This report is a result of master thesis in network forensics at Halmstad University during spring term 2018. Industrial engineers are becoming aware of the importance of network security. In today's industrial system, attacks on industrial control system are becoming more commonplace. The availability of industrial specific search engine which can reveal system to anyone interested, has made it easier to target vulnerable systems. Years ago, the networks that are not connected to a public network were considered "Safe". Today these networks are inter-connected, and the challenge is how to make them secure. To protect industrial control systems, monitoring of the industrial network is required to find abnormal activities. There are many open source intrusion detection systems available we have chosen SNORT for our project work since SNORT is a powerful open source intrusion detection system and has many default sets of rules also communitybased rules can be implemented. SNORT has features such as real-time traffic analysis, logging packets and content searching ability. SNORT has limited capability in understanding the PROFINET protocol and the aim of our project is to modify SNORT application to read PROFINET packets so that it can be used in industrial networks running on PROFINET protocol and create rules for PROFINET by examining the data captured from the lab environment.

The detection and intrusion prevention systems could be realized as independent hardware or set in the software form on to the host. The primary purpose of these protective elements is the undesirable activity detection such as integrity intrusion of the files, invalid attempts while connecting to the remote service or acquisition of the local network data. The systems react to the event on the basis of the action that is defined by internal rules. We can include the caution sending or communication blocking among possible counteractions. The base principals of the detection and intrusion prevention systems are described in the dissertation. Various types of captured data analyses and processes of the inhere rules creation and further more caution formats are mentioned in the dissertation. There are also considered the alternatives of their location including advantages of selected situations. There is described the installation and setting up of particular elements of the realized network and security systems. In order to the verification of functionality and factor of the protection providing there was realized several selected types of attacks.

Natural Language machine learning methods are applied to rules generated to identify malware at the network level. These rules use a computer-based signature specification "language" called Snort. Using Natural Language processing techniques and other machine learning methods, new rules are generated based on a training set of existing Snort rule signatures for a specific type of malware family. The performance is then measured, in terms of the detection of existing types of malware and the number of "false positive" triggering events.

Konstant uppkoppling till internet idag är en självklarhet för många världen över. Internet bidrar till en global förbindelse som aldrig tidigare varit möjligt, vilken kan tyckas vara underbart i många avseenden. Dessvärre kan denna digitala förbindelse missbrukas och användas för ondsinta ändamål vilket har lett till behov av säkerhetslösningar som bland annat nätverks-intrångsdetektionssystem. Ett av de mest omtalade verktygen som är ett exempel på ett sådant system är Snort som studeras i denna studie. Utöver analysering av Snort, evalueras även olika signaturdatabasers detektionsförmåga av angrepp. Totalt exekverades 1143 angrepp från 2008-2019 och dessa utvärderades av tre Snort-versioner daterade 2012, 2016 och 2018. Varje Snort-version analyserade angreppen med 18 signaturdatabaser daterade 2011-2019 från tre olika utgivare. Resultaten visar att det stor skillnad mellan de olika utgivarnas signaturdatabaser där den bästa detekterade runt 70% av angreppen medan den sämsta endast detekterade runt 1%. Även hur Snort konfigurerades hade stor inverkan på resultatet där Snort med för-processorn detekterade omkring 15% fler angrepp än utan den. / For many people all over the world being constantly connected to the Internet is taken for granted. The Internet connects people globally in a way that has never been possible before, which in many ways is a fantastic thing. Unfortunately, this global connection can be abused for malicious purposes which have led to the need for security solutions such as network intrusion detection systems. One prominent example of such a system is Snort which is the subject of evaluation in this thesis. This study investigates the ability of signature databases for Snort to detect cyberattacks. In total, we executed 1143 attacks released between 2008-2019 and recorded the network traffic. We then analyzed the network traffic using three versions of Snort released 2012, 2016, and 2018. For each version, we used 18 different signature databases dated 2011-2019 from three different publishers. Our results show that there are a significant difference between the different publishers’ signature databases, where the best signature database detected around 70% of the attacks and the worst only detected around 1%. The configuration of Snort also had a significant impact on the results, where Snort with the pre-processor detected about 15% more attacks than without it.

Network security has become a vital part for computer networks to ensure that they operate as expected. With many of today's services relying on networks it is of great importance that the usage of networks are not being compromised. One way to increase the security of a computer network is to implement a Network Intrusion Detection System (NIDS). This system monitors the traffic sent to, from and within the network. This study investigates how a NIDS called SNORT with different configurations handles common network attacks. The knowledge of how SNORT managed the attacks is used to evaluate and indicate the vulnerability of different SNORT configurations. Different approaches on both how to bypass SNORT and how to detect attacks are described both theoretically, and practically with experiments. This study concludes that a carefully prepared configuration is the factor for SNORT to perform well in network intrusion detection.

As more of the modern world is connected to the Internet, threats can reach further than ever before. Attacks happen all the time and many have serious consequences that disrupts the daily processes of people and companies, possibly causing lasting damage. To fight back, defensive tools are used to find and counter attacks. One of these tools is Snort. Snort finds malicious data packets and warns the user and counters the found attack. Snort relies on a list of signatures of different attacks, called a rule set, to know what is malicious. Many rule sets are available as paid subscriptions, but there are free alternatives. But how well can Snort defend a network using these free rule sets? By designing a network for experimentation and populating it with realistic background traffic, a group of rule sets are evaluated using a set of common attacks and tools. The performance hit when defending in a high speed, high bandwidth environment is evaluated as well. The results favour the Emerging Threats rule set. As for performance, Snort could not handle the most extreme amounts of traffic, with the rate of dropped packets making security dubious, but that occurred at the absolute peak of what consumer hardware can provide.

Computer networks are already ubiquitous in people’s lives and work and network security is becoming a critical part. A simple firewall, which can only scan the bottom four OSI layers, cannot satisfy all security requirements. An intrusion detection system (IDS) with deep packet inspection, which can filter all seven OSI layers, is becoming necessary for more and more networks. However, the processing throughputs of the IDSs are far behind the current network speed. People have begun to improve the performance of the IDSs by implementing them on different hardware platforms, such as Field-Programmable Gate Array (FPGA) or some special network processors. Nevertheless, all of these options are either less flexible or more expensive to deploy. This research focuses on some possibilities of implementing a parallelized IDS on a general computer environment based on Snort, which is the most popular open-source IDS at the moment. In this thesis, some possible methods have been analyzed for the parallelization of the pattern-matching engine based on a multicore computer. However, owing to the small granularity of the network packets, the pattern-matching engine of Snort is unsuitable for parallelization. In addition, a pipelined structure of Snort has been implemented and analyzed. The universal packet capture API - LibPCAP has been modified for a new feature, which can capture a packet directly to an external buffer. Then, the performance of the pipelined Snort can have an improvement up to 60% on an Intel i7 multicore computer for jumbo frames. A primary limitation is on the memory bandwidth. With a higher bandwidth, the performance of the parallelization can be further improved.

Government agencies and corporations are growing increasingly reliant on networks for day-to-day operations including communication, data processing, and data storage. As a result, these networks are in a constant state of growth. These burgeoning networks cause the number of network security events requiring investigation to grow exceptionally, creating new problems for network security analysts. The increasing number of attacks propagated against high-value networks only increases the gravity. Therefore, security analysts need assistance to be able to continue to monitor network events at an acceptable rate. Network analysts rely on many different systems and tools to properly secure a network. One line of defense is an intrusion detection system or IDS. Intrusion detection systems monitor networks for suspicious activity and then print alerts to a log file. An important part of effective intrusion detection is finding relationships between network events, which allows for detection of network anomalies. However, network analysts typically monitor these logs in a sparsely formatted view, which simply isn’t effective for large networks. Therefore, a Visual Intrusion Detection System or VIDS is an interesting solution to aid network security analysts in properly securing the networks. The visualization tool takes a log file and represents the alerts on a three-dimensional graph. Previous research shows that humans have an innate ability to match patterns based on visual cues, which we hope will allow network analysts to match patterns between alerts and identify anomalies. In addition, the tool will leverage the user’s intuition and experience to aid intrusion detection by allowing them to manipulate the view of the data. The objective of this thesis is to quantify and measure the effectiveness of this Visual Intrusion Detection System built as an extension to the SNORT open source IDS. The purpose of the visualization is to give network security analysts an alternative view from what traditional network security software provides. This thesis will also explore other features that can be built into a Visual Intrusion Detection System to improve its functionality. / Department of Computer Science

<p>Det här examensarbetet undersöker effektiviteten hos ett Intrusion Detection System(IDS). Ett IDS är ett system som skall upptäcka om klienter på ett nätverk attackerasav en ”hacker” eller om någon obehörig försöker inkräkta, ungefär som en vakthund.Det IDS som testats är Snort, ett mycket populärt IDS skrivet med öppen källkod.Syftet med studien är att kunna påvisa huruvida ett IDS är ett bra komplement till ettsystems säkerhet eller inte, då det gjorts väldigt få metodiska undersökningar avSnort, och IDS i allmänhet.Den studie som gjorts utfördes med hjälp av ett antal experiment i enlaborationsmiljö, där effektiviteten hos Snort sattes på prov med hjälp av olika typerav attacker.Utifrån det resultat som uppkom så går det att konstatera att ett IDS absolut är ettkomplement värt att överväga för en organisation som är villig att ägna de resursersom systemet kräver, då ett högt antal av de utförda attackerna upptäcktes – attackersom anti-virus eller brandväggar inte är skapade för att reagera på.</p>

<p>Arbetets syfte är att jämföra intrångsdetekteringssystemen Snort och Suricata för att ge en uppfattning om vilken av applikationerna som lämpar sig att implementeras hos en internetleverantör för att upptäcka attacker och öka säkerheten på nätverket. Jämförelsen utförs med hänseende till antal upptäckta attacker, prestanda, implementeringstid, antal konfigurationsfiler samt vilka operativsystem de finns tillgängliga på.</p><p>Resultatet visar att Suricata med sitt stöd för att använda signaturer skapade för Snort upptäcker fler attacker än Snort. Snort däremot går både smidigare och snabbare att implementera. Prestandamässigt så visar Suricata bäst resultat, genom att använda sig av flera kärnor och mindre minne.</p>