Snort Intrusion Detection System Research Articles

P‐DNN: Parallel DNN based IDS framework for the detection of IoT vulnerabilities

SummaryThe rapid growth of the Internet of Things (IoT) in our daily life has recently received attention from hackers in releasing novel attacks. This is because the existing traditional Intrusion Detection System (IDS) uses an alert‐based approach that cannot detect new emerging attacks, making it unfeasible for devices with limited resources. The study presents the deployment of a Parallel Deep Neural Network (P‐DNN) IDS framework to improve the detection rate and resource constraint issues. To validate the framework for detecting the latest IoT vulnerabilities, we generated a proprietary dataset created under the IoT environment. The experimental results reveal that the P‐DNN IDS framework proposed in this study outperformed the K‐nearest Neighbour (KNN), Support Vector Machine (SVM), and Naive Bayes algorithms with respect to performance metrics. In addition, a comparative analysis of the P‐DNN framework with Snort and Suricata IDS results demonstrates a significant reduction in CPU consumption, memory utilization, and processing time. The results of this investigation indicate that the deployment of the P‐DNN framework has the capability to detect vulnerabilities in resource‐constraint IoT environments effectively.

Research of Model Increasing Reliability Intrusion Detection Systems

The paper presents the results of the using, a recurrent neural network to detect malicious software as part of the Snort intrusion detection system.The research was conducted on datasets generated on the basis of athena, dyre, engrat, grum, mimikatz, surtr malware exploiting vulnerability CVE-2022-20685 in the Snort intrusion detection system. Processing of input traffic data was carried out before the frag-3 and modbus preprocessors. The method of k nearest neighbors was used as a mathematical apparatus. The simulation of the developed software at different iterations. All research results are presented in https://github.com/T-JN

Network Security Analysis with SnortIDS Using ACID (Analysis Console for Intrusion Databases

The use of Wi-Fi and Ethernet is increasing in today's computer networks due to the advancement of technology. The development of networks today is characterized by the need for low-latency and high-bandwidth technology. The technology has also introduced 5G and Wi-Fi 6 which support high-speed internet surfing. The introduction of Network File System (NFS) in this era sparked the demand for Ethernet. NFS also increased the use of UNIX in education and professional computing in the 1980s. Then, in 1982, Token Ring Topology emerged as an alternative to the internet and was only standardized in 1985. Network security is an important factor in ensuring data is not stolen or damaged. With the increasing knowledge of hacking and cracking, and the availability of tools that can be easily used to launch attacks or intrusions, it is important to investigate when an attack occurs. One network forensic method for monitoring attacks on the network is using Snort IDS and Ntop to facilitate the logging process for monitoring the network system. Based on the results obtained from designing a network security with Snort Intrusion Detection System (IDS) using ACID (Analysis Console for Intrusion Databases) with the utilization of IPTables on Ubuntu Server can stop attackers. In this research, the researcher used IPTables on Ubuntu as a firewall to anticipate attacks. To prevent port scanning attacks conducted by the attacker, the author created a firewall using IPTables where the IPTables rules aim to block the IP address of the attacker.

An Empirical Investigation on Snort NIDS versus Supervised Machine Learning Classifiers

With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade the detection rates of current NIDSs, thorough analyses are essential to identify where ML predictors outperform them. The first step is to provide assessment of most used NIDS worldwide, Snort, and comparing its performance with ML classifiers. This paper provides an empirical study to evaluate performance of Snort and four supervised ML classifiers, KNN, Decision Tree, Bayesian net and Naïve Bays against network attacks, probing, Brute force and DoS. By measuring Snort metric, True Alarm Rate, F-measure, Precision and Accuracy and compares them with the same metrics conducted from applying ML algorithms using Weka tool. ML classifiers show an elevated performance with over 99% correctly classified instances for most algorithms, While Snort intrusion detection system shows a degraded classification of about 25% correctly classified instances, hence identifying Snort weaknesses towards certain attack types and giving leads on how to overcome those weaknesses. es.

Intrusion Detection System (IDS) Accuracy Testing for Software Defined Network Internet of Things (SDN-IOT) Testbed


 Intrusion detection system (IDS) are considered as one of the best solutions for network security as it can detect intrusion and alert the network administrator on possible intrusions. However there are possible false alert that could cause unnecessary trigger of the network to the administrator. This paper provides a proof of concept of the accuracy test of an intrusion detection system (IDS) using software defined network IoT platform. The testbed uses UNSW-NB15 dataset that feeds the testbed and the traffic are mirror in a Ryu Controller that is installed with Snort IDS to monitor any DDoS attacks. For proof of concept false positive and false negative tests are run to ensure that the IDS are well configured. The experiment shows that the SDN-IoT platform with Snort IDS is accurate in both false positive and false negative test.

Mengoptimalkan Keamanan Jaringan Komputer Menggunakan Snort dan Telegram Bot yang Terintegrasi dengan Mikrotik

The implementation of IDS (Intrusion Detection System) snort and telegram bot that can be integrated with a microtic router on the Uppala Villa Nusa Dua computer network is used to detect attack activities and suspicious activity on the Uppala Villa Nusa Dua computer network and to provide notification in real-time logs of suspicious activity on computer network. The research method used is the SPDLC (Security Policy Development Life Cycle) method which has six stages: Identification, Analysis, Design, Implementation, Testing and Evaluation. The software used on the server computer is Snort, WinPcap, Xampp and BASE (Basic Analysis and Security Engine) while for testing a computer network security system using Nmap, Loic and Brutus tools. The results obtained by the implementation of the IDS (Intrusion Detection System) Snort and telegram bot have been successfully implemented and can be integrated with the microtic router. Based on testing conducted after the implementation of the new system, it was found that 95% of the use of snort and telegram bot can optimize the computer network security system at Uppala Villa Nusa Dua.

Implementation of a Malicious Traffic Filter Using Snort and Wireshark as a Proof of Concept to Enhance Mobile Network Security

In the 1970s, roaming interconnections for cellular networks were designed for a few trusted parties. Hence, security was not a major concern. Today, the SS7 (Signaling System no. 7) solution that is several decades old is still used for many roaming interconnections. SS7 has been proven vulnerable to serious threats due to deregulation, expansion, and convergence with IP-based Long Term Evolution (LTE) networks. The limitations of the SS7 network that it is unable to check the subscriber’s authentic location, verify their identity and filter illegitimate messages, makes the system vulnerable to attacks. Adversaries taking advantage of these shortcomings can inflict threats such as interception of calls and text messages, subscriber tracking and denial of service attacks. Although LTE and Diameter signaling protocols promise enhanced security keeping up with the latest attack vectors, their inherent flaws related to roaming interconnections are still there and continue to make the networks vulnerable. Hence, a highly secure signaling network is required to protect the operators and the subscribers from a diverse range of security attacks. SS7 network protocol layers, such as signaling connection control part (SCCP), transaction capabilities application part (TCAP), and global system for mobile Communications – mobile application part (GSM MAP), manage connectivity between networks and subscribers. An analysis of the parameters of these layers may provide a clear insight into any anomalies present. Unfortunately, these parameters are not validated and verified at the network’s edge. The major contribution of this research is a methodology for detecting anomalies by checking malformed parameters and intra-layer parameter discrepancies at the abovementioned protocol layers. This paper provides an insight into the severity of SS7 network security vulnerabilities. Furthermore, it provides a proof of concept for the analysis of SS7 network traffic using the Wireshark packet capture tool and the Snort intrusion detection system (IDS) capable of detecting malicious traffic patterns.

Snort ids system visualization interface for alert analysis

Over the past decades, the rapid Internet development and the growth in the number of its users have raised various security issues. Therefore, it is of great importance to ensure the security of the network in order to enable the safe exchange of confidential data, as well as their integrity. One of the most important components of network attack detection is an Intrusion Detection System (IDS). Snort IDS is a widely used intrusion detection system, which logs alerts after detecting potentially dangerous network packets. A major challenge in network monitoring is the high volume of generated IDS alerts. A necessary step in successful network protection is the analysis of the great amount of logged alerts in search of deviations from normal traffic that may indicate an intrusion. The goal of this paper is to design and implement a visualization interface for IDS alert analysis, which graphically presents alerts generated by Snort IDS. Also, the proposed system classifies the alerts according to the most important attack parameters, and allows the users to understand evolving network situations and easily detect possible traffic irregularities. An environment in which the system has been tested in real-time is described, and the results of attack detection and classification are given. One of the detected attacks is analyzed in detail, as well as the method of its detection and its possible consequences.

Supervised Learning Techniques for Intrusion Detection System based on Multi-layer Classification Approach

The goal of this study is to discover a solution to two problems: first, the signature-based intrusion detection system SNORT can identify a new attack signature without human intervention; and second, signature-based IDS cannot detect multi-stage attacks. The interesting aspect of this study is the growing ways to address the aforementioned issues. We introduced a multi-layer classification strategy in this study, in which we employ two layers, the first of which is based on a decision tree, and the second of which includes machine learning technique fuzzy logic and neural networks. If the first layer fails to identify fresh attacks, the second layer takes over and detects new signature assaults, updating the SNORT signature automatically.

Dynamical analysis of diversity in rule-based open source network intrusion detection systems

Diverse layers of defence play an important role in the design of defence-in-depth architectures. The use of Intrusion Detection Systems (IDSs) are ubiquitous in this design. But the selection of the “right” IDSs in various configurations is an important decision that the security architects need to make. Additionally, the ability of these IDSs to adapt to the evolving threat-landscape also needs to be investigated. To help with these decisions, we need rigorous quantitative analysis. In this paper, we present a diversity analysis of open-source IDSs, Snort and Suricata, to help security architects tune/deploy these IDSs. We analyse two types of diversities in these IDSs; configurational diversity and functional diversity. In the configurational diversity analysis, we investigate the diversity in the sets of rules and the Blacklisted IP Addresses (BIPAs) these IDSs use in their configurations. The functional diversity analysis investigates the differences in alerting behaviours of these IDSs when they analyse real network traffic, and how these differences evolve. The configurational diversity experiment utilises snapshots of the rules and BIPAs collected over a period of 5 months, from May to October 2017. The snapshots have been collected for three different off-the-shelf default configurations of the Snort IDS and the Emerging Threats (ET) configuration of the Suricata IDS. The functional diversity investigates the alerting behaviour of these two IDSs for a sample of the real network traffic collected in the same time window. Analysing the differences in these systems allows us to get insights into where the diversity in the behaviour of these systems comes from, how does it evolve and whether this has any effect on the alerting behaviour of these IDSs. This analysis gives insight to security architects on how they can combine and layer these systems in a defence-in-depth deployment.

A flow-based intrusion detection framework for internet of things networks

The application of the Internet of Things concept in domains such as industrial control, building automation, human health, and environmental monitoring, introduces new privacy and security challenges. Consequently, traditional implementation of monitoring and security mechanisms cannot always be presently feasible and adequate due to the number of IoT devices, their heterogeneity and the typical limitations of their technical specifications. In this paper, we propose an IP flow-based Intrusion Detection System (IDS) framework to monitor and protect IoT networks from external and internal threats in real-time. The proposed framework collects IP flows from an IoT network and analyses them in order to monitor and detect attacks, intrusions, and other types of anomalies at different IoT architecture layers based on some flow features instead of using packet headers fields and their payload. The proposed framework was designed to consider both the IoT network architecture and other IoT contextual characteristics such as scalability, heterogeneity, interoperability, and the minimization of the use of IoT networks resources. The proposed IDS framework is network-based and relies on a hybrid architecture, as it involves both centralized analysis and distributed data collection components. In terms of detection method, the framework uses a specification-based approach drawn on normal traffic specifications. The experimental results show that this framework can achieve ≈ 100% success and 0% of false positives in detection of intrusions and anomalies. In terms of performance and scalability in the operation of the IDS components, we study and compare it with three different conventional IDS (Snort, Suricata, and Zeek) and the results demonstrate that the proposed solution can consume fewer computational resources (CPU, RAM, and persistent memory) when compared to those conventional IDS.

WITHDRAWN: Testing performance of RaspberryPi as IDS using SNORT

Now a day’s world is moving fast towards more advancement where internet usage has become essential to carry out daily activities. Almost all IoT based devices in our home like fans, lights, air conditioners etc. are being connected and accessed over the internet with own IP address can be open to hackers. Definitely IoT makes its mark of significance in our life at the other coin the major issue of IoT is security which makes security analysts to move their focus towards security of IoT devices and networks. Performance of RaspberryPi is tested in this paper to justify which kit works well to safeguard from intrusion when Snort IDS (Intrusion Detection System) is installed. Comparison of RaspberryPi is carried out using snort IDS with custom rule set specified in the case study done in 2013 and default set of rules of Snort IDS tested on IOT kits RaspberryPi 4B and RaspberryPi 3 A+ and comparison on non functional requirements CPU load, throughput &memory load was made to find out which IOT kit is preferable to safeguard from hacking attack.

Intruder Detection Systems on Computer Networks Using Host Based Intrusion Detection System Techniques

The openness of access to information raises various problems, including maintaining the validity and integrity of data, so a network security system is needed that can deal with potential threats that can occur quickly and accurately by utilizing an IDS (intrusion detection system). One of the IDS tools that are often used is Snort which works in real-time to monitor and detect the ongoing network by providing warnings and information on potential threats in the form of DoS attacks. DoS attacks run to exhaust the packet path by requesting packets to a target in large and continuous ways which results in increased usage of CPU (central processing unit), memory, and ethernet or WiFi networks. The snort IDS implementation can help provide accurate information on network security that you want to monitor because every communication that takes place in a network, every event that occurs and potential attacks that can paralyze the internet network are monitored by snort.

Denial‐of‐Service Attack Detection over IPv6 Network Based on KNN Algorithm

With the rapid increase and complexity of IPv6 network traffic, the traditional intrusion detection system Snort detects DoS attacks based on specific rules, which reduces the detection performance of IDS. To solve the DoS intrusion detection problem in the IPv6 network environment, the lightweight KNN optimization algorithm in machine learning is adopted. First, the double dimensionality reduction of features is achieved through the information gain rate, and discrete features with more subfeatures are selected and aggregated to further dimensionality reduction and feature dimension of the actual operation. Secondly, the information gain rate is used as the weight to optimize the sample Euclidean distance measurement. Based on the proposed measure of the reverse distance influence, the classification decision algorithm of the KNN algorithm is optimized to make the detection technology better. The effect is further improved. The experimental results show that the traditional TAD‐KNN algorithm based on average distance and the GR‐KNN algorithm that only optimizes the distance definition, the GR‐AD‐KNN algorithm can not only improve the overall detection performance in the detection of IPv6 network traffic characteristics but also for small groups of samples. As a result, classification has better detection results.

Comparative Analysis of the Performance of Network Intrusion Detection Systems: Snort, Suricata and Bro Intrusion Detection Systems in Perspective

Comparative Analysis of the Performance of Network Intrusion Detection Systems: Snort, Suricata and Bro Intrusion Detection Systems in Perspective

SNORT based early DDoS detection system using Opendaylight and open networking operating system in software defined networking

Software-defined networking (SDN) is an approach in the network that provides many advantages with the help of separating the intelligence of the network (controller) with the underlying network infrastructure (data plane). But this isolation also gives birth to many security concerns; therefore, the need to protect the network from various attacks is becoming mandatory. Distributed Denial of Service (DDoS) in SDN is one such attack that is becoming a hurdle to its growth. Before the mitigation of DDoS attacks, the primary step is to detect them. In this paper, an early DDoS detection tool is created by using SNORT IDS (Intrusion Detection System). This tool is integrated with popularly used SDN controllers (Opendaylight and Open Networking Operating System). For the experimental setup, five different network scenarios are considered. In each scenario number of hosts, switches and data packets vary. For the creation of different hosts, switches the Mininet emulation tool is used whereas for generating the data packets four different penetration tools such as Hping3, Nping, Xerxes, Tor Hammer, LOIC are used. The generated data packets are ranging from (50,000 per second–2,50,000 per second) and the number of hosts/switches are ranging from (50–250) in every scenario respectively. The data traffic is bombarded towards the controllers and the evaluation of these packets is achieved by making use of Wireshark. The analysis of our DDoS detection system is performed on the basis of various parameters such as time to detect the DDoS attack, Round Trip Time (RTT), percentage of packet loss and type of DDoS attack. It is found that ODL takes minimum time to detect the successful DDoS attack and more time to go down than ONOS. Our tool ensures the timely detection of fast DDoS attacks which delivers the better performance of the SDN controller and not compromising the overall functionality of the entire network.

BOT DETECTION IN CLOUD STORAGE SYSTEMS – A PREPARATORY EFFORT

Data that the users upload on the Internet in the form of social media etc.., which are supposed to be stored on cloud is presumed to be secure and private by them, but how far this is true is a million dollar question that arises. The core objective of this paper is to imbibe network security in the field of cloud computing by detecting any kind of malware from attacking the system and preventing it to safeguard our data. Throughout this paper, it has been focused on detecting the Botnet that is emerging as the major concern and threat these days in cloud storage systems. Hence, to detect such Botnets, Snort Intrusion Detection System is used as our tool by examining the type of packets that are captured during transit. Wireshark packet sniffing software is also used to make our work easier. For this software to work effectively and collaboratively, Security Onion Virtual Machine plays a major key role by invoking virtualization technology and by also providing Squert visualization tool which is the main interface where Snort alerts can be seen.

An intelligent flow-based and signature-based IDS for SDNs using ensemble feature selection and a multi-layer machine learning-based classifier

Software-defined networking is a new paradigm that overcomes problems associated with traditional network architecture by separating the control logic from data plane devices. It also enhances performance by providing a highly-programmable interface that adapts to dynamic changes in network policies. As software-defined networking controllers are prone to single-point failures, providing security is one of the biggest challenges in this framework. This paper intends to provide an intrusion detection mechanism in both the control plane and data plane to secure the controller and forwarding devices respectively. In the control plane, we imposed a flow-based intrusion detection system that inspects every new incoming flow towards the controller. In the data plane, we assigned a signature-based intrusion detection system to inspect traffic between Open Flow switches using port mirroring to analyse and detect malicious activity. Our flow-based system works with the help of trained, multi-layer machine learning-based classifier, while our signature-based system works with rule-based classifiers using the Snort intrusion detection system. The ensemble feature selection technique we adopted in the flow-based system helps to identify the prominent features and hasten the classification process. Our proposed work ensures a high level of security in the Software-defined networking environment by working simultaneously in both control plane and data plane.

Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN

Distributed Denial of Service (DDoS) is one of the most rampant attacks in the modern Internet of Things (IoT) network infrastructures. Security plays a very vital role for an ever-growing heterogeneous network of IoT nodes, which are directly connected to each other. Due to the preliminary stage of Software Defined Networking (SDN), in the IoT network, sampling based measurement approaches currently results in low-accuracy, higher memory consumption, higher-overhead in processing and network, and low attack-detection. To deal with these aforementioned issues, this paper proposes sFlow and adaptive polling based sampling with Snort Intrusion Detection System (IDS) and deep learning based model, which helps to lower down the various types of prevalent DDoS attacks inside the IoT network. The flexible decoupling property of SDN enables us to program network devices for required parameters without utilizing third-party propriety based hardware or software. Firstly, in data-plane, to lower down processing and network overhead of switches, we deployed sFlow and adaptive polling based sampling individually. Secondly, in control-plane, to optimize detection accuracy, we deployed Snort IDS collaboratively with Stacked Autoencoders (SAE) deep learning model. Furthermore, after applying performance metrics on collected traffic streams, we quantitatively investigate trade off among attack detection accuracy and resources overhead. The evaluation of the proposed system demonstrates higher detection accuracy with 95% of True Positive rate with less than 4% of False Positive rate within sFlow based implementation compared to adaptive polling.

Distributed intrusion detection scheme for next generation networks

IP Multimedia Subsystem (IMS) is a next generation network that provides the hypermedia services as data, voice and video to users. Due to high level requirements for IMS services, new kinds of network attacks are endlessly emerging. Thus, it is of paramount importance to protect the networks from attacks. Consequently, the Intrusion Detection Systems (IDS) are quickly becoming a popular requirement in building a network security infrastructure. Securing service and signalization is a vital feature in the IMS network. Signaling is generally based on Session Initiation Protocol (SIP) which offers numerous challenges regarding security which causes issues in IMS network. This work presents a study of SIP protocol and discovers the critical security vulnerabilities in the course of registration phase. We focused on DDoS attacks on IMS server using SIP particularly with REGISTER message and proposed a scheme based on multi agent systems for intrusion detection which takes the advantage of the distributed paradigm to implement an efficient distributed system, as well as the integration of existing techniques, i.e., the well-known IDS SNORT.