Diversity in Open Source Intrusion Detection Systems

We present an analysis of the diversity that exists in the rules and blacklisted IP addresses of the Snort and Suricata Intrusion Detection Systems (IDSs). We analysed the evolution of the rulesets and blacklisted IP addresses of these two IDSs over a 5-month period between May and October 2017. We used three different off-the-shelf default configurations of the Snort IDS and the Emerging Threats (ET) configuration of the Suricata IDS. Analysing the differences in these systems allows us to get insights on where the diversity in the behaviour of these systems comes from and how does it evolve over time. This gives insight to Security architects on how they can combine and layer these systems in a defence-in-depth deployment. To the best of our knowledge a similar experiment has not been performed before. We will also show results on the observed diversity in behaviour of these systems, when they analysed the network data of the DMZ network of City, University of London

On the Detection Capabilities of Signature-Based Intrusion Detection Systems in the Context of Web Attacks

Signature-based Intrusion Detection Systems (SIDS) play a crucial role within the arsenal of security components of most organizations. They can find traces of known attacks in the network traffic or host events for which patterns or signatures have been pre-established. SIDS include standard packages of detection rulesets, but only those rules suited to the operational environment should be activated for optimal performance. However, some organizations might skip this tuning process and instead activate default off-the-shelf rulesets without understanding its implications and trade-offs. In this work, we help gain insight into the consequences of using predefined rulesets in the performance of SIDS. We experimentally explore the performance of three SIDS in the context of web attacks. In particular, we gauge the detection rate obtained with predefined subsets of rules for Snort, ModSecurity and Nemesida using seven attack datasets. We also determine the precision and rate of alert generated by each detector in a real-life case using a large trace from a public webserver. Results show that the maximum detection rate achieved by the SIDS under test is insufficient to protect systems effectively and is lower than expected for known attacks. Our results also indicate that the choice of predefined settings activated on each detector strongly influences its detection capability and false alarm rate. Snort and ModSecurity scored either a very poor detection rate (activating the less-sensitive predefined ruleset) or a very poor precision (activating the full ruleset). We also found that using various SIDS for a cooperative decision can improve the precision or the detection rate, but not both. Consequently, it is necessary to reflect upon the role of these open-source SIDS with default configurations as core elements for protection in the context of web attacks. Finally, we provide an efficient method for systematically determining which rules deactivate from a ruleset to significantly reduce the false alarm rate for a target operational environment. We tested our approach using Snort’s ruleset in our real-life trace, increasing the precision from 0.015 to 1 in less than 16 h of work. View Full-TextMinisterio de Ciencias e Innovación (MICINN)/AEI 10.13039/501100011033: PID2020-115199RB-I00FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades PYC20-RE-087-US

On the Detection Capabilities of Signature-Based Intrusion Detection Systems in the Context of Web Attacks

This work has been partly funded by the research grant PID2020-115199RB-I00 provided by the Spanish ministry of Industry under the contract MICIN/AEI/10.13039/501100011033, and also by FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades under project PYC20-RE-087-USE.Signature-based Intrusion Detection Systems (SIDS) play a crucial role within the arsenal of security components of most organizations. They can find traces of known attacks in the network traffic or host events for which patterns or signatures have been pre-established. SIDS include standard packages of detection rulesets, but only those rules suited to the operational environment should be activated for optimal performance. However, some organizations might skip this tuning process and instead activate default off-the-shelf rulesets without understanding its implications and trade-offs. In this work, we help gain insight into the consequences of using predefined rulesets in the performance of SIDS. We experimentally explore the performance of three SIDS in the context of web attacks. In particular, we gauge the detection rate obtained with predefined subsets of rules for Snort, ModSecurity and Nemesida using seven attack datasets. We also determine the precision and rate of alert generated by each detector in a real-life case using a large trace from a public webserver. Results show that the maximum detection rate achieved by the SIDS under test is insufficient to protect systems effectively and is lower than expected for known attacks. Our results also indicate that the choice of predefined settings activated on each detector strongly influences its detection capability and false alarm rate. Snort and ModSecurity scored either a very poor detection rate (activating the less-sensitive predefined ruleset) or a very poor precision (activating the full ruleset). We also found that using various SIDS for a cooperative decision can improve the precision or the detection rate, but not both. Consequently, it is necessary to reflect upon the role of these open-source SIDS with default configurations as core elements for protection in the context of web attacks. Finally, we provide an efficient method for systematically determining which rules deactivate from a ruleset to significantly reduce the false alarm rate for a target operational environment. We tested our approach using Snort’s ruleset in our real-life trace, increasing the precision from 0.015 to 1 in less than 16 h of work.Spanish Government PID2020-115199RB-I00 MICIN/AEI/10.13039/501100011033FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades PYC20-RE-087-US

Optimising a defence-aware threat modelling diagram incorporating a defence-in-depth approach for the internet-of-things

Modern technology has proliferated into just about every aspect of life while improving the quality of life. For instance, IoT technology has significantly improved over traditional systems, providing easy life, time-saving, financial saving, and security aspects. However, security weaknesses associated with IoT technology can pose a significant threat to the human factor. For instance, smart doorbells can make household life easier, save time, save money, and provide surveillance security. Nevertheless, the security weaknesses in smart doorbells could be exposed to a criminal and pose a danger to the life and money of the household. In addition, IoT technology is constantly advancing and expanding and rapidly becoming ubiquitous in modern society. In that case, increased usage and technological advancement create security weaknesses that attract cybercriminals looking to satisfy their agendas. Perfect security solutions do not exist in the real world because modern systems are continuously improving, and intruders frequently attempt various techniques to discover security flaws and bypass existing security control in modern systems. In that case, threat modelling is a great starting point in understanding the threat landscape of the system and its weaknesses. Therefore, the threat modelling field in computer science was significantly improved by implementing various frameworks to identify threats and address them to mitigate them. However, most mature threat modelling frameworks are implemented for traditional IT systems that only consider software-related weaknesses and do not address the physical attributes. This approach may not be practical for IoT technology because it inherits software and physical security weaknesses. However, scholars employed mature threat modelling frameworks such as STRIDE on IoT technology because mature frameworks still include security concepts that are significant for modern technology. Therefore, mature frameworks cannot be ignored but are not efficient in addressing the threat associated with modern systems. As a solution, this research study aims to extract the significant security concept of matured threat modelling frameworks and utilise them to implement robust IoT threat modelling frameworks. This study selected fifteen threat modelling frameworks from among researchers and the defence-in-depth security concept to extract threat modelling techniques. Subsequently, this research study conducted three independent reviews to discover valuable threat modelling concepts and their usefulness for IoT technology. The first study deduced that integration of threat modelling approach software-centric, asset-centric, attacker-centric and data-centric with defence-in-depth is valuable and delivers distinct benefits. As a result, PASTA and TRIKE demonstrated four threat modelling approaches based on a classification scheme. The second study deduced the features of a threat modelling framework that achieves a high satisfaction level toward defence-in-depth security architecture. Under evaluation criteria, the PASTA framework scored the highest satisfaction value. Finally, the third study deduced IoT systematic threat modelling techniques based on recent research studies. As a result, the STRIDE framework was identified as the most popular framework, and other frameworks demonstrated effective capabilities valuable to IoT technology. Respectively, this study introduced Defence-aware Threat Modelling (DATM), an IoT threat modelling framework based on the findings of threat modelling and defence-in-depth security concepts. The steps involved with the DATM framework are further described with figures for better understatement. Subsequently, a smart doorbell case study is considered for threat modelling using the DATM framework for validation. Furthermore, the outcome of the case study was further assessed with the findings of three research studies and validated the DATM framework. Moreover, the outcome of this thesis is helpful for researchers who want to conduct threat modelling in IoT environments and design a novel threat modelling framework suitable for IoT technology