Научная статья на тему 'Lan abnormalities threat detection: an outlook and applicability analysis'

Lan abnormalities threat detection: an outlook and applicability analysis Текст научной статьи по специальности «Компьютерные и информационные науки»

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Ключевые слова
SECURITY / NETWORKS / LAN / THREAT DETECTION / CISCO / NEURAL NETWORKS / SIGNATURE / FIREWALLING / TRAFFIC DUMP

Аннотация научной статьи по компьютерным и информационным наукам, автор научной работы — Modorskiy Alexey, Minzov Anatoliy, Baronov Oleg, Nevskiy Alexander

In this article contemporary LAN threats are considered in scope of signatures, where a pile of polymorphic elements, not applicable for diverse analysis by itself are combining along with preambles, traffic strains and flows, as well as protocols and ports, could be reviewed to tell if there are threats detected. The point is to compile a standalone system, capable of scoping and triaging diversified elements on a LAN Core, giving a system owner an opportunity to early detect, prioritize and workaround threat that standard security systems allow by default. However, it is wrong to consider such mechanism a classic signature-based, where traffic dump is investigated for known issues. Conversely, the system in scope should act proactive, which require shaping of basic, likewise ideal, traffic flow, seeking for abnormalities in an early threat occurrence. For this occasion, the neural network should step in, utilizing the vector comparison for the abnormalities detection process being effective.

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Текст научной работы на тему «Lan abnormalities threat detection: an outlook and applicability analysis»

I LAN ABNORMALITIES THREAT DETECTION: AN OUTLOOK AND APPLICABILITY ANALYSIS

A.M.Modorskly1, A.S.Minzov2, O.R.Baronov3, A.Y.Nevskiy4.

In this article contemporary LAN threats are considered in scope of signatures, where a pile of polymorphic elements, not applicable for diverse analysis by itself are combining along with preambles, traffic strains and flows, as well as protocols and ports, could be reviewed to tell if there are threats detected. The point is to compile a standalone system, capable of scoping and triaging diversified elements on a LAN Core, giving a system owner an opportunity to early detect, prioritize and workaround threat that standard security systems allow by default. However, it is wrong to consider such mechanism a classic signature-based, where traffic dump is investigated for known issues. Conversely, the system in scope should act proactive, which require shaping of basic, likewise ideal, traffic flow, seeking for abnormalities in an early threat occurrence. For this occasion, the neural network should step in, utilizing the vector comparison for the abnormalities detection process being effective.

Keywords: Security, Networks, LAN, Threat Detection, Cisco, Neural Networks, Signature, Firewalling, Traffic dump.

DOI: 10.21681/2311-3456-2018-1-11-18

Introduction

A contemporary world of Data Security could seemingly have a whole set of defensive mechanisms: depends on a current need we have firewalls, IDS/IPS, antiviruses, integrated security solutions etc. However, could we suppose this set complete? As Cisco CEO John Chambers recently said: «There are two types of companies: those that have been hacked and those who don't know they have been hacked». Couldn't agree with him more; despite constant improvement in data security field, despite a bunch on new technologies, all the researches and product range we still suffer severe attacks and, as a consequence, casualties.

Why is this topic a thing? Well, the answer is simple, yet catchy. IT world has no current systems capable of acting fully proactive. The closest to this we have is a signature-based defense systems. The point is once each signature met, the prevention mechanism is triggered, so a full attack is mitigated (or neglected) at the very beginning acting so-called «proactive». In fact, this is not 100% true. The signature is yet to be found, established, processed and spread until it became functional. Suppose we have no defense against not-well-known threats, making a zero-day and system vulnerabilities a thing we may only overcome once recorded and studied. In fact, if a hacker (intruder, attacker) is first to find a weak point, we may only pray our system is sufficiently protected

on access level otherwise attack is predefined successful.

What can we do with this state? Obviously, we need a system to have a fully proactive mechanism, meaning, if we are focusing on a network POV and LAN specifically, some sort of traffic flow control, which allowing us to detect any abnormalities, thus decreasing both 1st and 2nd type errors.

Chapter 1. Abnormalities thread detection method overview

Leading to contemporary world techniques and defense systems, signature threat detection systems are not a newbie to a data security. The most common solution closest to considerate topic is IDS/IPS class systems. IDSs are devices that in promiscuous mode detect malicious activity within the network. IPS devices are capable of detecting all these security threats; however, they are also able to drop noncompliant packets inline. Traditionally, IDS systems have provided excellent application layer attack-detection capabilities; however, they were not able to protect against day-zero attacks using valid packets. The problem is that most attacks today use valid packets. On the other hand, now IPS systems such as the Cisco IPS software Version 6.x and later offer anomaly-based capabilities that help you detect such attacks. This is a big advantage, since it makes the IPS devices less dependent on signature updates for protection against DDoS, worms, and

1 Alexey Modorskiy, Master degree student Information and Economic Security Institute,, CCNA, CCNA Instructor, National research University «MPEI», Moscow, Russia. E-mail: alexeymodorskiy@gmail.com

2 Anatoliy Minzov, Dr.Sc. Professor, National research University «MPEI», Moscow, Russia. E-mail: aminzov@mpei.ru

3 Oleg Baronov, Associate Professor, Ph.D., National research University «MPEI», Moscow, Russia. E-mail: baronovor@mpei.ru

4 Alexander Nevskiy, Associate Professor, Ph.D., National research University «MPEI», Moscow, Russia. E-mail: nevskyay@mpei.ru

m

CS-MARS

Figure 1-2. IDS/IPS solution based on MARS example

any day-zero threats. Just like any other anomaly detection systems, the sensors need to learn what is «normal.» In other words, they need to create a baseline of legitimate behavior [1].

Looks a rather close to a topic being reviewed, isn't it? The basics are solid milestone: everything we need to protect our network undercover a convenient and reliable vendor. Let's dig a little deeper into a underlying mechanism. Turns out, as the most of IDS/IPS, Cisco utilizes a monitoring mechanism -Netflow - as shown on Figure 1. Other vendors often rely on vendor-independent technologies, yet

the outcome is still: monitoring sys tem is a key to work on anomalies.

The figures above only gives us a stratified and simplified look to the technology, which is, of course, more complicated and advance. Allow us to have a look at Neflow: Each packet that is forwarded within a router or switch is examined for a set of IP packet attributes. These attributes are the IP packet identity or fingerprint of the packet and determine if the packet is unique or similar to other packets, as presented on Figure 3.

Usage

Tim© of Day

Port Utilization

QoS

• Packet Count • Byte Count - Source IP Address - Destination IP Address

• Start sysUpTime •End sysUpTime • Source TCP/UDP Port • Destination TCP/UDP Port

- Input iflndex - Output iflndex - Next Hop Address * Source AS Number • Dest. AS Number

- Source Prefix Mask

- Type of Service • TCP Flags - Protocol * Dest. Prefix Mask

From/To

Application

Routing

and Peering

Figure 3. Cisco NetFlow v5 network utilization data report example [3]

BasicRouterf BasicRouterf BasicRouter#sh arp Protocol Address

Internet Internet Internet Internet Internet Internet BasicRouterf

10.0.0.5 10.0.0.6 10.0.0.9 10.0.0.10 10.0.0.13 10.0.0.14

Age (min) 3

Hardware Addr Type Interface

000A. .4148 . A501 ARPA GigabitEthernet0/0

00D0. . FFC7 .3401 ARPA GigabitEthernet0/0

00D0. . FFC7 .3403 ARPA GigabitEthernet0/2

0003. . E45E .7701 ARPA GigabitEthernet0/2

00D0. . FFC7 .3402 ARPA GigabitEthernetO/l

0001. . 64DB . 5A01 ARPA GigabitEthernetO/l

Figure 4. ARP table overview, as issued on Cisco Router Series 2900

Traditionally, an IP Flow is based on a set of 5 and up to 7 IP packet attributes.

IP Packet attributes used by NetFlow:

• IP source address;

• IP destination address;

• Source port;

• Destination port;

• Layer 3 protocol type;

• Class of Service;

• Router or switch interface [2].

Away with the Cisco technologies, the rest of network-based IDS/IPS systems are quite alike, for example, Juniper SRX utilizes PCAP Syslog along with Juniper Secure Analytics (JSA) appliance, which basically is the same filtering solution [4]. Same may be found under Checkpoint, PaloAlto and other market leading vendor solutions.

Is this really enough? Well, to find out we need again to dive into the underlying principle, which is an OSI packet flow relation and processing ability.

Turns out that mentioned traffic flow control protocols are strictly limited to routing and transport layers of OSI, since classic routers, as well as firewalls or gateways are operating at four below OSI layers (it's often misunderstood those devices are only operating at layers 3-4, however it's obviously has a physical layer, and those devices does have a Layer 2 operational units since ARP tables are exists and default routing contains MAC-address changes from one routing device to another - otherwise neighbours could not know each other), as shown on Figure 4 and Table 1.

As we can see, such IDS/IPS solutions are limited by design, since they utilize embedded, built-in routing mechanism. The advantages of these solutions are considerable: significant decrease of additional load on active net device, standard architecture and configuration, optimization, topology-independency etc.

However there is certainly the flip side of a coin. For the default router, as well as classic firewall, each OSI layer above 4th is just a payload with no considerate

Table 1. OSI standard representation as presented in LAN

Layer Protocol data unit (PDU) Functions

Host layers 7. Application Data payload Application internal information, often described as payload.

6. Presentation Translation of data from application to network format and vise versa

5. Session Session control between two endpoints including time sync

4. Transport Segment (for TCP) or Datagram (for UDP) Reliability control between endpoints, segmentation and fragmentation control.

Media layers 3. Network Packets Routing between either endpoints or subnets using routing protocols

2. Datalink Frames Switching between two or more endpoints (connected to the same switching infrastructure) and reliability control

1. Physical Bits Transmission of electrical signals

indexes/preambles etc. Hence, it couldn't be investigated for further decision-making process and is not considerate useful for mentioned IDS/IPS solutions.

If we need a solution to scope a whole packet into investigation and filtering, we obviously have to have a Layer 7 device, either a same called firewall or server, capable of running appropriate software and equipped with a set of required hardware/firmware. This is not a new solution to a market, those devices are called host-based IDS/IPS and they utilize a variety of advantages:

• Full 7 layer OSI coverage;

• Any IP-demanded filtering;

• Flexibility of use.

As far as we have advantages, disadvantages are also in place:

• Dedicated environment demand;

• Low speed of filtering;

• Network traffic is not counted for host-based solutions;

• Costs etc.

Those solutions are extremely protective yet expensive, hard to tune and support. Host-based IDS often require a small-cell diversion of LAN, since are only capable of carrying application traffic parameters [5]. Layer 7 Firewalls (as well as Multilayer Firewalls) are way more advanced, yet extremely costly, and often work as a transparent devices (it is recommended to implement transparent firewall mode on a network, if firewall is implemented along router [6]), which means they are routing-insensitive.

As a result, there is currently no end-to-end solution to cover a whole scope of LAN network security. Since valid packet threats are a thing we cannot only rely on integrated network IDS/IPS [7], while filtering is a target of networking devices. A complete solution is a more like a compilation of Host-based IDS, Network-based and a Multilayer (7th layer) Firewall. This is a compilation of disadvantages as well: a poor performance, costs and a rather challenging support.

Chapter 2. How to perform

Clearly, the solution to original agenda should aggregate a whole scope of technologies to perform

a complete investigation of traffic. Yet the most efficient way is to have an ideal dump, therefore having the non-standard traffic analyzed separately. There are multiple advantages to this solution: we do not need to have a filtering device working 24/7, inspecting a whole flow of payload, but enabling specifically at the time anomaly detected, improving performance and boosting the routing; the analysis itself becomes more efficient due to a considerate decrease on a data marked to investigate. As a consequence, a LAN may miss a whole set of infrastructure dedicated to act IDS-alike, while bearing specific device, say, a server, performing on-demand with a few recourses allocated at the time.

This raises a couple of reasonable questions:

1. How do we have a normal traffic dump idea?

2. How to analyze an ideal dump for abnormalities?

3. Where to have analyzing equipment installed?

The first question is basically a matter of modeling.

Since only a LAN traffic is in scope, we are able to decrease the area to the data being send and received between local resources. Nevertheless, the problem of modeling this traffic flow is a thing. To solve this task it is better to have original traffic decomposed to several components easier to analyze [8-9].

Firstly, a service traffic - data, required by network devices to communicate between each other and function around dedicated mechanisms (e.g. routing, fail-proof, redundancy etc.) [10]. A formalizing of this traffic could be done by simply listing a used technologies or sniffing traffic in a «silent mode», where no payload is neither sent nor received. It is worth saying such test should be done on an isolated LAN where no suspicious traffic is presented, and there is only one way to have it done: on the network cut-over, when LAN is initially disconnected from unprotected environments such as Internet or adjacent LANs.

Having a listed scope of service data circling on a network we may proceed to determining user traffic and at this point we may need to divide payload flow from servicing traffic to have a clear representation of ideal (or normal) dump. How can we have this done? Modeling is the best way to perform in this case.

Figure 5. Iperf relations schema

Table 2. Iperf general options

GENERAL OPTIONS

Command line option Description

-p, --port n The server port for the server to listen on and the client to connect to. This should be the same in both client and server. Default is 5201.

--cport n Option to specify the client-side port. (new in iPerf 3.1)

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-f, --format [kmKM] A letter specifying the format to print bandwidth numbers in. Supported formats are <k> = Kbits/sec <K> = KBytes/sec <m> = Mbits/sec <M> = MBytes/sec The adaptive formats choose between kilo- and mega- as appropriate.

-i, --interval n Sets the interval time in seconds between periodic bandwidth, jitter, and loss reports. If nonzero, a report is made every interval seconds of the bandwidth since the last report. If zero, no periodic reports are printed. Default is zero.

-F, --file name client-side: read from the file and write to the network, instead of using random data; server-side: read from the network and write to the file, instead of throwing the data away.

-A, --affinity n/n,m-F Set the CPU affinity, if possible (Linux and FreeBSD only). On both the client and server you can set the local affinity by using the n form of this argument (where n is a CPU number). In addition, on the client side you can override the server's affinity for just that one test, using the n,m form of argument. Note that when using this feature, a process will only be bound to a single CPU (as opposed to a set containing potentialy multiple CPUs).

-B, --bind host Bind to host, one of this machine's addresses. For the client this sets the outbound interface. For a server this sets the incoming interface. This is only useful on multihomed hosts, which have multiple network interfaces.

-V, --verbose give more detailed output

-J, --json output in JSON format

--logfile file send output to a log file. (new in iPerf 3.1)

--d, --debug emit debugging output. Primarily (perhaps exclusively) of use to developers.

-v, --version Show version information and quit.

-h, --help Show a help synopsis and quit.

First of all, we need to exclude service traffic, and there's only one way to do it with 100% efficiency: getting rid of network device, stratifying a host-server relations. There are several techniques to do it, let's consider the simplest: a traffic generator [11].

In this example we will use iperf traffic generator as a simple, free-based software available online.

For the correct usage we will need to consider a following simple topology presented below on Figure 5: The point is to have a both way relations required to establish a model which is maximum close to a real one, excluding any service-related flow. Syntax is clear and easy to use (refer to Table 2).

Having this utility settled and tune we may proceed to collecting a dump of ideal, or normal, traffic. For this matter we may use either iperf embedded output or Wireshark as a sniffer. To use this application we would need to adjust and convert original topology (Figure 6):

This application allows engineer to sniff packets flowing through a networking interfaces while not interrupting the flow itself [12]. Clearly, to built alike topology we will need to have a machine with at least 2 NW cards installed. Original user interface of Wireshark is rather clear and straight-through, which makes sniffing easy. Outcome of sniffing process of-

Iperf PC Client Wireshark Iperf

sniffer machine Server

Figure 6. Iperf realtions schema with sniffer integrated

> Frame 1140: 556 bytes on wire (4448 bits), 556 bytes captured (4448 bits) on interface 0 Ethernet II, Src: HewlettP_ed:27:c5 (fc:15:b4:ed:27:c5), Dst: IPv6mcast_0c (33:33:00:00:00:0c)

* Internet Protocol Version 6, Src: fe80::31c8:b49:2dda:bef0, Dst: ff02::c 0110 ____ = Version: 6

t>____ 0000 0000 ....................= Traffic class: 0x00 (DSCP: CS0, ECN: Not-ЕСТ)

............ 0000 0000 0000 0000 0000 = Flowlabel: 0x00000000

Payload length: 502 Next header: UDP (17) Hop limit: 1

Source : fe80: :31c8:b49:2dda:bef0 Destination: ff02::c [Source GeoIP: Unknown] [Destination GeoIP: Unknown]

^ User Datagram Protocol, Src Port: 1900 (1900), Dst Port: 1900 (1900) Source Port: 1900 Destination Port: 1900 Length: 502 [> Checksum: 0x2973 [validation disabled] [Stream index: 1]

> hypertext Transfer Protocol

Figure 7. Example of Wireshark sniffer output

ten presented as a combinations of packets tied by some of crucial preambles and service markers, as shown on Figure 7 above.

A compilation of iperf and Wireshark is a reasonable way to have a payload traffic flow modeled, since an output is a scalable model considering input data and case-sensitive, yet not the only.

For the SOHO and middle Enterprise LAN we may also find heuristic analysis as a suitable form of modeling [13]. For example, a secure environments requiring DMZ are built based on a compilation of ACLs following a traffic audit or predictions [14].

Let's now switch to the second stated question -how to analyze an ideal dump for abnormalities. This is the main point of research since there are currently no systems capable of determining deviations from a clean dump. The modern systems are working vise versa, looking for known signatures on unknown flow.

This brings us to the point where we need to develop such system, yet we require it to be based on standard solutions for the sake of stability and readiness to go for production. As a first step, we need to compile and represent each packet properties to the form appropriate for further analysis. To simplify the input data, we may create a table of listed packet properties, marking each property as «1» if included or «-1» otherwise (bi-polar standard representation [15]). This brings every packet to following view as presented on Table 3.

Table З. Binary algebra issued to a packet

properties index

VLAN flag 1

QoS tag -1

TCP identifier 1

UDP identifier -1

The main issue following this schema is a non-binary properties such as IP addresses and alike parameters. It is supposed we still may formalize such cases using a number of variables from the left pane.

How to fulfill such table? Well, the easiest way is to combine a database table, having those variables sent via simple procedure: a Wireshark converts variables to .CSV format, while a script (supposing we are running WIN workstation or server) inputs it to database:

CREATE TABLE packet_prop ( VLAN DECIMAL(10,2) NULL, QoS DECIMAL(10,2) NULL, TCP DECIMAL(10,2) NULL, UDP DECIMAL(10,2) NULL, PRIMARY KEY (id)

);

LOAD DATA INFILE <c:/tmp/current_packet.csv>

INTO TABLE discounts

FIELDS TERMINATED BY <,>

ENCLOSED BY <«>

LINES TERMINATED BY <\n>

IGNORE 1 ROWS;

Above is represented only a simplest case, while an adequate datastore should include a variety of parameters depends on a network in scope.

Having a main table fulfilled with first packet we may switch over to analysis. Obviously, as valid traffic attacks are already mentioned, we would need more than one sample to compile a full ideal traffic dump. Its size may vary depending on an environment and audit performed. At this moment, we need to decide what component may we use to analyze the dump while comparing it to suspicious traffic.

Let's get input parameters together: having an ideal traffic dump in place we would need to compare any traffic marked as «unusual» to the gold probe, hence desired analyzing tool should have an embedded mechanism to compare every each of current traffic dump to a packet (or a group of packets) from the original dump. There we can use a neu-

ralnetwork adhesive to original issue. Basically we need c|uite thu same mechanism ypsfp^ied on ATM to check whietlcnr e cush tsill is recogeized ccoirrec^tly and ¿avei.aktlss to use within certain en vironment Th ii/ is a HofsfieM neural neUwonk, or, t(s be more specific, a Little's neural network us s derivative to original one.

The point of cising such mathood is Wiavirrg a com-ilete net ol |rrput pparismi^l:^!,, as it is: a database tayle described enrlier coulcJI coompj^lei a summery on ideal pirot^eie recuited for Hopfielci netvt/ork. to fsropffrlyy fia nution, ai preseut ed rut: (1-S):

rrr m tey. ^ ...isJCii]; (1)

1 ^^Jd. ... y (22 )

3 = [z j , , • •• , z n]; (3 )

s = [s(, s2, ..., ] (4)

Where ZS>X2,Zr3 - pac kets b e i n g or i g i nali y ca p -tured art the modeliny etage, and s represen-s a packet (u group oZ |s^ce<^ts) under consideration.

Now we may compile a matrix nsing standard Litt i e's n etwo r k p r i n s i p l e :

sr1 = lUCdS ut ; (5)

Where W Is tuhrr matrix and 'S .s a coueter for current vector bCng used1 i^or the vector it ¡s easien to use rt to avoid rtrir-ii^a,^ ireai violotion, smce, in fact, we need ro corrt|pace each parameter withi itts owv baselinei

A comparison happen s aa^ eech vector mackaV cuspicious ¡s mult iplien l'ecpu^i^ltcitdi y wit h a whole set of baseline vectocs< wh¡oh ouC|sufis ae eitCer inverted packet originai nacket iOfuetn-i ideai scope, dependr on which packet's variation being considered by neuial netwomk) ooit a anmplete unknown vector, which may pioint out an m om aly.

Tie suggested solution is a new directron to Data Security, yet it has some lookalikes when compared to CM systems. However, it does dIffen starOng ^i'oiui u nci concept: unlike CIVi, It works with a specif-

ic modeled ideal dump ría■t||^elr tls^n assuming somu

point where traffic is clean while comparing it to any given moment. This allows system administrator to collate a suspicious object to an ideal data stamp, not specific state of LAN traffic flow defined formerly. It is also conce n trates on a network components rather than standard server-cliech infrastructure.

Conclusion. Implementation and future development.

Having estahlished an environment and a set of inhtrumentals we, however, d oes not getting a complete product. To make it usaPle and user-friendly wh stillstrive to have a GUI, sufficient modeling const ru cto r, ft lug -a nd-play d atabase and so on.

As a res u l t to this rese a rch we offered a new solution , wh ich, if cor rectly comp iled and implemented, m ay cons i d e ra b ly i n crease overall LAN security wh i l e sh aring existing i nfrastructure and cutting the costs . A so l uti o n offe red com bines both Network-and Host-based IDS/.PS princi p les, while having a case -sensitive reaction system at the same time. The key adva ntages to issno. solution may vary depend-in- o n a LAN i nfra, s co p i n g data processing speed, hel--l earning mechanism, simplicity, and, at the end, t h is is a b ra n d n ew s oi u tion completely unknown to mod ern hacking c ommunity.

However th e re a re still tome blind spots to this topic: the sytt-m is ¡ncomfhlete and for this moment is databan e-dep ende nt.Th is mea ns a neural network should be used leased on a DB table, using a built-in multiplication, tr-ns-ose and summation meth-ads. Those .actors may impact performance and setf-learning -bilities, decreasing the LAN throughput jf fjtt^che-ci to Core laoer.

It is predictably more efficient to use programming:! languages for neural network platform while having a joint with DB, so kuture development al-teady has some areas to evolve. Nevertheless, main components should seat still: a neural network, sniffer, modeling tool and database have to be installed for proper Puhctioning. The rest may vary depending oh a certain cases and preferences.

Rev/ewer: V.L. Tsirloe, Ph.D., Associate Professor, information Security Department, Bauman Moscow State Technical University, Moscow, Russia. E-mail: v.tsirlov@bmstu.ru

References

1. End-to-End Network Security: Defense-in-Depth By Omar Santos. Published Aug 24, 2007 by Cisco Press, pp. 19-22

2. Introduction to Cisco IOS NetFlow - A Technical Overview, Cisco White papers, issued May 29, 2012. [https://www.cisco.com/cXen/ us/products/collateral/ios-nx-os-software/ios-netflow/prod_white_paper0900aecd80406232.html]

3. NetFlow gives Network Managers a Detailed View of Application Flows on the Network, Cisco® IT Case Study/Cisco Network Management/NetFlow, Case study, issued © 2004 Cisco Systems, Inc,[http://www.cisco.com/en/US/prod/collateral/iosswrel/ ps6537/ps6555/ps6601/prod_case_study0900aecd80311fc2.pdf], p. 4.

4. Junos® OS IDP Series Appliance to SRX Series Services Gateway Migration Guide. Copyright © 2017 Juniper Networks, Inc, pp. 3-7.

5. Host-based vs Network-base Intrusion detection systems, SANS institute article, [https://cyber-defense.sans.org/resources/ papers/gsec/host-vs-network-based-intrusion-detection-systems-102574], 2005, pp.3-5

6. CLI Book 1: Cisco ASA Series General Operations CLI Configuration Guide, 9.4, Cisco inc. Issued Dec 4, 2017.

7. K. Scarfone, P. Mell, Special Publication 800-94: Guide to Intrusion Detection and Prevention Systems (IDPS), National Institute of Standards and Technology (NIST) (2007), pp 4-6.

8. Two-layer modeling for local area networks Authors: M. Murata, Comput. Center, Osaka Univ.,Japan, H. Takagi, pp 1-10

9. Modeling and Analysis of Wireless LAN Traffic, JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 25, 1783-1801 (20090, DASHDORJ YAMKHIN AND YOUJIP WON+, Hanyang University, Seoul, 133-791 Korea

10. Enhancing LAN Performance, Gilbert Held, Fourth Edition, CRC Press, 18 March, 2004 , pp 130-140 ISBN 978-0-203-49605-3

11. A Network Traffic Generator Model for Fast Network-on-Chip Simulation, IEEE Article, Shankar Mahadevan, Federico Angiolini, Michael Storgaard, Rasmus Grendahl, Olsen Jens Spars0, Jan Madsen, Informatics and Mathematical Modelling (IMM), Technical University of Denmark (DTU), Richard Petersens Plads, 2800 Lyngby, Denmark - Dipartimento di Elettronica, Informatica e Sistemistica (DEIS), University of Bologna, Viale Risorgimento, 2 40136 Bologna, Italy. Proceedings of the Design, Automation and Test in Europe Conference and Exhibition (DATE'05), 1530-1591/05 IEEE, pp 1-6.

12. Wireshark - The best open source network packet analyzer(Part I), Himanshuz.chd | Sep 23 2012, IBM DeveloperWorks Electronic Archive.

13. Traffic inspection for visibility, control and new business opportunities, Ericsson White papers, 284 23-3112 Uen Rev B | September 2010, pp 2-12.

14. The Science DMZ: A Network Design Pattern for Data-Intensive Science, Eli Dart, Lauren Rotman, Brian Tierney, Mary Hester Jason Zurawski, Lawrence Berkeley National Laboratory under Contract No. DE-AC02-05CH11231 with the U.S. Department of Energy. The U.S. SC13 November 17-21, 2013, Denver, CO, USA Copyright 2013 ACM 978-1-4503-2378-9/13/11, page 5

15. Mathematical morphology on bipolar fuzzy sets: general algebraic framework, IsabelleBloch Télécom ParisTech, CNRS LTCI, Paris, France, May, 2012, pp 2-3

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