This document provides an overview of the Metasploit framework. It discusses that Metasploit is an open-source penetration testing software that can be used to test vulnerabilities on computer systems. It details the history and components of Metasploit, including its interfaces like the Metasploit Framework edition, Metasploit Community edition, Metasploit Express, and Metasploit Pro. The document also describes the basic steps to exploit a system using Metasploit and some related tools like Armitage and Cobalt Strike.
A Comparison Study of Open Source Penetration Testing Toolsijtsrd
Penetration testing also known as Pen Test is a series of activities which is performed by authorized simulated attack on computer system, network or web application to find vulnerabilities that an attacker could exploit. It helps confirm the efficiency and effectiveness of the various security measures that have been implemented. In the world of Open Source Software, even Penetration Testing is not untouched. The purpose of this pilot study was to compare various the open source penetration testing tools. Nilesh Bhingardeve | Seeza Franklin"A Comparison Study of Open Source Penetration Testing Tools" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15662.pdf http://www.ijtsrd.com/computer-science/computer-security/15662/a-comparison-study-of-open-source-penetration-testing-tools/nilesh-bhingardeve
Malware analysis on android using supervised machine learning techniquesMd. Shohel Rana
In recent years, a widespread research is conducted with the growth of malware resulted in the domain of malware analysis and detection in Android devices. Android, a mobile-based operating system currently having more than one billion active users with a high market impact that have inspired the expansion of malware by cyber criminals. Android implements a different architecture and security controls to solve the problems caused by malware, such as unique user ID (UID) for each application, system permissions, and its distribution platform Google Play. There are numerous ways to violate that fortification, and how the complexity of creating a new solution is enlarged while cybercriminals progress their skills to develop malware. A community including developer and researcher has been evolving substitutes aimed at refining the level of safety where numerous machine learning algorithms already been proposed or applied to classify or cluster malware including analysis techniques, frameworks, sandboxes, and systems security. One of the most promising techniques is the implementation of artificial intelligence solutions for malware analysis. In this paper, we evaluate numerous supervised machine learning algorithms by implementing a static analysis framework to make predictions for detecting malware on Android.
AI approach to malware similarity analysis: Maping the malware genome with a...Priyanka Aash
In recent years, cyber defenders protecting enterprise networks have started incorporating malware code sharing identification tools into their workflows. These tools compare new malware samples to a large databases of known malware samples, in order to identify samples with shared code relationships. When unknown malware binaries are found to share code "fingerprints" with malware from known adversaries, they provides a key clue into which adversary is generating these new binaries, thus helping develop a general mitigation strategy against that family of threats. The efficacy of code sharing identification systems is demonstrated every day, as new family of threats are discovered, and countermeasures are rapidly developed for them. Unfortunately, these systems are hard to maintain, deploy, and adapt to evolving threats. First and foremost, these systems do not learn to adapt to new malware obfuscation strategies, meaning they will continuously fall out of date with adversary tradecraft, requiring, periodically, a manually intensive tuning in order to adjust the formulae used for similarity between malware. In addition, these systems require an up to date, well maintained database of recent threats in order to provide relevant results. Such a database is difficult to deploy, and hard and expensive to maintain for smaller organizations. In order to address these issues we developed a new malware similarity detection approach. This approach, not only significantly reduces the need for manual tuning of the similarity formulate, but also allows for significantly smaller deployment footprint and provides significant increase in accuracy. Our family/similarity detection system is the first to use deep neural networks for code sharing identification, automatically learning to see through adversary tradecraft, thereby staying up to date with adversary evolution. Using traditional string similarity features our approach increased accuracy by 10%, from 65% to 75%. Using an advanced set of features that we specifically designed for malware classification, our approach has 98% accuracy. In this presentation we describe how our method works, why it is able to significantly improve upon current approaches, and how this approach can be easily adapted and tuned to individual/organization needs of the attendees.
(Source: Black Hat USA 2016, Las Vegas)
Open Source Insight: Meltdown, Spectre Security Flaws “Impact Everything”Black Duck by Synopsys
Welcome to 2018, with two major security flaws revealed that makes any computer device that has chips from Intel, AMD and ARM at risk. One security flaw, dubbed Meltdown, impacts Intel semiconductors, enabling enabling bad guys to steal passwords. The other security flaw, Spectre, impacts chips from all three companies. During an interview with CNBC covered by Reuters, Intel’s chief executive noted that “Phones, PCs, everything are going to have some impact, but it’ll vary from product to product.”
In other cybersecurity news, we look at 10 open source technologies you need to know about, cybersecurity predictions for 2018, and an interesting white paper published by the University of Michigan on identifying cybersecurity threats in connected vehicles.
Anti malware solution using Machine LearningAkash Sarode
These slides are about how to build anti-malware solution using machine learning classification model. We will be using a python script to learn our machine learning model and another py script to predict file as legitimate or malware. Entire demo of the same is available on youtube - https://www.youtube.com/watch?v=Kf9VD1os_pY&index=58&list=PL2iM-fIRjbTBFazzQ5uEzeASpmP8o40y1.
A Comparison Study of Open Source Penetration Testing Toolsijtsrd
Penetration testing also known as Pen Test is a series of activities which is performed by authorized simulated attack on computer system, network or web application to find vulnerabilities that an attacker could exploit. It helps confirm the efficiency and effectiveness of the various security measures that have been implemented. In the world of Open Source Software, even Penetration Testing is not untouched. The purpose of this pilot study was to compare various the open source penetration testing tools. Nilesh Bhingardeve | Seeza Franklin"A Comparison Study of Open Source Penetration Testing Tools" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15662.pdf http://www.ijtsrd.com/computer-science/computer-security/15662/a-comparison-study-of-open-source-penetration-testing-tools/nilesh-bhingardeve
Malware analysis on android using supervised machine learning techniquesMd. Shohel Rana
In recent years, a widespread research is conducted with the growth of malware resulted in the domain of malware analysis and detection in Android devices. Android, a mobile-based operating system currently having more than one billion active users with a high market impact that have inspired the expansion of malware by cyber criminals. Android implements a different architecture and security controls to solve the problems caused by malware, such as unique user ID (UID) for each application, system permissions, and its distribution platform Google Play. There are numerous ways to violate that fortification, and how the complexity of creating a new solution is enlarged while cybercriminals progress their skills to develop malware. A community including developer and researcher has been evolving substitutes aimed at refining the level of safety where numerous machine learning algorithms already been proposed or applied to classify or cluster malware including analysis techniques, frameworks, sandboxes, and systems security. One of the most promising techniques is the implementation of artificial intelligence solutions for malware analysis. In this paper, we evaluate numerous supervised machine learning algorithms by implementing a static analysis framework to make predictions for detecting malware on Android.
AI approach to malware similarity analysis: Maping the malware genome with a...Priyanka Aash
In recent years, cyber defenders protecting enterprise networks have started incorporating malware code sharing identification tools into their workflows. These tools compare new malware samples to a large databases of known malware samples, in order to identify samples with shared code relationships. When unknown malware binaries are found to share code "fingerprints" with malware from known adversaries, they provides a key clue into which adversary is generating these new binaries, thus helping develop a general mitigation strategy against that family of threats. The efficacy of code sharing identification systems is demonstrated every day, as new family of threats are discovered, and countermeasures are rapidly developed for them. Unfortunately, these systems are hard to maintain, deploy, and adapt to evolving threats. First and foremost, these systems do not learn to adapt to new malware obfuscation strategies, meaning they will continuously fall out of date with adversary tradecraft, requiring, periodically, a manually intensive tuning in order to adjust the formulae used for similarity between malware. In addition, these systems require an up to date, well maintained database of recent threats in order to provide relevant results. Such a database is difficult to deploy, and hard and expensive to maintain for smaller organizations. In order to address these issues we developed a new malware similarity detection approach. This approach, not only significantly reduces the need for manual tuning of the similarity formulate, but also allows for significantly smaller deployment footprint and provides significant increase in accuracy. Our family/similarity detection system is the first to use deep neural networks for code sharing identification, automatically learning to see through adversary tradecraft, thereby staying up to date with adversary evolution. Using traditional string similarity features our approach increased accuracy by 10%, from 65% to 75%. Using an advanced set of features that we specifically designed for malware classification, our approach has 98% accuracy. In this presentation we describe how our method works, why it is able to significantly improve upon current approaches, and how this approach can be easily adapted and tuned to individual/organization needs of the attendees.
(Source: Black Hat USA 2016, Las Vegas)
Open Source Insight: Meltdown, Spectre Security Flaws “Impact Everything”Black Duck by Synopsys
Welcome to 2018, with two major security flaws revealed that makes any computer device that has chips from Intel, AMD and ARM at risk. One security flaw, dubbed Meltdown, impacts Intel semiconductors, enabling enabling bad guys to steal passwords. The other security flaw, Spectre, impacts chips from all three companies. During an interview with CNBC covered by Reuters, Intel’s chief executive noted that “Phones, PCs, everything are going to have some impact, but it’ll vary from product to product.”
In other cybersecurity news, we look at 10 open source technologies you need to know about, cybersecurity predictions for 2018, and an interesting white paper published by the University of Michigan on identifying cybersecurity threats in connected vehicles.
Anti malware solution using Machine LearningAkash Sarode
These slides are about how to build anti-malware solution using machine learning classification model. We will be using a python script to learn our machine learning model and another py script to predict file as legitimate or malware. Entire demo of the same is available on youtube - https://www.youtube.com/watch?v=Kf9VD1os_pY&index=58&list=PL2iM-fIRjbTBFazzQ5uEzeASpmP8o40y1.
Malware Detection Using Machine Learning TechniquesArshadRaja786
Malware viruses can be easily detected using machine learning Techniques such as K-Mean Algorithms, KNN algorithm, Boosted J48 Decision Tree and other Data Mining Techniques. Among them J48 proved to be more effective in detecting computer virus and upcoming networks worms...
robust malware detection for iot devices using deep eigen space learningVenkat Projects
Internet of Things (IoT) in military settings generally consists of a diverse range of Internet-connected devices and nodes (e.g. medical devices and wearable combat uniforms). These IoT devices and nodes are a valuable target for cyber criminals, particularly state-sponsored or nation state actors. A common attack vector is the use of malware. In this paper, we present a deep learning based method to detect Internet Of Battlefield Things (IoBT) malware via the device’s Operational Code (OpCode) sequence. We transmute OpCodes into a vector space and apply a deep Eigenspace learning approach to classify malicious and benign applications. We also demonstrate the robustness of our proposed approach in malware detection and its sustainability against junk code insertion attacks. Lastly, we make available our malware sample on Github, which hopefully will benefit future research efforts (e.g. to facilitate evaluation of future malware detection approaches).
small talk about IOT security especially IOT pentesting for beginner. What exactly IOT and how we test it?
Live on Ethical Hacker Indonesia
April 14th 2020
Often the overall security of a software application is impacted due to loopholes in the operating systems, networks, system configuration, third-party services, or endpoints. The QA professionals perform penetration testing to identify the loopholes that make the software vulnerable to targeted security attacks by gaining access to the application’s features and data.
Intrusion Detection Systems By Anamoly-Based Using Neural NetworkIOSR Journals
To improve network security different steps has been taken as size and importance of the network has
increases day by day. Then chances of a network attacks increases Network is mainly attacked by some
intrusions that are identified by network intrusion detection system. These intrusions are mainly present in data
packets and each packet has to scan for its detection. This paper works to develop a intrusion detection system
which utilizes the identity and signature of the intrusion for identifying different kinds of intrusions. As network
intrusion detection system need to be efficient enough that chance of false alarm generation should be less,
which means identifying as a intrusion but actually it is not an intrusion. Result obtained after analyzing this
system is quite good enough that nearly 90% of true alarms are generated. It detect intrusion for various
services like Dos, SSH, etc by neural network
Slide yang kupresentasikan di MII-Intel Seminar (Jakarta, 30/10/2019)
IoT is the Future. Or even, IoT is widely adopted now.
Are you sure you are prepared enough for it? Are confident that your IoT solution is secure?
Presentation made of the paper J. McGibney, M. PoncedeLeon, J. Ronan, Security for Heterogeneous Mobile Network Services, eChallenges, Ljubljana, Slovenia, October 2005.
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.
Top 10 Software to Detect & Prevent Security Vulnerabilities from BlackHat US...Mobodexter
BlackHat USA 2015 got recently concluded and we head a bunch of news around how BlackHat brought to light various security vulnerabilities in day-to-day life like ZigBee protocol, Device for stealing keyless cars & ATM card skimmers. However the presenters, who are also ethical hackers, also gave a bunch of tools to help software community to detect & prevent security holes in the hardware & software while the product is ready for release. We have reviewed all the presentations from the conference and give you here a list of Top 10 tools/utilities that helps in security vulnerability detection & prevention.
Malware Detection Using Machine Learning TechniquesArshadRaja786
Malware viruses can be easily detected using machine learning Techniques such as K-Mean Algorithms, KNN algorithm, Boosted J48 Decision Tree and other Data Mining Techniques. Among them J48 proved to be more effective in detecting computer virus and upcoming networks worms...
robust malware detection for iot devices using deep eigen space learningVenkat Projects
Internet of Things (IoT) in military settings generally consists of a diverse range of Internet-connected devices and nodes (e.g. medical devices and wearable combat uniforms). These IoT devices and nodes are a valuable target for cyber criminals, particularly state-sponsored or nation state actors. A common attack vector is the use of malware. In this paper, we present a deep learning based method to detect Internet Of Battlefield Things (IoBT) malware via the device’s Operational Code (OpCode) sequence. We transmute OpCodes into a vector space and apply a deep Eigenspace learning approach to classify malicious and benign applications. We also demonstrate the robustness of our proposed approach in malware detection and its sustainability against junk code insertion attacks. Lastly, we make available our malware sample on Github, which hopefully will benefit future research efforts (e.g. to facilitate evaluation of future malware detection approaches).
small talk about IOT security especially IOT pentesting for beginner. What exactly IOT and how we test it?
Live on Ethical Hacker Indonesia
April 14th 2020
Often the overall security of a software application is impacted due to loopholes in the operating systems, networks, system configuration, third-party services, or endpoints. The QA professionals perform penetration testing to identify the loopholes that make the software vulnerable to targeted security attacks by gaining access to the application’s features and data.
Intrusion Detection Systems By Anamoly-Based Using Neural NetworkIOSR Journals
To improve network security different steps has been taken as size and importance of the network has
increases day by day. Then chances of a network attacks increases Network is mainly attacked by some
intrusions that are identified by network intrusion detection system. These intrusions are mainly present in data
packets and each packet has to scan for its detection. This paper works to develop a intrusion detection system
which utilizes the identity and signature of the intrusion for identifying different kinds of intrusions. As network
intrusion detection system need to be efficient enough that chance of false alarm generation should be less,
which means identifying as a intrusion but actually it is not an intrusion. Result obtained after analyzing this
system is quite good enough that nearly 90% of true alarms are generated. It detect intrusion for various
services like Dos, SSH, etc by neural network
Slide yang kupresentasikan di MII-Intel Seminar (Jakarta, 30/10/2019)
IoT is the Future. Or even, IoT is widely adopted now.
Are you sure you are prepared enough for it? Are confident that your IoT solution is secure?
Presentation made of the paper J. McGibney, M. PoncedeLeon, J. Ronan, Security for Heterogeneous Mobile Network Services, eChallenges, Ljubljana, Slovenia, October 2005.
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.
Top 10 Software to Detect & Prevent Security Vulnerabilities from BlackHat US...Mobodexter
BlackHat USA 2015 got recently concluded and we head a bunch of news around how BlackHat brought to light various security vulnerabilities in day-to-day life like ZigBee protocol, Device for stealing keyless cars & ATM card skimmers. However the presenters, who are also ethical hackers, also gave a bunch of tools to help software community to detect & prevent security holes in the hardware & software while the product is ready for release. We have reviewed all the presentations from the conference and give you here a list of Top 10 tools/utilities that helps in security vulnerability detection & prevention.
Metasploit (Module-1) - Getting Started With MetasploitAnurag Srivastava
Vulnerability and exploitation framework designed to ease the burden on security professionals when it comes to performing security assessments.
One of the single most useful auditing tools freely available to security professionals today
Contains an extensive library of "modules.“
Each module has a function, and they are divided up into "exploits", "auxiliary", "post" (post exploitation), "payloads", "encoders", and "nops.
The purpose of this document
is not to show how to use Metasploit tool there are enormous amount of sources available to do that but to show you how to look deeper into the code and try to decipher how the various classes and modules hang
together to produce the various functions we love to use.
In doing so we will learn how the exploit framework could be structured, how the interaction between the
attacker and the exploited vulnerability could be
achieved and how the user can extend the functionality of Metasploit.
Metasploit framework can also be called as ‘Swiss Army knife ’ of penetration testers as it provides multiple exploit, customization, easy to redevelop according to the requirements of the system . To secure our system and prevent it from any type of threats , we should perform the penetration testing.
Exploring the Social Engineering Toolkit (Set) Using Backtrack 5R3IJERA Editor
Linux Operating System is being reverenced by many professionals because of its versatile nature. As many network security professionals ,particularly those of ethical hackers use linux in an extensive way, did we ever observe how and why the number of hackers were enhancing day to day. Not only professionals ,every one are unleashing their hacking potentials with the help of Backtrack5R3 operating system which is a comprehensive tool kit for security auditing. This paper emphasizes on the so called SET (Social Engineering Toolkit).In a pen-testing scenario, alongside uncovering vulnerabilities in the hardware and software systems and exploiting them ,the most effective of all is penetrating the human mind to extract the desire information. Such devious technics are known as social engineering ,and computer based software tools to facilitate this form the basis of Social Engineering Toolkit
this ppt is base on construction of under ground cable. in this ppt i gave information the all type of insulation and its specification. and is advantages.
in this PPT i gave introductions of the dc chopper . basic principle and working of dc chopper. and all the contents are in ps bimbhra power electronics book.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
1. Cyber Security (2150002)
Active Learning Assignment
on
Metasploit
Prepared By:
Patel RajalKumar H.
(160123109013)
Guided By :
Prof. Abhishek Harit
Electrical Department
Batch-B3
Gandhinagar Institute Of technology 1
2. Introduction
• The Metasploit is a computer security that provides information
about security vulnerabilities and aids in penetration testing and
IDS signature development.
• Its best-known sub-project is the open source Metasploit
Framework, a tool for developing and executing exploit code
against a remote target machine.
• The Metasploit is well known for its anti-forensic and evasion
tools, some of which are built into the Metasploit Framework.
Gandhinagar Institute Of technology 2
3. History
• Metasploit was created by H. D. Moore in 2003 as a portable
network tool using Perl. By 2007, the Metasploit Framework
had been completely rewritten in Ruby.
• On October 21, 2009, the Metasploit Project announced that it
had been acquired by Rapid7, a security company that
provides unified vulnerability management solutions.
• Like comparable commercial products such as Immunity's
Canvas or Core Security Technologies' Core Impact,
Metasploit can be used to test the vulnerability of computer
systems or to break into remote systems.
Gandhinagar Institute Of technology 3
4. • Like many information security tools, Metasploit can be used for
both legitimate and unauthorized activities.
• Since the acquisition of the Metasploit Framework, Rapid7 has
added two open core proprietary editions called Metasploit
Express and Metasploit Pro.
• Metasploit's emerging position as the de facto exploit
development framework led to the release of software
vulnerability advisories often accompanied by a third party
Metasploit exploit module that highlights the exploitability, risk
and remediation of that particular bug.
Gandhinagar Institute Of technology 4
5. • Metasploit 3.0 began to include fuzzing tools, used to discover
software vulnerabilities, rather than just exploits for known
bugs. This avenue can be seen with the integration of the
lorcon wireless (802.11) toolset into Metasploit 3.0 in
November 2006. Metasploit 4.0 was released in August 2011.
Gandhinagar Institute Of technology 5
6. Metasploit Framework
• The basic steps for exploiting a system using the Framework
include:
1. Choosing and configuring an exploit (code that enters a target
system by taking advantage of one of its bugs; about 900
different exploits for Windows, Unix/Linux and Mac OS X
systems are included).
2. Optionally checking whether the intended target system is
susceptible to the chosen exploit.
Gandhinagar Institute Of technology 6
7. 3. Choosing and configuring a payload (code that will be
executed on the target system upon successful entry for
instance, a remote shell or a VNC server).
4. Choosing the encoding technique so that the intrusion-
prevention system (IPS) ignores the encoded payload.
5. Executing the exploit.
Gandhinagar Institute Of technology 7
8. Metasploit interfaces
1. Metasploit Framework Edition
• The free version. It contains a command line interface, third-party
import, manual exploitation and manual brute forcing. This free
version of metasploit project also includes Zenmap, a well known
ports-scanner and a compiler for Ruby, the language in which this
version of metasploit was written.
2. Metasploit Community Edition
• In October 2011, Rapid7 released Metasploit Community Edition, a
free, web-based user interface for Metasploit. Metasploit Community
is based on the commercial functionality of the paid-for editions with
a reduced set of features, including network discovery, module
browsing and manual exploitation. Metasploit Community is included
in the main installer.
Gandhinagar Institute Of technology 8
9. 3. Metasploit Express
• In April 2010, Rapid7 released Metasploit Express, an open-core
commercial edition for security teams who need to verify
vulnerabilities. It offers a graphical user interface, integrates nmap
for discovery, and adds smart brute forcing as well as automated
evidence collection.
4. Metasploit Pro
3. In October 2010, Rapid7 added Metasploit Pro, an open-core
commercial Metasploit edition for penetration testers. Metasploit
Pro adds onto Metasploit Express with features such as Quick Start
Wizards/Meta Modules, building and managing social engineering
campaigns, web application testing, an advanced Pro Console,
dynamic payloads for anti-virus evasion, integration with Nexpose
for ad-hoc vulnerability scans, and VPN pivoting.
Gandhinagar Institute Of technology 9
10. 5. Armitage
• Armitage is a graphical cyber attack management tool for the
Metasploit Project that visualizes targets and recommends exploits.
It is a free and open source network security tool notable for its
contributions to red team collaboration allowing for shared
sessions, data, and communication through a single Metasploit
instance.[11]
6. Cobalt Strike
• Cobalt Strike is a collection of threat emulation tools provided by
Strategic Cyber LLC (https://cobaltstrike.com/) to work with the
Metasploit Framework. Cobalt Strike includes all features of
Armitage and adds post-exploitation tools, in addition to report
generation features.
Gandhinagar Institute Of technology 10
