This document provides an overview of intrusion detection systems (IDS), including their challenges, potential solutions, and future developments. It discusses how IDS aim to detect attacks against computer systems and networks. The challenges of high false alarm rates and dependency on the environment are outlined. Potential solutions explored include data mining, machine learning, and co-simulation mechanisms. Alarm correlation techniques are examined as ways to combine fragmented alert information to better interpret attack flows. Artificial intelligence is seen as important for improving IDS flexibility, adaptability, and pattern recognition.
This presentation will cover all you need to know about mobile and application device security.
With an introduction, threats, applications, security, and useful tips for people who need to know
So, let's get started. If you enjoy this and find the information beneficial, please like and share it with your friends.
This presentation brings out few basic steps that every android phone user should configure to harden his/her device.Although the list is not completly exhaustive but it brings out basic necessities as expected from any smart user.
Intrusion detection and prevention systemNikhil Raj
This presentation describes how to implement Network based Intrusion Detection System (SNORT) in the network. Detecting and analyzing alerts generated and blocking the Attacker using Access Control List.
Slides for a college course at City College San Francisco. Based on "Hands-On Ethical Hacking and Network Defense, Third Edition" by Michael T. Simpson, Kent Backman, and James Corley -- ISBN: 9781285454610.
Instructor: Sam Bowne
Class website: https://samsclass.info/123/123_S17.shtml
VAPT defines the security measures that are supposed to be put in place to address cyber threats. There are plenty of strategies that can be adopted in Pen Testing which include Black Box Pen Test, White Box Pen Text, Hidden Pen Test, Internal Pen Test, and Gray Box Testing. It is mandatory that VAPT is conducted in order to deter cyber-attacks that are on the upsurge daily. These VAPT ranges from Mobile, Network Penetration Testing, and Vulnerability Assessments.
There are many merits to VAPT in your business which include early error detection in program codes which will prevent cyber attacks. Most companies lose billions of dollars due to cyber-attacks. With VAPT, it guarantees that all loopholes are tightened before an intrusion transpires.
ids&ips technique is used to capture logs,sessions,port no,trojans,and malicious activity on the networkand servers.here u can get detailed about ids and ips techniques
This presentation will cover all you need to know about mobile and application device security.
With an introduction, threats, applications, security, and useful tips for people who need to know
So, let's get started. If you enjoy this and find the information beneficial, please like and share it with your friends.
This presentation brings out few basic steps that every android phone user should configure to harden his/her device.Although the list is not completly exhaustive but it brings out basic necessities as expected from any smart user.
Intrusion detection and prevention systemNikhil Raj
This presentation describes how to implement Network based Intrusion Detection System (SNORT) in the network. Detecting and analyzing alerts generated and blocking the Attacker using Access Control List.
Slides for a college course at City College San Francisco. Based on "Hands-On Ethical Hacking and Network Defense, Third Edition" by Michael T. Simpson, Kent Backman, and James Corley -- ISBN: 9781285454610.
Instructor: Sam Bowne
Class website: https://samsclass.info/123/123_S17.shtml
VAPT defines the security measures that are supposed to be put in place to address cyber threats. There are plenty of strategies that can be adopted in Pen Testing which include Black Box Pen Test, White Box Pen Text, Hidden Pen Test, Internal Pen Test, and Gray Box Testing. It is mandatory that VAPT is conducted in order to deter cyber-attacks that are on the upsurge daily. These VAPT ranges from Mobile, Network Penetration Testing, and Vulnerability Assessments.
There are many merits to VAPT in your business which include early error detection in program codes which will prevent cyber attacks. Most companies lose billions of dollars due to cyber-attacks. With VAPT, it guarantees that all loopholes are tightened before an intrusion transpires.
ids&ips technique is used to capture logs,sessions,port no,trojans,and malicious activity on the networkand servers.here u can get detailed about ids and ips techniques
Seminar Report - Network Intrusion Prevention by Configuring ACLs on the Rout...Disha Bedi
Base Paper presented by - Muhammad Naveed, Shams un Nihar and Mohammad Inayatullah Babar At 2010 6th International Conference on Emerging Technologies (ICET)
Cloudslam09:Building a Cloud Computing Analysis System for Intrusion DetectionWei-Yu Chen
In order to resolve huge amount of anomaly
information generated by Intrusion Detection System (IDS), this paper presents and evaluates a log analysis system for IDS based on Cloud Computing technique,
named IDS Cloud Analysis System (ICAS). To achieve this, there are two basic components have to be designed. First is the regular parser, which normalizes
the raw log files. The other is the Analysis Procedure, which contains Data Mapper and Data Reducer. The Data Mapper is designed to anatomize alert messages and the Data Reducer is used to aggregates and merges. As a result, this paper will show that the
performance of ICAS is suitable for analyzing and reducing large alerts.
Network Intrusion Prevention by Configuring ACLs on the Routers, based on Sno...Disha Bedi
Base Paper presented by - Muhammad Naveed, Shams un Nihar and Mohammad Inayatullah Babar At 2010 6th International Conference on Emerging Technologies (ICET)
Detecting Anomaly IDS in Network using Bayesian NetworkIOSR Journals
In a hostile area of network, it is a severe challenge to protect sink, developing flexible and adaptive
security oriented approaches against malicious activities. Intrusion detection is the act of detecting, monitoring
unwanted activity and traffic on a network or a device, which violates security policy. This paper begins with a
review of the most well-known anomaly based intrusion detection techniques. AIDS is a system for detecting
computer intrusions, type of misuse that falls out of normal operation by monitoring system activity and
classifying it as either normal or anomalous .It is based on Machine Learning AIDS schemes model that allows
the attacks analyzed to be categorized and find probabilistic relationships among attacks using Bayesian
network.
An intrusion detection system for packet and flow based networks using deep n...IJECEIAES
Study on deep neural networks and big data is merging now by several aspects to enhance the capabilities of intrusion detection system (IDS). Many IDS models has been introduced to provide security over big data. This study focuses on the intrusion detection in computer networks using big datasets. The advent of big data has agitated the comprehensive assistance in cyber security by forwarding a brunch of affluent algorithms to classify and analysis patterns and making a better prediction more efficiently. In this study, to detect intrusion a detection model has been propounded applying deep neural networks. We applied the suggested model on the latest dataset available at online, formatted with packet based, flow based data and some additional metadata. The dataset is labeled and imbalanced with 79 attributes and some classes having much less training samples compared to other classes. The proposed model is build using Keras and Google Tensorflow deep learning environment. Experimental result shows that intrusions are detected with the accuracy over 99% for both binary and multiclass classification with selected best features. Receiver operating characteristics (ROC) and precision-recall curve average score is also 1. The outcome implies that Deep Neural Networks offers a novel research model with great accuracy for intrusion detection model, better than some models presented in the literature.
Cyber security is a Major concern in the world. As a result of frequent and consistent daily cyber attack, this journal was written to enlighten viewers and readers on zero day attack prediction
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SETIJNSA Journal
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SETIJNSA Journal
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
In order to avoid illegitimate use of any intruder, intrusion detection over the network is one of the critical
issues. An intruder may enter any network or system or server by intruding malicious packets into the
system in order to steal, sniff, manipulate or corrupt any useful and secret information, this process is
referred to as intrusion whereas when packets are transmitted by intruder over the network for any purpose
of intrusion is referred to as attack. With the expanding networking technology, millions of servers
communicate with each other and this expansion is always in progress every day. Due to this fact, more
and more intruders get attention; and so to overcome this need of smart intrusion detection model is a
primary requirement.
By analyzing the feature selection methods the identification of essential features of NSL-KDD data set is
done, then by using selected features and machine learning approach and analyzing the basic features of
networks over the data set a hybrid algorithm is made. Finally a model is produced over the algorithm
containing the rules for the network features.
A hybrid misuse intrusion detection model is made to find attacks on system to improve the intrusion
detection. Based on prior features, intrusions on the system can be detected without any previous learning.
This model contains the advantage of feature selection and machine learning techniques with misuse
detection.
The main goal of Intrusion Detection Systems (IDSs) is
to detect intrusions. This kind of detection system represents a
significant tool in traditional computer based systems for ensuring
cyber security. IDS model can be faster and reach more accurate
detection rates, by selecting the most related features from the
input dataset. Feature selection is an important stage of any IDs to
select the optimal subset of features that enhance the process of the
training model to become faster and reduce the complexity while
preserving or enhancing the performance of the system. In this
paper, we proposed a method that based on dividing the input
dataset into different subsets according to each attack. Then we
performed a feature selection technique using information gain
filter for each subset. Then the optimal features set is generated by
combining the list of features sets that obtained for each attack.
Experimental results that conducted on NSL-KDD dataset shows
that the proposed method for feature selection with fewer features,
make an improvement to the system accuracy while decreasing the
complexity. Moreover, a comparative study is performed to the
efficiency of technique for feature selection using different
classification methods. To enhance the overall performance,
another stage is conducted using Random Forest and PART on
voting learning algorithm. The results indicate that the best
accuracy is achieved when using the product probability rule.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
ATTACK DETECTION AVAILING FEATURE DISCRETION USING RANDOM FOREST CLASSIFIERCSEIJJournal
The widespread use of the Internet has an adverse effect of being vulnerable to cyber attacks. Defensive
mechanisms like firewalls and IDSs have evolved with a lot of research contributions happening in these
areas. Machine learning techniques have been successfully used in these defense mechanisms especially
IDSs. Although they are effective to some extent in identifying new patterns and variants of existing
malicious patterns, many attacks are still left as undetected. The objective is to develop an algorithm for
detecting malicious domains based on passive traffic measurements. In this paper, an anomaly-based
intrusion detection system based on an ensemble based machine learning classifier called Random Forest
with gradient boosting is deployed. NSL-KDD cup dataset is used for analysis and out of 41 features, 32
features were identified as significant using feature discretion. Our observations confirm the conjecture
that both the feature selection and stochastic based genetic operators improves the accuracy and the
effectiveness. The training time is shown to be reduced tremendously by 98.59% and accuracy improved to
98.75%.
Attack Detection Availing Feature Discretion using Random Forest ClassifierCSEIJJournal
The widespread use of the Internet has an adverse effect of being vulnerable to cyber attacks. Defensive
mechanisms like firewalls and IDSs have evolved with a lot of research contributions happening in these
areas. Machine learning techniques have been successfully used in these defense mechanisms especially
IDSs. Although they are effective to some extent in identifying new patterns and variants of existing
malicious patterns, many attacks are still left as undetected. The objective is to develop an algorithm for
detecting malicious domains based on passive traffic measurements. In this paper, an anomaly-based
intrusion detection system based on an ensemble based machine learning classifier called Random Forest
with gradient boosting is deployed. NSL-KDD cup dataset is used for analysis and out of 41 features, 32
features were identified as significant using feature discretion.
Review of Intrusion and Anomaly Detection Techniques IJMER
Intrusion detection is the act of detecting actions that attempt to compromise the
confidentiality, integrity or availability of a resource. With the tremendous growth of network-based
services and sensitive information on networks, network security is getting more and more importance
than ever. Intrusion poses a serious security threat in a huge network environment. The increasing use of
internet has dramatically added to the growing number of threats that inhabit within it. Intrusion
detection does not, in general, include prevention of intrusions. Now a days Network intrusion detection
systems have become a standard component in the area of security infrastructure. This review paper tries
to discusses various techniques which are already being used for intrusion detection.
Supervised Machine Learning Algorithms for Intrusion Detection.pptxssuserf3a100
Intrusion detection systems using supervised machine learning algorithms are considered one of the most important tools used in the field of information security. These systems analyze data and detect illegal activities and intrusions into networks and systems. These systems rely on machine learning techniques to classify data as either normal activity or a hack. These systems include training and testing phases, where the algorithms are trained on a set of pre-labeled data to learn the natural pattern of the data and distinguish between normal activities and intrusions. Many supervisory machine learning algorithms are available for intrusion detection systems, such as Gaussian Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, and Logistic Regression.
The problem of security and electronic breaches targeting networks is one of the biggest problems facing organizations today. To solve this problem, intrusion detection systems (IDS) and their tools can be used to detect and prevent these threats. This file provides an introductory overview of this problem
Security breaches
networks
Infiltration (IDS)
Algorithms
Machine learning
FORTIFICATION OF HYBRID INTRUSION DETECTION SYSTEM USING VARIANTS OF NEURAL ...IJNSA Journal
Intrusion Detection Systems (IDS) form a key part of system defence, where it identifies abnormal
activities happening in a computer system. In recent years different soft computing based techniques have
been proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfect
technology. This has provided an opportunity for data mining to make quite a lot of important
contributions in the field of intrusion detection. In this paper we have proposed a new hybrid technique
by utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Neurofuzzy and radial basis function(RBF) SVM for fortification of the intrusion detection system. The
proposed technique has five major steps in which, first step is to perform the relevance analysis, and then
input data is clustered using Fuzzy C-means clustering. After that, neuro-fuzzy is trained, such that each
of the data point is trained with the corresponding neuro-fuzzy classifier associated with the cluster.
Subsequently, a vector for SVM classification is formed and in the last step, classification using RBF-
SVM is performed to detect intrusion has happened or not. Data set used is the KDD cup 1999 dataset
and we have used precision, recall, F-measure and accuracy as the evaluation metrics parameters. Our
technique could achieve better accuracy for all types of intrusions. The results of proposed technique are
compared with the other existing techniques. These comparisons proved the effectiveness of our
technique.
SURVEY OF NETWORK ANOMALY DETECTION USING MARKOV CHAINijcseit
Recently an internet threat has been increased. Our motive is detect the intrusion in the network in concise.
The real time issue such as DoS attack in banking, companies, industries and organization have been
increased significantly IDS has been used in both server and host side. The major challenge is to effectively
predict the periods of threats and protect the server from the unauthorized user. In this study, a novel
probabilistic approach is proposed effectively to detect the network intrusions. It uses a Markov chain for
probabilistic modelling of abnormal events in network systems. The degree of abnormality of the incoming
data is performed on the basis of the network states.
Survey of network anomaly detection using markov chainijcseit
Recently an internet threat has been increased. Our motive is detect the intrusion in the network in concise.
The real time issue such as DoS attack in banking, companies, industries and organization have been
increased significantly IDS has been used in both server and host side. The major challenge is to effectively
predict the periods of threats and protect the server from the unauthorized user. In this study, a novel
probabilistic approach is proposed effectively to detect the network intrusions. It uses a Markov chain for
probabilistic modelling of abnormal events in network systems. The degree of abnormality of the incoming
data is performed on the basis of the network states.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
Recently an internet threat has been increased. Our motive is detect the intrusion in the network in concise.
The real time issue such as DoS attack in banking, companies, industries and organization have been
increased significantly IDS has been used in both server and host side. The major challenge is to effectively
predict the periods of threats and protect the server from the unauthorized user. In this study, a novel
probabilistic approach is proposed effectively to detect the network intrusions. It uses a Markov chain for
probabilistic modelling of abnormal events in network systems. The degree of abnormality of the incoming
data is performed on the basis of the network states.
A BAYESIAN CLASSIFICATION ON ASSET VULNERABILITY FOR REAL TIME REDUCTION OF F...IJNSA Journal
IT assets connected on internetwill encounter alien protocols and few parameters of protocol process are exposed as vulnerabilities. Intrusion Detection Systems (IDS) are installed to alerton suspicious traffic or activity. IDS issuesfalse positives alerts, if any behavior construe for partial attack pattern or the IDS lacks environment knowledge. Continuous monitoring of alerts to evolve whether, an alert is false positive or not is a major concern. In this paper we present design of an external module to IDS,to identify false positive alertsbased on anomaly based adaptive learning model. The novel feature of this design is that the system updates behavior profile of assets and environment with adaptive learning process.A mixture model is used for behavior modeling from reference data. The design of the detection and learning process are based on normal behavior and of environment. The anomaly alert identification algorithm isbuiltonSparse Markov Transducers (SMT) based probability.The total process is presented using real-time data. The Experimental results are validated and presentedwith reference to lab environment.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Machine learning-based intrusion detection system for detecting web attacksIAESIJAI
The increasing use of smart devices results in a huge amount of data, which raises concerns about personal data, including health data and financial data. This data circulates on the network and can encounter network traffic at any time. This traffic can either be normal traffic or an intrusion created by hackers with the aim of injecting abnormal traffic into the network. Firewalls and traditional intrusion detection systems detect attacks based on signature patterns. However, this is not sufficient to detect advanced or unknown attacks. To detect different types of unknown attacks, the use of intelligent techniques is essential. In this paper, we analyse some machine learning techniques proposed in recent years. In this study, several classifications were made to detect anomalous behaviour in network traffic. The models were built and evaluated based on the Canadian Institute for Cybersecurity-intrusion detection systems dataset released in 2017 (CIC-IDS-2017), which includes both current and historical attacks. The experiments were conducted using decision tree, random forest, logistic regression, gaussian naïve bayes, adaptive boosting, and their ensemble approach. The models were evaluated using various evaluation metrics such as accuracy, precision, recall, F1-score, false positive rate, receiver operating characteristic curve, and calibration curve.
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.
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
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
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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.
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.
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.
2. Overview
• Introduction
• Challenges of current IDS
• Potential solutions
• Alarm Correlation
• Existing methods of Alarm
Correlation
• Future IDS developments
2
3. Introduction
What is actually Intrusion Detection System
(IDS)?
• A component of computer and network
infrastructure which is aimed at detecting
attacks against computer systems and
networks, or information system.
IDS implementation:
• As a hardware installed on the network
• Or as an agent on an existing piece of
hardware that is connected to the network.
3
4. Introduction
Component of IDS
– Information Collection
– Detection
– Response
Parameters of IDS
– Accuracy
– Performance
– Completeness
4
6. Challenges of IDS
• Runtime limitations
• Specification of detection signatures
• Dependency on environment
High rate of false alarms – the limiting
factor for the performance of an
intrusion detection system
Is fine-tuning effective?
6
7. Potential Solutions
• Data mining
The creation of complex database which is used
to record data related to specific activities.
Through the data generated, a pattern or
model will be developed by knowledge seeker
based on the accumulated data processed by
the algorithm implemented on the front end
application.
• Examples:
– Applying the concept of root cause (“the reason for
which alerts occur”) (Dain and Cunningham 2001)
– Sequential pattern mining and episode rules (Lee and
Stolfo 2000)
7
8. Potential Solutions
• Machine learning technique
Referring to a system capable of the
autonomous acquisition and integration
of knowledge
• Example:
– Alert Classification true positives and
false positive (Pietraszek 2004)
8
9. Potential Solutions
• Co-simulation mechanism (based on
a biological immune mechanism)
(Qiao and Weixin 2002)
– Integrating the misuse detection
technique with the anomaly detection
technique
– Applying a co-stimulation mechanism
Alarm Correlation
9
10. Alert Correlation
“Correlating alarms”: combining the
fragmented information contained in the
alert sequences and interpreting the
whole flow of alerts
Functional requirements:
• Modifying alarms
• Suppressing alarms
• Clearing active alarms
• Generating new alarms
• Delaying alarms
10
11. Existing methods of Alarm Correlation
• Correlating alerts based on the prerequisites
of intrusion providing a high level of
representation of the correlated alerts, and thus
reveals the structure of series of attacks. (Ning et
al. 2001)
• Correlating alerts based on the similarities
between alert features grouping alerts into
scenario depending on the number of matching
attributes from the most general to the most
specific cases. (Debar and Wespi 2001)
11
12. Existing methods of Alarm Correlation
• Alarm correlation based on chronicle
formalism multi-event correlation
component using input IDS alerts (Morin
and Debar 2003)
• Probabilistic approach to alert
correlation providing a mathematical
framework for fusing alerts that match
closely but not perfectly (Valdes and
Skinner 2001)
12
13. Future Development of IDS techniques
Why use Artificial Intelligence?
• Flexibility (vs. threshold definition)
• Adaptability (vs. specific rules)
• Pattern Recognition (and detection of new
patterns)
• Faster computing (faster than human)
• Learning abilities
AI tools:
• Neural Network
• Fuzzy logic
• Others
13
14. Future Development of IDS techniques
Artificial Neural Network
Neural Network identifying the typical
characteristics of system user and statistically
identify significant variations from the user’s
established behaviours.
Type of Neural Network
• Multilayer perceptron
• Self Organising Map
• Radial basis neural networks
• Support Vector Machine
• Other
14
15. Future Development of IDS techniques
Potential implementation of Artificial Intelligence
techniques on alert correlation:
• Multilayer perceptron and Support Vector
Machine
– Probabilistic output of these methods support the causal
relationships of alerts, which is helpful for constructing
attack scenarios (Zhu and Ghorbani 2006)
• Fuzzy Cognitive Modelling
– A causal knowledge based reasoning mechanism with
fuzzy cognitive modelling is used to correlate alerts by
discovering causal relationships in alert data (Siraj and
Vaughn 2005)
15
16. Conclusions
• The problem of high false alarm rate has become
one of the most critical issues faced by IDS
today.
• The future of IDS lies on data correlation
• Alarm correlation mechanism aims at acquiring
intrusion detection alerts and relating them
together to expose a more condensed view of the
security issues.
• Artificial Intelligence plays an important role in
improving the performance of IDS technology.
16
17. Conclusions
Benefit of Artificial Intelligence:
• Flexibility (vs. threshold definition)
• Adaptability (vs. specific rules)
• Pattern Recognition (and detection of
new patterns)
• Faster computing (faster than
human)
• Learning abilities
17
18. References
• Allen, J., Christie, A. et al (2000), ‘State of the Practice of Intrusion
Detection Technologies’, Technical Report CMU/SEI-99-TR-028, Carnegie
Mellon University, available online:
http://www.sei.cmu.edu/publications/documents/99.reports/99tr028/99tr02
8abstract.html, date visited: 22 January 2007
• Lee, W., Stolfo, S.J. (2000), “A Framework for Constructing Features and
Models for Intrusion Detection Systems”, ACM Transactions on
Information and System Security 3(4) pp. 227-261
• Pietraszek, T., Tanner, A. (2005), “Data mining and machine learning -
Towards reducing false positives in intrusion detection”, Information
security technical report [1363-4127] 10(3) pp. 169-183
• Pietraszek, T.: Using Adaptive Alert Classification to Reduce False
Positives inIntrusion Detection. In Jonsson, E., Valdes, A., Almgren, M.,
eds.: RAID ’04:Proc. 7th Symposium on Recent Advances in Intrusion
Detection. Volume 3224 ofLNCS., Springer-Verlag (2004) 102–124
• Julisch, K. (2001), “Mining Alarm Clusters to Improve Alarm Handling
Efficiency”, In:ACSAC ’01: Proc. 17th Annual Computer Security
Applications Conference (AC-SAC), ACM Press pp. 12–21
• Siraj, A., Vaughn, R. (2005), “A Cognitive Model for Alert Correlation in a
Distributed Environment” Lecture Notes in Computer Science vol.
3495/2005, pp. 218-230
18
19. References
• Qiao, Y., Weixin, X.: A Network IDS with Low False Positive Rate. In
Fogel,D.B., El-Sharkawi, M.A., Yao, X., Greenwood, G., Iba, H., Marrow,
P., Shackleton,M., eds.: CEC ’02: Proc. IEEE Congress on Evolutionary
Computation, IEEE Computer Society Press (2002) 1121–1126
• Ning, P., Reeves, D., Cui, Y. (2001) “Correlating Alerts Using
Prerequisites of Intrusions” Technical Report TR-2001-13, North Carolina
State University
• Debar H. and A. Wespi. (2001) “Aggregation and Correlation of Intrusion-
Detection Alerts”, In Proceedings of the 4th International Symposium on
Recent Advances in Intrusion Detection, Davis, CA, USA, pp. 85-103,
October 2001
• Valdes, A. and Skinner, K. (2001), “Probabilistic Alert Correlation”, In
Proceedings of the 4th International Symposium on Recent Advances in
Intrusion Detection, Davis, CA, USA, October, pp. 54-68
• Morin, B., Debar, H., “Correlation of Intrusion Symptoms: an Application of
Chronicles” (2003), Proceedings of the 6th symposium on Recent
Advances in Intrusion Detection (RAID 2003), Carnegie Mellon University,
Pittsburg, PA,. Springer LNCS 2820, pages 94-112.
• Zhu, B., Ghorbani, A. (2006), “Alert Correlation for Extracting Attack
Strategies”, International Journal of Network Security 3(2), pp. 244-258
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