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
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
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.
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.
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.
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.
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.
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
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
When stars align: studies in data quality, knowledge graphs, and machine lear...
Volume 2-issue-6-2037-2039
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