A 1-day short course developed for visiting guests from Tecsup on network forensics, prepared in a day : ]
The requirements/constraints were 5-7 hours of content and that the target audience had very little forensic or networking knowledge. [For that reason, flow analysis was not included as an exercise, discussion of network monitoring solutions was limited, and the focus was on end-node forensics, not networking devices/appliances themselves]
Network Forensics - Your Only Choice at 10GSavvius, Inc
Watch the full OnDemand Webcast: http://bit.ly/networkforensics10G
Network forensics remains one of the hottest topics in network analysis, especially with the exploding deployments of 10 Gigabit (10G) gear. Though often considered for security analysis, especially the identification of network intrusions, network forensics can and should be used for much more general network analysis purposes.
At 10G, real-time network analysis is essentially unmanageable. The only effective way to deal with 10G traffic is to quickly screen incoming data for key network performance indicators and then to store the data for in-depth analysis of small slices of pertinent data as the need arises. Again, this in-depth analysis need not be security oriented – network forensics works equally well in identifying spikes in utilization, drops in VoIP call quality and increased latency, whether network or application. At 10G speeds this isn’t easy to accomplish, but with network forensics you’ll make quick work of it.
In this web seminar, we cover:
- Key technologies used in network forensics
- Applicability of network forensics in analyzing typical network performance issues
- Combining real-time capabilities with network forensics for effective 10G network analysis
What you will learn:
- How to effectively capture and manage 10G traffic for network analysis
- How to use real-time key network performance indicators to identify potential problems
- How to use network forensics to analyze and solve typical network performance issues
Layered Approach for Preprocessing of Data in Intrusion Prevention SystemsEditor IJCATR
Due to extensive growth of the Internet and increasing availability of tools and methods for intruding and attacking
networks, intrusion detection has become a critical component of network security parameters. TCP/IP protocol suite is the defacto
standard for communication on the Internet. The underlying vulnerabilities in the protocols is the root cause of intrusions. Therefor
Intrusion detection system becomes an important element in network security that controls real time data and leads to huge
dimensional problem. Processing large number of packets and data in real time is very difficult and costly. Therefor data preprocessing
is necessary to remove redundant and unwanted information from packets and clean network data. Here, we are focusing on
two important aspects of intrusion detection; one is accuracy and other is performance. The layered approach of TCP/IP model can be
applied to packet pre-processing to achieve early and faster intrusion detection. Motivation for the paper comes from the large impact
data preprocessing has on the accuracy and capability of anomaly-based NIPS. In this paper it is demonstrated that high attack
detection accuracy can be achieved by using layered approach for data preprocessing in Internet. To reduce false positive rate and to
increase efficiency of detection, the paper proposed framework for preprocessing in intrusion prevention system. We experimented
with real time network traffic as well as he KDDcup99 dataset for our research.
A 1-day short course developed for visiting guests from Tecsup on network forensics, prepared in a day : ]
The requirements/constraints were 5-7 hours of content and that the target audience had very little forensic or networking knowledge. [For that reason, flow analysis was not included as an exercise, discussion of network monitoring solutions was limited, and the focus was on end-node forensics, not networking devices/appliances themselves]
Network Forensics - Your Only Choice at 10GSavvius, Inc
Watch the full OnDemand Webcast: http://bit.ly/networkforensics10G
Network forensics remains one of the hottest topics in network analysis, especially with the exploding deployments of 10 Gigabit (10G) gear. Though often considered for security analysis, especially the identification of network intrusions, network forensics can and should be used for much more general network analysis purposes.
At 10G, real-time network analysis is essentially unmanageable. The only effective way to deal with 10G traffic is to quickly screen incoming data for key network performance indicators and then to store the data for in-depth analysis of small slices of pertinent data as the need arises. Again, this in-depth analysis need not be security oriented – network forensics works equally well in identifying spikes in utilization, drops in VoIP call quality and increased latency, whether network or application. At 10G speeds this isn’t easy to accomplish, but with network forensics you’ll make quick work of it.
In this web seminar, we cover:
- Key technologies used in network forensics
- Applicability of network forensics in analyzing typical network performance issues
- Combining real-time capabilities with network forensics for effective 10G network analysis
What you will learn:
- How to effectively capture and manage 10G traffic for network analysis
- How to use real-time key network performance indicators to identify potential problems
- How to use network forensics to analyze and solve typical network performance issues
Layered Approach for Preprocessing of Data in Intrusion Prevention SystemsEditor IJCATR
Due to extensive growth of the Internet and increasing availability of tools and methods for intruding and attacking
networks, intrusion detection has become a critical component of network security parameters. TCP/IP protocol suite is the defacto
standard for communication on the Internet. The underlying vulnerabilities in the protocols is the root cause of intrusions. Therefor
Intrusion detection system becomes an important element in network security that controls real time data and leads to huge
dimensional problem. Processing large number of packets and data in real time is very difficult and costly. Therefor data preprocessing
is necessary to remove redundant and unwanted information from packets and clean network data. Here, we are focusing on
two important aspects of intrusion detection; one is accuracy and other is performance. The layered approach of TCP/IP model can be
applied to packet pre-processing to achieve early and faster intrusion detection. Motivation for the paper comes from the large impact
data preprocessing has on the accuracy and capability of anomaly-based NIPS. In this paper it is demonstrated that high attack
detection accuracy can be achieved by using layered approach for data preprocessing in Internet. To reduce false positive rate and to
increase efficiency of detection, the paper proposed framework for preprocessing in intrusion prevention system. We experimented
with real time network traffic as well as he KDDcup99 dataset for our research.
Matthias Vallentin - Towards Interactive Network Forensics and Incident Respo...boundary_slides
Incident response, post-facto forensics, and network troubleshooting rely on the ability to quickly extract relevant information. To this end, security analysts and network operators need a system that (i) allows for directly expressing a query using domain-specific constructs, (ii) that delivers the performance required for interactive analysis, and (iii) that is not affected by a continuously arriving stream of semi-structured data.
This talk covers the design and implementation plans of a distributed analytics platform that meets these requirements. Well-proven Google architectures like GFS, BigTable, Chubby, and Dremel heavily influenced the design of the system, which leverages bitmap indexes to meet the interactive query requirements. The goal is to develop a prototype ready for production usage in the next few months and obtain feedback from using it on various large-scale sites serving tens of thousands of machines.
EFFICIENT DEFENSE SYSTEM FOR IP SPOOFING IN NETWORKScscpconf
In this age of gigabit Ethernet and broadband internet, network security has been the top
priority for most of the researchers. Technology advancements have advantages as well as
disadvantages. Most of the communication of present world, the e-world, takes place online,
through the internet. Thus the context of network intrusions and attacks to hack into servers also
came into existence. A technique to perform this activity is made possible by preventing the
discovery of the sender’s identity through IP Spoofing [7]. Many popular internet sites have
been hacked and attackers try to forge or spoof the source addresses in IP packets. Using
spoofing detection technique, the user can retrieve the list of IP addresses and able to identify
the malicious IP addresses.Hence mechanisms must be designed to prevent hacking. This paper
proposes a novel technique to detect IP spoofing based on traffic verification and filtering
DDOS ATTACK DETECTION ON INTERNET OF THINGS USING UNSUPERVISED ALGORITHMSijfls
The increase in the deployment of IoT networks has improved productivity of humans and organisations.
However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent
weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS
attack in IoT networks by classifying incoming network packets on the transport layer as either
“Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep
learning algorithms and two clustering algorithms were independently trained for mitigating DDoS
attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and
UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during
the experimentation phase. The accuracy score and normalized-mutual-information score are used to
quantify the classification performance of the four algorithms. Our results show that the autoencoder
performed overall best with the highest accuracy across all the datasets.
Network forensics - Follow the Bad Rabbit down the wirecasheeew
We will take a sneak peak into the rabbit hole of network analysis and forensics.
For this I will show you how the recent ransomware Bad Rabbit hops around the wire. We are going to take a look at
basic procedures and tools that help us follow its traces.
Be prepared to dig your own rabbit hole with the links I will offer at the end and follow them at your own risk (;
Denial of Service attacks – Definitions, related surveys
Traceback of DDoS Attacks – Proposed method, advantages, future work
Detection methods with Shannon and Renyi cross entropy – Previous works, proposed method, dataset and results
The added value of entropy detection methods
References
Pre-filters in-transit malware packets detection in the networkTELKOMNIKA JOURNAL
Conventional malware detection systems cannot detect most of the new malware in the network
without the availability of their signatures. In order to solve this problem, this paper proposes a technique
to detect both metamorphic (mutated malware) and general (non-mutated) malware in the network using a
combination of known malware sub-signature and machine learning classification. This network-based
malware detection is achieved through a middle path for efficient processing of non-malware packets.
The proposed technique has been tested and verified using multiple data sets (metamorphic malware,
non-mutated malware, and UTM real traffic), this technique can detect most of malware packets in
the network-based before they reached the host better than the previous works which detect malware in
host-based. Experimental results showed that the proposed technique can speed up the transmission of
more than 98% normal packets without sending them to the slow path, and more than 97% of malware
packets are detected and dropped in the middle path. Furthermore, more than 75% of metamorphic
malware packets in the test dataset could be detected. The proposed technique is 37 times faster than
existing technique.
Rothke Using Kazaa To Test Your Security PostureBen Rothke
An effective corporate information security policy will completely ban the use of peer-to-peer (P2P) file sharing software,
such as Morpheus and Kazaa.
LATTICE STRUCTURAL ANALYSIS ON SNIFFING TO DENIAL OF SERVICE ATTACKSIJCNCJournal
Sniffing is one of the most prominent causes for most of the attacks in the digitized computing environment. Through various packet analyzers or sniffers available free of cost, the network packets can be captured and analyzed. The sensitive information of the victim like user credentials, passwords, a PIN which is of more considerable interest to the assailants’ can be stolen through sniffers. This is the primary reason for most of the variations of DDoS attacks in the network from a variety of its catalog of attacks. An effective and trusted framework for detecting and preventing these sniffing has greater significance in today’s computing. A counter hack method to avoid data theft is to encrypt sensitive information. This paper provides an analysis of the most prominent sniffing attacks. Moreover, this is one of the most important strides to guarantee system security. Also, a Lattice structure has been derived to prove that sniffing is the prominent activity for DoS or DDoS attacks.
Telepresence success is based on an almost flawless presentation of HD video and Audio. Clearly the video and audio quality are clearly the most relevant metrics. But in relation to understanding the full picture of quality of experience more test parameters need to be included in the test strategy. Evidently Telepresence is not the only service to run on a network, therefore its important to test with varying applications and load conditions.
Matthias Vallentin - Towards Interactive Network Forensics and Incident Respo...boundary_slides
Incident response, post-facto forensics, and network troubleshooting rely on the ability to quickly extract relevant information. To this end, security analysts and network operators need a system that (i) allows for directly expressing a query using domain-specific constructs, (ii) that delivers the performance required for interactive analysis, and (iii) that is not affected by a continuously arriving stream of semi-structured data.
This talk covers the design and implementation plans of a distributed analytics platform that meets these requirements. Well-proven Google architectures like GFS, BigTable, Chubby, and Dremel heavily influenced the design of the system, which leverages bitmap indexes to meet the interactive query requirements. The goal is to develop a prototype ready for production usage in the next few months and obtain feedback from using it on various large-scale sites serving tens of thousands of machines.
EFFICIENT DEFENSE SYSTEM FOR IP SPOOFING IN NETWORKScscpconf
In this age of gigabit Ethernet and broadband internet, network security has been the top
priority for most of the researchers. Technology advancements have advantages as well as
disadvantages. Most of the communication of present world, the e-world, takes place online,
through the internet. Thus the context of network intrusions and attacks to hack into servers also
came into existence. A technique to perform this activity is made possible by preventing the
discovery of the sender’s identity through IP Spoofing [7]. Many popular internet sites have
been hacked and attackers try to forge or spoof the source addresses in IP packets. Using
spoofing detection technique, the user can retrieve the list of IP addresses and able to identify
the malicious IP addresses.Hence mechanisms must be designed to prevent hacking. This paper
proposes a novel technique to detect IP spoofing based on traffic verification and filtering
DDOS ATTACK DETECTION ON INTERNET OF THINGS USING UNSUPERVISED ALGORITHMSijfls
The increase in the deployment of IoT networks has improved productivity of humans and organisations.
However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent
weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS
attack in IoT networks by classifying incoming network packets on the transport layer as either
“Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep
learning algorithms and two clustering algorithms were independently trained for mitigating DDoS
attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and
UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during
the experimentation phase. The accuracy score and normalized-mutual-information score are used to
quantify the classification performance of the four algorithms. Our results show that the autoencoder
performed overall best with the highest accuracy across all the datasets.
Network forensics - Follow the Bad Rabbit down the wirecasheeew
We will take a sneak peak into the rabbit hole of network analysis and forensics.
For this I will show you how the recent ransomware Bad Rabbit hops around the wire. We are going to take a look at
basic procedures and tools that help us follow its traces.
Be prepared to dig your own rabbit hole with the links I will offer at the end and follow them at your own risk (;
Denial of Service attacks – Definitions, related surveys
Traceback of DDoS Attacks – Proposed method, advantages, future work
Detection methods with Shannon and Renyi cross entropy – Previous works, proposed method, dataset and results
The added value of entropy detection methods
References
Pre-filters in-transit malware packets detection in the networkTELKOMNIKA JOURNAL
Conventional malware detection systems cannot detect most of the new malware in the network
without the availability of their signatures. In order to solve this problem, this paper proposes a technique
to detect both metamorphic (mutated malware) and general (non-mutated) malware in the network using a
combination of known malware sub-signature and machine learning classification. This network-based
malware detection is achieved through a middle path for efficient processing of non-malware packets.
The proposed technique has been tested and verified using multiple data sets (metamorphic malware,
non-mutated malware, and UTM real traffic), this technique can detect most of malware packets in
the network-based before they reached the host better than the previous works which detect malware in
host-based. Experimental results showed that the proposed technique can speed up the transmission of
more than 98% normal packets without sending them to the slow path, and more than 97% of malware
packets are detected and dropped in the middle path. Furthermore, more than 75% of metamorphic
malware packets in the test dataset could be detected. The proposed technique is 37 times faster than
existing technique.
Rothke Using Kazaa To Test Your Security PostureBen Rothke
An effective corporate information security policy will completely ban the use of peer-to-peer (P2P) file sharing software,
such as Morpheus and Kazaa.
LATTICE STRUCTURAL ANALYSIS ON SNIFFING TO DENIAL OF SERVICE ATTACKSIJCNCJournal
Sniffing is one of the most prominent causes for most of the attacks in the digitized computing environment. Through various packet analyzers or sniffers available free of cost, the network packets can be captured and analyzed. The sensitive information of the victim like user credentials, passwords, a PIN which is of more considerable interest to the assailants’ can be stolen through sniffers. This is the primary reason for most of the variations of DDoS attacks in the network from a variety of its catalog of attacks. An effective and trusted framework for detecting and preventing these sniffing has greater significance in today’s computing. A counter hack method to avoid data theft is to encrypt sensitive information. This paper provides an analysis of the most prominent sniffing attacks. Moreover, this is one of the most important strides to guarantee system security. Also, a Lattice structure has been derived to prove that sniffing is the prominent activity for DoS or DDoS attacks.
Telepresence success is based on an almost flawless presentation of HD video and Audio. Clearly the video and audio quality are clearly the most relevant metrics. But in relation to understanding the full picture of quality of experience more test parameters need to be included in the test strategy. Evidently Telepresence is not the only service to run on a network, therefore its important to test with varying applications and load conditions.
Powerpoint berkaitan nilai:
Cinta akan negara
Taat setia kepada raja dan negara
Sanggup berkorban untuk negara
#Merangkumi buku teks/isu semasa terbaru/KBKK#
Using Network Security and Identity Management to Empower CISOs Today: The Ca...ForgeRock
A General Session Presentation by Scott Stevens, VP of Technology-WW Systems Engineering at Palo Alto Networks, and Allan Foster, VP Technology & Standards, Office of the CTO at ForgeRock at the 2014 IRM Summit in Phoenix, Arizona.
Security Delivery Platform: Best practicesMihajlo Prerad
Security Delivery Platform: Best practices
The traditional Security model was one that operated under simple assumptions. Those assumptions led to deployment models which in todays’ world of cyber security have been proven to be quite vulnerable and inadequate to growing amount and diversity of threats.
A Security Delivery Platform addresses the above considerations and provides a powerful solution for deploying a diverse set of security solutions, as well as scaling each security solution beyond traditional deployments. Such platform delivers visibility into the lateral movement of malware, accelerate the detection of ex-filtration activity, and could significantly reduce the overhead, complexity and costs associated with such security deployments.
In today’s world of industrialized and well-organized cyber threats, it is no longer sufficient to focus on the security applications exclusively. Focusing on how those solutions get deployed together and how they get consistent access to relevant data is a critical piece of the solution. A Security Delivery Platform in this sense is a foundational building block of any cyber security strategy.
Using Your Network as a Sensor for Enhanced Visibility and Security Lancope, Inc.
Driven by the mobility, cloud computing, and Internet of Everything megatrends and fueled by increasingly sophisticated cybercriminals, today’s information landscape is more dynamic and more vulnerable than ever before.
Join Cisco and Lancope for a complimentary webinar to learn how you can implement a comprehensive, network-enabled approach to cybersecurity.
During the webinar we will discuss:
Using the Network as a Security Sensor with Lancope’s StealthWatch System and Flexible NetFlow and to obtain visibility at scale, monitor network activity efficiently, discover security incidents quickly, and help achieve compliance.
Using the Network as a Security Enforcer with Cisco TrustSec to ensure policy-based access control and network segmentation for containment of the network attacks, assist compliance and reduce risks of data-breaches.
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14. Thank You! Award Winning Test & Monitoring Solutions Industry Associations