious activity. This makes hacked
honeypots much easier to analyze then
hacked production systems, as any data
you retrieve from a honeypot is most
likely related to the attacker. The value
honeypots provide here is quickly giving
organizations the in-depth information
they need to rapidly and effectively
respond to an incident. In general, high-
interaction honeypots make the best
solution for response. To respond to an
intruder, you need in-depth knowledge
on what they did, how they broke in, and
the tools they used. For that type of data
you most likely need the capabilities of a
high-interaction honeypot.
Honeypots are extremely powerful, not
only can they be used to protect your
organization, but they can be used to
gain extensive information on threats,
information few other technologies are
capable of gathering. One of the greatest
problems security professionals face is a
lack of information or intelligence on
cyber threats. For centuries military
organizations have depended on
information to better understand who
their enemy is and how to defend against
them. Research honeypots address this
by collecting information on threats.
This information can then be used for a
variety of purposes, including trend
analysis, identifying new tools or
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7. methods, identifying attackers and their
communities, early warning and
prediction, or motivations. One of the
most well known examples of using
honeypots for research is the work done
by the Honeynet Project, an all
volunteer, non-profit security research
organization. All of the data they collect
is with Honeynet distributed around the
world. As threats are constantly
changing, this information is proving
more and more critical.
Advantages
Honeypots are a tremendously simply
concept, which gives them some very
powerful strengths.
• Small data sets of high value:
Honeypots collect small amounts
of information. Instead of
logging a one GB of data a day,
they can log only one MB of data
a day. Instead of generating
10,000 alerts a day, they can
generate only 10 alerts a day.
Remember, honeypots only
capture bad activity, any
interaction with a honeypot is
most likely unauthorized or
malicious activity. As such,
honeypots reduce 'noise' by
collecting only small data sets,
but information of high value, as
it is only the bad guys. This
means its much easier (and
cheaper) to analyze the data a
honeypot collects and derive
value from it.
• New tools and tactics:
Honeypots are designed to
capture anything thrown at them,
including tools or tactics never
seen before.
• Minimal resources: Honeypots
require minimal resources, they
only capture bad activity. This
means an old Pentium computer
with 128MB of RAM can easily
handle an entire class B network
sitting off an OC-12 network.
• Encryption or IPv6: Unlike most
security technologies (such as
IDS systems) honeypots work
fine in encrypted or IPv6
environments. It does not matter
what the bad guys throw at a
honeypot, the honeypot will
detect and capture it.
• Information: Honeypots can
collect in-depth information that
few, if any other technologies
can match.
• Simplicity: Finally, honeypots
are conceptually very simple.
There are no fancy algorithms to
develop, state tables to maintain,
or signatures to update. The
simpler a technology, the less
likely there will be mistakes or
misconfigurations.
Disadvantages:
Like any technology, honeypots also
have their weaknesses. It is because of
this they do not replace any current
technology, but work with existing
technologies.
• Limited view: Honeypots can
only track and capture activity
that directly interacts with them.
Honeypots will not capture
attacks against other systems,
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8. unless the attacker or threat
interacts with the honeypots also.
• Risk: All security technologies
have risk. Firewalls have risk of
being penetrated, encryption has
the risk of being broken, IDS
sensors have the risk of failing to
detect attacks. Honeypots are no
different, they have risk also.
Specifically, honeypots have the
risk of being taken over by the
bad guy and being used to harm
other systems. Depending on the
type of honeypot, it can have no
more risk than an IDS sensor,
while some honeypots have a
great deal of risk.
Conclusion
Honeypots can be used for research,
gathering information on threats so we
can better understand and defend against
them. The modern rapid advancements
in computer networking, communication
and mobility increased the need of
reliable ways to verify the loopholes
within the system. Honeypots pave a
significant way towards production
purposes by preventing, detecting, or
responding to attacks.
References
[1] The Honeynet project,
http://www.honeynet.org
[2] Honeypots, by Lance Spitzner,
http://www.spitzner.net/honeypots.html
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