Biblio

For the network intrusion detection system (NIDS), improving the performance of the analysis process has always been one of the primary goals that NIDS needs to solve. An important method to improve performance is to use parallel processing technology to maximize the usage of multi-core CPU resources. In this paper, by splitting Pcap data packets, the NIDS software Snort3 can process Pcap packets in parallel mode. On this basis, this paper compares the performance between Snort2, Suricata, and Snort3 with different CPU cores in processing different sizes of Pcap data packets. At the same time, a parallel unpacking algorithm is proposed to further improve the parallel processing performance of Snort3.

One of the main goals of studying pattern matching techniques is their significant role in real-world applications, such as the intrusion detection systems branch. The purpose of the network attack detection systems NIDS is to protect the infocommunication network from unauthorized access. This article provides an analysis of the exact match and fuzzy matching methods, and discusses a new implementation of the classic Aho-Korasik pattern matching algorithm at the hardware level. The proposed approach to the implementation of the Aho-Korasik algorithm can make it possible to ensure the efficient use of resources, such as memory and energy.

Network Intrusion Detection System (NIDS) can help administrators of a server in detecting attacks by analyzing packet data traffic on the network in real-time. If an attack occurs, an alert to the administrator is provided by NIDS so that the attack can be known and responded immediately. On the other hand, the alerts cannot be monitored by administrators all the time. Therefore, a system that automatically sends notifications to administrators in real-time by utilizing social media platforms is needed. This paper provides an analysis of the notification system built using Snort as NIDS with WhatsApp and Telegram as a notification platform. There are three types of attacks that are simulated and must be detected by Snort, which are Ping of Death attacks, SYN flood attacks, and SSH brute force attacks. The results obtained indicate that the system successfully provided notification in the form of attack time, IP source of the attack, source of attack port and type of attack in real-time.

Intrusion Detection System is a well-known term in the domain of Network and Information Security. It's one of the important components of the Network and Information Security infrastructure. Host Intrusion Detection System (HIDS) helps to detect unauthorized use, abnormal and malicious activities on the host, whereas Network Intrusion Detection System (NIDS) helps to detect attacks and intrusion on networks. Various researchers are actively working on different approaches to improving the IDS performance and many improvements have been achieved. However, development in many other technologies and newly emerging techniques always opens the doors of opportunity to add a sharp edge to IDS and to make it more robust and reliable. This paper proposes the development of Distributed Intrusion Detection System (DIDS) using emerging and promising technologies like Blockchain upon a stable platform like cloud infrastructure.

Intrusion detection systems (IDS) are important security tools. NIDS monitors network's traffic and HIDS filters local one. HIDS are often based on anomaly detection. Several studies deal with anomaly detection using system-call traces. In this paper, we propose an anomaly detection and prediction approach. System-call traces, invoked by the running programs, are analyzed in real time. For prediction, we use a Sequence to sequence model based on variational encoder-decoder (VED) and variants of Recurrent Neural Networks (RNN), these architectures showed their performance on natural language processing. To make the analogy, we exploit the semantics behind the invoking order of system-calls that are then seen as sentences. A preprocessing phase is added to optimize the prediction model input data representation. A one-class classification is done to categorize the sequences into normal or abnormal. Tests are achieved on the ADFA-LD dataset and showed the advantage of the prediction for the intrusion detection/prediction task.

Internet of Things is realized by a large number of heterogeneous smart devices which sense, collect and share data with each other over the internet in order to control the physical world. Due to open nature, global connectivity and resource constrained nature of smart devices and wireless networks the Internet of Things is susceptible to various routing attacks. In this paper, we purpose an architecture of Ensemble Learning based Network Intrusion Detection System named ELNIDS for detecting routing attacks against IPv6 Routing Protocol for Low-Power and Lossy Networks. We implement four different ensemble based machine learning classifiers including Boosted Trees, Bagged Trees, Subspace Discriminant and RUSBoosted Trees. To evaluate proposed intrusion detection model we have used RPL-NIDDS17 dataset which contains packet traces of Sinkhole, Blackhole, Sybil, Clone ID, Selective Forwarding, Hello Flooding and Local Repair attacks. Simulation results show the effectiveness of the proposed architecture. We observe that ensemble of Boosted Trees achieve the highest Accuracy of 94.5% while Subspace Discriminant method achieves the lowest Accuracy of 77.8 % among classifier validation methods. Similarly, an ensemble of RUSBoosted Trees achieves the highest Area under ROC value of 0.98 while lowest Area under ROC value of 0.87 is achieved by an ensemble of Subspace Discriminant among all classifier validation methods. All the implemented classifiers show acceptable performance results.

Abstract—Network intrusion detection systems (NIDS) are essential security building-blocks for today’s organizations to ensure safe and trusted communication of information. In this paper, we study the feasibility of off-line deep learning based NIDSes by constructing the detection engine with multiple advanced deep learning models and conducting a quantitative and comparative evaluation of those models. We first introduce the general deep learning methodology and its potential implication on the network intrusion detection problem. We then review multiple machine learning solutions to two network intrusion detection tasks (NSL-KDD and UNSW-NB15 datasets). We develop a TensorFlow-based deep learning library, called NetLearner, and implement a handful of cutting-edge deep learning models for NIDS. Finally, we conduct a quantitative and comparative performance evaluation of those models using NetLearner.

Web Application becomes the leading solution for the utilization of systems that need access globally, distributed, cost-effective, as well as the diversity of the content that can run on this technology. At the same time web application security have always been a major issue that must be considered due to the fact that 60% of Internet attacks targeting web application platform. One of the biggest impacts on this technology is Cross Site Scripting (XSS) attack, the most frequently occurred and are always in the TOP 10 list of Open Web Application Security Project (OWASP). Vulnerabilities in this attack occur in the absence of checking, testing, and the attention about secure coding practices. There are several alternatives to prevent the attacks that associated with this threat. Network Intrusion Detection System can be used as one solution to prevent the influence of XSS Attack. This paper investigates the XSS attack recognition and detection using regular expression pattern matching and a preprocessing method. Experiments are conducted on a testbed with the aim to reveal the behaviour of the attack.

A frequent claim that has not been validated is that signature based network intrusion detection systems (SNIDS) cannot detect zero-day attacks. This paper studies this property by testing 356 severe attacks on the SNIDS Snort, configured with an old official rule set. Of these attacks, 183 attacks are zero-days' to the rule set and 173 attacks are theoretically known to it. The results from the study show that Snort clearly is able to detect zero-days' (a mean of 17% detection). The detection rate is however on overall greater for theoretically known attacks (a mean of 54% detection). The paper then investigates how the zero-days' are detected, how prone the corresponding signatures are to false alarms, and how easily they can be evaded. Analyses of these aspects suggest that a conservative estimate on zero-day detection by Snort is 8.2%.

This paper proposes a new network-based cyber intrusion detection system (NIDS) using multicast messages in substation automation systems (SASs). The proposed network-based intrusion detection system monitors anomalies and malicious activities of multicast messages based on IEC 61850, e.g., Generic Object Oriented Substation Event (GOOSE) and Sampled Value (SV). NIDS detects anomalies and intrusions that violate predefined security rules using a specification-based algorithm. The performance test has been conducted for different cyber intrusion scenarios (e.g., packet modification, replay and denial-of-service attacks) using a cyber security testbed. The IEEE 39-bus system model has been used for testing of the proposed intrusion detection method for simultaneous cyber attacks. The false negative ratio (FNR) is the number of misclassified abnormal packets divided by the total number of abnormal packets. The results demonstrate that the proposed NIDS achieves a low fault negative rate.
 

An intrusion detection system (IDS) inspects all inbound and outbound network activity and identifies suspicious patterns that may indicate a network or system attack from someone attempting to break into or compromise a system. A networkbased system, or NIDS, the individual packets flowing through a network are analyzed. In a host-based system, the IDS examines at the activity on each individual computer or host. IDS techniques are divided into two categories including misuse detection and anomaly detection. In recently years, Mobile Agent based technology has been used for distributed systems with having characteristic of mobility and autonomy. In this working we aimed to combine IDS with Mobile Agent concept for more scale, effective, knowledgeable system.

Intrusion Detection Systems (IDS) have become a necessity in computer security systems because of the increase in unauthorized accesses and attacks. Intrusion Detection is a major component in computer security systems that can be classified as Host-based Intrusion Detection System (HIDS), which protects a certain host or system and Network-based Intrusion detection system (NIDS), which protects a network of hosts and systems. This paper addresses Probes attacks or reconnaissance attacks, which try to collect any possible relevant information in the network. Network probe attacks have two types: Host Sweep and Port Scan attacks. Host Sweep attacks determine the hosts that exist in the network, while port scan attacks determine the available services that exist in the network. This paper uses an intelligent system to maximize the recognition rate of network attacks by embedding the temporal behavior of the attacks into a TDNN neural network structure. The proposed system consists of five modules: packet capture engine, preprocessor, pattern recognition, classification, and monitoring and alert module. We have tested the system in a real environment where it has shown good capability in detecting attacks. In addition, the system has been tested using DARPA 1998 dataset with 100% recognition rate. In fact, our system can recognize attacks in a constant time.