Basic survey on malware analysis, tools and techniquesijcsa
The term malware stands for malicious software. It is a program installed on a system without the
knowledge of owner of the system. It is basically installed by the third party with the intention to steal some
private data from the system or simply just to play pranks. This in turn threatens the computer’s security,
wherein computer are used by one’s in day-to-day life as to deal with various necessities like education,
communication, hospitals, banking, entertainment etc. Different traditional techniques are used to detect
and defend these malwares like Antivirus Scanner (AVS), firewalls, etc. But today malware writers are one
step forward towards then Malware detectors. Day-by-day they write new malwares, which become a great
challenge for malware detectors. This paper focuses on basis study of malwares and various detection
techniques which can be used to detect malwares.
Malware is a worldwide pandemic. It is designed to damage computer systems without
the knowledge of the owner using the system. Software‟s from reputable vendors also contain
malicious code that affects the system or leaks information‟s to remote servers. Malware‟s includes
computer viruses, spyware, dishonest ad-ware, rootkits, Trojans, dialers etc. Malware detectors are
the primary tools in defense against malware. The quality of such a detector is determined by the
techniques it uses. It is therefore imperative that we study malware detection techniques and
understand their strengths and limitations. This survey examines different types of Malware and
malware detection methods.
Symantec propone un'analisi approfondita sui Rogue Security Software. I RSS sono applicazioni fasulle che fingono di fornire servizi di tutela della sicurezza informatica ma che, al contrario, hanno come obiettivo quello di installare dei codici maligni che compromettono la sicurezza generale della macchina.
Panoramica - Rischi - Principali modalità di diffusione e distribuzione.
Il periodo di osservazione va da luglio 2008 a giugno 2009.
Basic survey on malware analysis, tools and techniquesijcsa
The term malware stands for malicious software. It is a program installed on a system without the
knowledge of owner of the system. It is basically installed by the third party with the intention to steal some
private data from the system or simply just to play pranks. This in turn threatens the computer’s security,
wherein computer are used by one’s in day-to-day life as to deal with various necessities like education,
communication, hospitals, banking, entertainment etc. Different traditional techniques are used to detect
and defend these malwares like Antivirus Scanner (AVS), firewalls, etc. But today malware writers are one
step forward towards then Malware detectors. Day-by-day they write new malwares, which become a great
challenge for malware detectors. This paper focuses on basis study of malwares and various detection
techniques which can be used to detect malwares.
Malware is a worldwide pandemic. It is designed to damage computer systems without
the knowledge of the owner using the system. Software‟s from reputable vendors also contain
malicious code that affects the system or leaks information‟s to remote servers. Malware‟s includes
computer viruses, spyware, dishonest ad-ware, rootkits, Trojans, dialers etc. Malware detectors are
the primary tools in defense against malware. The quality of such a detector is determined by the
techniques it uses. It is therefore imperative that we study malware detection techniques and
understand their strengths and limitations. This survey examines different types of Malware and
malware detection methods.
Symantec propone un'analisi approfondita sui Rogue Security Software. I RSS sono applicazioni fasulle che fingono di fornire servizi di tutela della sicurezza informatica ma che, al contrario, hanno come obiettivo quello di installare dei codici maligni che compromettono la sicurezza generale della macchina.
Panoramica - Rischi - Principali modalità di diffusione e distribuzione.
Il periodo di osservazione va da luglio 2008 a giugno 2009.
Review on mobile threats and detection techniquesijdpsjournal
Since last-decade, smart-phones have gained widespread usage. Mobile devices store personal details
such as contacts and text messages. Due to this extensive growth, smart-phones are attracted towards
cyber-criminals. In this research work, we have done a systematic review of the terms related to malware
detection algorithms and have also summarized behavioral description of some known mobile malwares
in tabular form. After careful solicitation of all the possible methods and algorithms for detection of
mobile-based malwares, we give some recommendations for designing future malware detection algorithm
by considering computational complexity and detection ration of mobile malwares.
Bitdefender - Solution Paper - Active Threat ControlJose Lopez
This Solution Paper describes how Bitdefender's Active Threat Control can protect Windows Endpoints both desktops and servers from Advanced and 0-day threats like Cryptomalware thanks to a proactive-by-design, dynamic detection technology, based on monitoring processes’ behavior, along with tagging and correlating suspect activities with minimal footprint
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.
Malware Detection Using Data Mining Techniques Akash Karwande
Computer programs which have a destructive content and applied to systems from invader, are called malware and the systems on which this program are applied is called victim system .
Malwares are classified into several kinds based on behavior or attack methods.
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMSieijjournal
An intrusion detection system detects various malicious behaviors and abnormal activities that might harm
security and trust of computer system. IDS operate either on host or network level via utilizing anomaly
detection or misuse detection. Main problem is to correctly detect intruder attack against computer
network. The key point of successful detection of intrusion is choice of proper features. To resolve the
problems of IDS scheme this research work propose “an improved method to detect intrusion using
machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion
detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random
forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three
algorithms J48, J48Graft and Random forest gives much better results than other machine learning
algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have
considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.
Separating Fact from Fiction – The realities of Cyber War
By Don Eijndhoven
Multifactor Authentication – A Requirement for the 21st Century By Robert Keeler
Regulatory Compliance under the Indian Cyber Laws
by Sagar Rahurkar
Ride the Dragon: Testing the Desktop by adopting criminal tools and strategies by Stefano MacGalia
Social Engineering by Falgun Rathod
Benefits of Attributionby Sayngeun Phouamkha
Attacking POS: history, technique and a look to the future
Have multiple attribute sets and want to transfer them to another store? Use advanced Store Manager export wizard to pull down attribute sets to .csv in several clicks only.
Review on mobile threats and detection techniquesijdpsjournal
Since last-decade, smart-phones have gained widespread usage. Mobile devices store personal details
such as contacts and text messages. Due to this extensive growth, smart-phones are attracted towards
cyber-criminals. In this research work, we have done a systematic review of the terms related to malware
detection algorithms and have also summarized behavioral description of some known mobile malwares
in tabular form. After careful solicitation of all the possible methods and algorithms for detection of
mobile-based malwares, we give some recommendations for designing future malware detection algorithm
by considering computational complexity and detection ration of mobile malwares.
Bitdefender - Solution Paper - Active Threat ControlJose Lopez
This Solution Paper describes how Bitdefender's Active Threat Control can protect Windows Endpoints both desktops and servers from Advanced and 0-day threats like Cryptomalware thanks to a proactive-by-design, dynamic detection technology, based on monitoring processes’ behavior, along with tagging and correlating suspect activities with minimal footprint
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.
Malware Detection Using Data Mining Techniques Akash Karwande
Computer programs which have a destructive content and applied to systems from invader, are called malware and the systems on which this program are applied is called victim system .
Malwares are classified into several kinds based on behavior or attack methods.
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMSieijjournal
An intrusion detection system detects various malicious behaviors and abnormal activities that might harm
security and trust of computer system. IDS operate either on host or network level via utilizing anomaly
detection or misuse detection. Main problem is to correctly detect intruder attack against computer
network. The key point of successful detection of intrusion is choice of proper features. To resolve the
problems of IDS scheme this research work propose “an improved method to detect intrusion using
machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion
detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random
forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three
algorithms J48, J48Graft and Random forest gives much better results than other machine learning
algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have
considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.
Separating Fact from Fiction – The realities of Cyber War
By Don Eijndhoven
Multifactor Authentication – A Requirement for the 21st Century By Robert Keeler
Regulatory Compliance under the Indian Cyber Laws
by Sagar Rahurkar
Ride the Dragon: Testing the Desktop by adopting criminal tools and strategies by Stefano MacGalia
Social Engineering by Falgun Rathod
Benefits of Attributionby Sayngeun Phouamkha
Attacking POS: history, technique and a look to the future
Have multiple attribute sets and want to transfer them to another store? Use advanced Store Manager export wizard to pull down attribute sets to .csv in several clicks only.
Journey through the landing page best practicesEmail Monks
The road to business success is very long. Beginning from the implementation of a marketing strategy to measuring return on investment (ROI), it is a long-lasting journey. Landing pages are one of the various milestones that collectively lead to the successful achievement of your goals. Some of the prominent companies and even electoral campaigns have measured tremendous success with the various methodologies involved in landing pages.
Fasten your seat-belts as you go on a journey to witness some of the best landing page practices.
A FRAMEWORK FOR ANALYSIS AND COMPARISON OF DYNAMIC MALWARE ANALYSIS TOOLSIJNSA Journal
Malware writers have employed various obfuscation and polymorphism techniques to thwart static analysis approaches and bypassing antivirus tools. Dynamic analysis techniques, however, have essentially overcome these deceits by observing the actual behaviour of the code execution. In this regard, various methods, techniques and tools have been proposed. However, because of the diverse concepts and strategies used in the implementation of these methods and tools, security researchers and malware analysts find it difficult to select the required optimum tool to investigate the behaviour of a malware and to contain the associated risk for their study. Focusing on two dynamic analysis techniques: Function Call monitoring and Information Flow Tracking, this paper presents a comparison framework for dynamic malware analysis tools. The framework will assist the researchers and analysts to recognize the tool’s implementation strategy, analysis approach, system-wide analysis support and its overall handling of binaries, helping them to select a suitable and effective one for their study and analysis.
A FRAMEWORK FOR ANALYSIS AND COMPARISON OF DYNAMIC MALWARE ANALYSIS TOOLSIJNSA Journal
Malware writers have employed various obfuscation and polymorphism techniques to thwart static analysis
approaches and bypassing antivirus tools. Dynamic analysis techniques, however, have essentially
overcome these deceits by observing the actual behaviour of the code execution. In this regard, various
methods, techniques and tools have been proposed. However, because of the diverse concepts and
strategies used in the implementation of these methods and tools, security researchers and malware
analysts find it difficult to select the required optimum tool to investigate the behaviour of a malware and to
contain the associated risk for their study. Focusing on two dynamic analysis techniques: Function Call
monitoring and Information Flow Tracking, this paper presents a comparison framework for dynamic
malware analysis tools. The framework will assist the researchers and analysts to recognize the tool’s
implementation strategy, analysis approach, system-wide analysis support and its overall handling of
binaries, helping them to select a suitable and effective one for their study and analysis.
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.
The digital world is plagued by cyber threats that have the potential to cause widespread damage to businesses, organizations, and individuals. One of the most common types of cyber attacks is the buffer overflow attack. This article will explore the concept of remote buffer overflow attacks, their consequences, and prevention measures.
Cybersecurity has become a primary concern in today’s digital age. The increasing number of cyber-attacks highlights the importance of understanding the vulnerabilities that exist in computer systems and how to protect against them. One such vulnerability is a remote buffer overflow exploit. In this article, we will explore what a remote buffer overflow exploit is and how to use Python to create one.The digital world is plagued by cyber threats that have the potential to cause widespread damage to businesses, organizations, and individuals. One of the most common types of cyber attacks is the buffer overflow attack. This article will explore the concept of remote buffer overflow attacks, their consequences, and prevention measures.
Cybersecurity has become a primary concern in today’s digital age. The increasing number of cyber-attacks highlights the importance of understanding the vulnerabilities that exist in computer systems and how to protect against them. One such vulnerability is a remote buffer overflow exploit. In this article, we will explore what a remote buffer overflow exploit is and how to use Python to create one.The digital world is plagued by cyber threats that have the potential to cause widespread damage to businesses, organizations, and individuals. One of the most common types of cyber attacks is the buffer overflow attack. This article will explore the concept of remote buffer overflow attacks, their consequences, and prevention measures.
Cybersecurity has become a primary concern in today’s digital age. The increasing number of cyber-attacks highlights the importance of understanding the vulnerabilities that exist in computer systems and how to protect against them. One such vulnerability is a remote buffer overflow exploit. In this article, we will explore what a remote buffer overflow exploit is and how to use Python to create one.The digital world is plagued by cyber threats that have the potential to cause widespread damage to businesses, organizations, and individuals. One of the most common types of cyber attacks is the buffer overflow attack. This article will explore the concept of remote buffer overflow attacks, their consequences, and prevention measures.
Cybersecurity has become a primary concern in today’s digital age. The increasing number of cyber-attacks highlights the importance of understanding the vulnerabilities that exist in computer systems and how to protect against them. One such vulnerability is a remote buffer overflow exploit. In this article, we will explore what a remote buffer overflow exploit is and how to use Python to create one.The digital world is plagued
Taxonomy mobile malware threats and detection techniquescsandit
Since last-decade, smart-phones have gained widespr
ead usage. Mobile devices store personal
details such as contacts and text messages. Due to
this extensive growth, smart-phones are
attracted towards cyber-criminals. In this research
work, we have done a systematic review of
the terms related to malware detection algorithms
and have also summarized behavioral
description of some known mobile malwares in tabula
r form. After careful solicitation of all the
possible methods and algorithms for detection of m
obile-based malwares, we give some
recommendations for designing future malware detect
ion algorithm by considering
computational complexity and detection ration of m
obile malwares.
CS266 Software Reverse Engineering (SRE)
Identifying, Monitoring, and Reporting Malware
Teodoro (Ted) Cipresso, teodoro.cipresso@sjsu.edu
Department of Computer Science
San José State University
Spring 2015
Malware Protection
Week5Part4-IS
Revision Fall2013
Malware Protection
Malware protection use to be known simply as virus protection. We have learned that
viruses are one form of malicious software and that a broader term to describe the
multitude of threats and the protection mechanism is needed. This is why the term
Malware is broader categorization of the threat and also the protection. Malware is a
portmanteau of the terms Malicious Software. Different malware protection packages
can cover a range of threats including viruses, worms, Trojans, spyware, adware, rootkits
to name a few.
As malware has evolved so has malware protection. Malware protection packages (MPP)
have evolved to provide more comprehensive protection mechanisms; including
firewalls, Intrusion Detection/Protection Systems (IDS/IPS), remote and central
management of system clusters, heterogeneous system protection and management,
signature and heuristic scanning, sandboxing to name just a few features.
It is important to understand that no one Malware Protection Package will find all pieces
of malware. Each package has its strengths and weaknesses. It is a good idea to always
have some form of malware protection running on your system in real time. However,
should you become infected it is useful to have an alternative strategy making use of
other scanners that you can run manually.
Free or Paid for Scanners
There is an adage that “you get what you pay for”. Generally this is true, but over time I
have found that there are some excellent free malware scanners that for single user
systems do a nice job. Some major requirements I have for a malware scanner are: it is
easy to run; does not require a lot of user interaction, uses little system resources, does a
good job finding and removing threats and automatically updates its signature database.
The following is not an endorsement for paid versus free scanners. It represents my
experiences for what they are worth.
I use to have a paid for Norton subscription. I found that over time the system footprint
for Norton grew which meant Norton required more CPU and overall system resources
for its real-time scanning processes. I think Norton has got better based on recent
experience I have with Windows 8 however at the time I had several performance
problems related to Norton. This got me to switch to free AVG. I used AVG for a while
and had real good luck, until AVG’s advertising got obnoxious. I decided to remove
AVG and found that process very difficult. I finally succeeded and then moved to using
free Avast. I have been using Avast for several years having very good luck.
I then started testing various malware scanners on virtual machines. This got me familiar
with Microsoft Security Essentials. This is a free product offered by Microsoft that nicely
integrates with Windows Vista and Windows 7 systems. I like the simplicity of its
inte ...
This comprehensive guide delves into the essential types of testing used in cybersecurity to ensure the resilience of digital systems against malicious attacks. From vulnerability assessments and penetration testing to social engineering and security audits, each testing method is examined in detail, providing insights into their purpose, methodology, and significance in safeguarding against cyber threats. Whether you're a cybersecurity professional seeking to deepen your knowledge or a novice looking to understand the fundamentals, this guide offers valuable insights into the world of cybersecurity testing. for more cybersecurity knowledge visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/#
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.
Unveiling the Shadows: A Comprehensive Guide to Malware Analysis for Ensuring...cyberprosocial
Malicious software, or malware, is a constant concern in the networked world of digital landscapes. Cybercriminals are always improving their strategies, which makes malware more complex and difficult to identify. To combat this, protecting computer systems requires an understanding of and application of malware analysis.
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
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.
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
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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
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/
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.
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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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
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.
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
1. ISSN: 2278 - 1323
International Journal of Advanced Research in Computer Engineering and Technology (IJARCET)
Volume 2, Issue 6, June 2013
2037
www.ijarcet.org
Abstract— Malware is one of the major security threats in
computer and network environment. However,
Signature-based approach that commonly used does not
provide enough opportunity to learn and understand malware
threats that can be used in implementing security prevention
mechanisms. In order to learn and understand the malwares,
behavior-based technique that applied dynamic approach is the
possible solution for identification, classification and clustering
the malwares.[1] However, it is still unpopular because of its
rigid and restrictive nature. In this paper, we study and analyze
both approaches and try to determine the best and optimal
anti-malware approach.
Index Terms—Anomaly, Behavior-based, Signature-based,
Specification-based.
I. INTRODUCTION
Malware (Malicious Software) is software that is
designed to deliberately infiltrate or damage a computer
system without the owner’s knowledge. It can appear in the
form of code, scripts, active content and other software.[2]
Numerous attacks made by malware pose a major security
threat to all computer users. Hence, malware detection is one
of the computer security topics that are of great interest.
The amount of malware threats on the Internet has
increased significantly over the past few years. Hence the
traditional methods of malware detection do not suffice.
Newer techniques and mechanisms have to be explored.
In this paper we will study and analyze various techniques
which use either Signature-based or Behavior-based
Malware detection approach.
II. AN OVERVIEW OF ANTI-MALWARE TECHNOLOGIES
Malware and other potentially harmful software have a
great impact on user’s security, reliability and privacy.
Hackers are becoming increasingly motivated by financial
gain to steal confidential or personal information rather than
just vandalizing the client machine. Moreover, users can
experience serious performance and stability problems with
Ashwini Mujumdar, Department of Computer Engineering, Veermata
Jijabai Technological Institute (VJTI), Mumbai, India.
Gayatri Masiwal, Department of Computer Engineering, Veermata
Jijabai Technological Institute (VJTI), Mumbai, India
Dr. B. B. Meshram, Department of Computer Engineering, Veermata
Jijabai Technological Institute (VJTI), Mumbai, India
their computer, due to presence of spyware and other
malware.
An anti-malware engine is responsible for detection and
removal of malware as it attempts to infect a computer. This
engine performs three main tasks:
A. Scanning
The engine must examine and monitor various locations of
the computer such as the hard disk, registry and main
memory. If a change to a critical component is detected, it
could be a sign of infection.
B. Detection
Once the engine has detected an item that requires further
examination, called candidate, by detecting a change or by
explicit request by the user, it must identify the presence of
malware, if any. The engine refers to a frequently updated list
of known malware, called the Blacklist, which contains
“signatures” or identifiable patterns of known malware.
Using this list, the engine can determine whether any file
matches any of the known malware. If a match is found, the
file is classified according to the signature as worm, virus,
Trojan etc.
C. Removal
The final step for this engine is to take appropriate actions
on files that are identified as malware. In most circumstances,
the engine removes the program or file completely and
restores the computer to its ore-infection state. Otherwise, a
file can be disabled or quarantined, so that the user could
enable it later.
III. SIGNATURE-BASED ANTI-MALWARE APPROACH
Signature-based detection is an anti-malware approach
that identifies the presence of a malware infection or instance
by matching at least one byte code pattern of the software in
question with the database of signatures of known malicious
programs, also known as blacklists. This detection scheme is
based on the assumption that malware can be described
through patterns (also called signatures).[3] Signature-based
detection is the most commonly used technique for
anti-malware systems.
However, this technique has certain disadvantages:
A. Susceptible to evasion
Since the signature byte patterns are derived from known
malware, these byte patterns are also commonly known.
Hence they can be easily evaded by hackers using simple
obfuscation techniques such as inserting no-ops and code
re-ordering. Thus malware code can be altered and
Analysis of Signature-Based and
Behavior-Based Anti-Malware Approaches
Ashwini Mujumdar, Gayatri Masiwal, Dr. B. B. Meshram
2. ISSN: 2278 - 1323
International Journal of Advanced Research in Computer Engineering and Technology (IJARCET)
Volume 2, Issue 6, June 2013
2038
signature-based detection can be evaded.
B. Zero-day attacks
Since the signature-based anti-malware systems are
constructed on the basis of known malware, they are unable
to detect unknown malware, or even variants of known
malware. Thus, without accurate signatures, they cannot
effectively detect polymorphic [4] malware. Therefore,
signature-based detection does not provide zero-day
protection. Moreover, since a signature-based detector uses a
separate signature for each malware variant, the database of
signatures grows at an exponential rate.
IV. WHITELISTING: ANOTHER MALWARE DETECTION
TECHNIQUE FOR SIGNATURE-BASED APPROACH
Signature-based Blacklisting of malware is no longer
enough for protection. Whitelisting is an alternative to this.
Whitelisting is a popular technique among computer users
to actively manage the software that is being installed on their
computer. Whitelisting involves permitting only approved
software to install and run. Software products that are not
explicitly on the control list lock down the computer.
Whitelisting is a very promising way to protect computers,
but it also creates a very rigid environment where rules about
what software can be downloaded and installed are strict.
But whitelisting detection has three drawbacks.
• Firstly, it can create an annoying computer experience.
Users are subjected to pop-up warnings constantly.
• Secondly, whitelisting limits users' ability to easily
download and use new software.
• Thirdly, whitelisted applications can be vulnerable. For
example, if you whitelist a browser, then any malware that
operates inside the browser will not be detected. In fact, a lot
of malware inject themselves into the browser.
V. BEHAVIOR-BASED ANTI-MALWARE APPROACH
Behavior-based approaches of malware detection monitor
behaviors of a program to determine whether it is malicious
or not. Behavior based method observes behaviors of a
program from outside by actually executing it, and if the
program performs the pre-defined malicious behaviors, it can
be identified as malware.[5] The behavior of a program that
is typically monitored is the stream of system calls that the
program issues to the operating system. Since behavior-based
techniques monitor what a program does, they are not
susceptible to the shortcomings of signature-based detection
discussed earlier. Simply put, a behavior-based detector
determines whether a program is malicious by inspecting
what it does rather than what it says. Several types of
behavior-based detections exist.
VI. ANOMALY DETECTION: A BEHAVIOR-BASED MALWARE
DETECTION TECHNIQUE
One major approach of behavior-based detection is
anomaly detection. In this approach of malware detection, a
profile of normal program behavior is constructed. Any
deviations from that profile are flagged as anomalous and
thus suspicious.
Anomaly detection is analogous to credit card fraud
detection. Credit card companies maintain "spending
profiles" for their customers. Any significant deviation from
these profiles is flagged as suspicious.
For example, if a credit card company notices a large
expense in a shop in Europe, and the customer has not
shopped in Europe in the last few years, they will flag that
transaction as anomalous. Similarly, let's say a program,
during its normal execution, never writes to a certain
sensitive directory. If the monitoring system notices writes to
that sensitive directory from the program, the detection
system will flag that behavior as anomalous.
Anomaly detection has the following two shortcomings:
A. It is susceptible to false positives
Normal behavior for complex programs is very
complicated. For example, the set of behaviors of Mozilla
Firefox are very complex. Therefore, it is very hard to
construct a model of normal behavior of a complex program.
An inadequate model of normal behavior can lead to false
positives.
B. It is susceptible to mimicry attacks
It has been demonstrated that anomaly detection-based
techniques are susceptible to mimicry attacks. In a mimicry
attack, an attacker transforms his attack into another
equally-malicious attack, but the transformed attack is
allowed by the model of normal execution of the program.
For this, the attacker has to be familiar with the normal
execution model of the program.
VII. SPECIFICATION-BASED MONITORING
Specification-based monitoring is a type of behavior-based
detection technique, which also makes use of signature-based
detection to some extent. In the specification-based approach
of malware detection, all events from the program to the
operating system are mediated by a specification or policy.
The policy specifies what action should be taken for a
sequence of events. Typically, the actions are allow, deny or
log.
For example, we might have a policy for a browser which
states that "any files downloaded from a Web site (not on a
whitelist) cannot be automatically executed." This policy will
not allow a user to download files from a Web site which are
not on a whitelist and execute them. These kinds of
specification policies can be very effective in addressing
important infection vectors such as drive-by-downloads.
Specification-based monitoring has the following two
advantages over anomaly detection:
A. It has flexibility
Specification-based monitoring decouples policy
construction from enforcement. For example, one can
imagine having a policy in a specification-based monitoring