Biblio

Intrusion Detection System (IDS) is a network security software and hardware widely used to detect anomaly network traffics by comparing the traffics against rules specified beforehand. Snort is one of the most famous open-source IDS system. To write a rule, Snort specifies structure and values in Snort manual. This specification is expressive enough to write in different way with the same meaning. If there are rule redundancy, it could distract performance. We, thus, propose a proof of semantical issues for Snort rule and found four pairs of Snort rule combinations that can cause redundancy. In addition, we create a tool to verify such redundancy between two rules on the public rulesets from Snort community and Emerging threat. As a result of our test, we found several redundancy issues in public rulesets if the user enables commented rules.

The rapid development of technology makes it possible for a physical machine to be converted into a virtual machine, which can operate multiple operating systems that are running simultaneously and connected to the internet. DoS/DDoS attacks are cyber-attacks that can threaten the telecommunications sector because these attacks cause services to be disrupted and be difficult to access. There are several software tools for monitoring abnormal activities on the network, such as IDS Snort and IDS Suricata. From previous studies, IDS Suricata is superior to IDS Snort version 2 because IDS Suricata already supports multi-threading, while IDS Snort version 2 still only supports single-threading. This paper aims to conduct tests on IDS Snort version 3.0 which already supports multi-threading and IDS Suricata. This research was carried out on a virtual machine with 1 core, 2 core, and 4 core processor settings for CPU, memory, and capture packet attacks on IDS Snort version 3.0 and IDS Suricata. The attack scenario is divided into 2 parts: DoS attack scenario using 1 physical computer, and DDoS attack scenario using 5 physical computers. Based on overall testing, the results are: In general, IDS Snort version 3.0 is better than IDS Suricata. This is based on the results when using a maximum of 4 core processor, in which IDS Snort version 3.0 CPU usage is stable at 55% - 58%, a maximum memory of 3,000 MB, can detect DoS attacks with 27,034,751 packets, and DDoS attacks with 36,919,395 packets. Meanwhile, different results were obtained by IDS Suricata, in which CPU usage is better compared to IDS Snort version 3.0 with only 10% - 40% usage, and a maximum memory of 1,800 MB. However, the capabilities of detecting DoS attacks are smaller with 3,671,305 packets, and DDoS attacks with a total of 7,619,317 packets on a TCP Flood attack test.

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 (IDS) was an application which was aimed to monitor network activity or system and it could find if there was a dangerous operation. Implementation of IDS on Software Define Network architecture (SDN) has drawbacks. IDS on SDN architecture might decreasing network Quality of Service (QoS). So the network could not provide services to the existing network traffic. Throughput, delay and packet loss were important parameters of QoS measurement. Snort IDS and bro IDS were tools in the application of IDS on the network. Both had differences, one of which was found in the detection method. Snort IDS used a signature based detection method while bro IDS used an anomaly based detection method. The difference between them had effects in handling the network traffic through it. In this research, we compared both tools. This comparison are done with testing parameters such as throughput, delay, packet loss, CPU usage, and memory usage. From this test, it was found that bro outperform snort IDS for throughput, delay , and packet loss parameters. However, CPU usage and memory usage on bro requires higher resource than snort.

A wide variety of security software systems need to be integrated into a Security Orchestration Platform (SecOrP) to streamline the processes of defending against and responding to cybersecurity attacks. Lack of interpretability and interoperability among security systems are considered the key challenges to fully leverage the potential of the collective capabilities of different security systems. The processes of integrating security systems are repetitive, time-consuming and error-prone; these processes are carried out manually by human experts or using ad-hoc methods. To help automate security systems integration processes, we propose an Ontology-driven approach for Security OrchestrAtion Platform (OnSOAP). The developed solution enables interpretability, and interoperability among security systems, which may exist in operational silos. We demonstrate OnSOAP's support for automated integration of security systems to execute the incident response process with three security systems (Splunk, Limacharlie, and Snort) for a Distributed Denial of Service (DDoS) attack. The evaluation results show that OnSOAP enables SecOrP to interpret the input and output of different security systems, produce error-free integration details, and make security systems interoperable with each other to automate and accelerate an incident response process.

In the paper is presented implementation of a system for detecting intrusion actions. An implementation of intrusion detection systems (IDS), their architectures, and intrusion detection methods are investigated. Analyzed are methods for SNORT (IDS) bandwidth traffic analysis in intrusion detection and prevention systems. The main requirements for Installation and configuration of the system are also discussed. Then the configuration of the firewall policy and specifics there, are also presented. It is also described the database structure, the operating modes, and analysis of the rules. Two of the most commonly implemented attacks and model for defense against them is proposed.

Deep packet inspection (DPI) is a critical component to prevent intrusion detection. This requires a detailed analysis of each network packet header and body. Although this is often done on dedicated high-power servers in most networked systems, mobile systems could potentially be vulnerable to attack if utilized on an unprotected network. In this case, having DPI hardware on the mobile system would be highly beneficial. Unfortunately, DPI hardware is generally area and power consuming, making its implementation difficult in mobile systems. We developed a memristor crossbar-based approach, inspired by memristor crossbar neuromorphic circuits, for a low-power, low-area, and high-throughput DPI system that examines both the header and body of a packet. Two key types of circuits are presented: static pattern matching and regular expression circuits. This system is able to reduce execution time and power consumption due to its high-density grid and massive parallelism. Independent searches are performed using low-power memristor crossbar arrays giving rise to a throughput of 160Gbps with no loss in the classification accuracy.

This research was an experimental analysis of the Intrusion Detection Systems(IDS) with Honey Pot conducting through a study of using Honey Pot in tricking, delaying or deviating the intruder to attack new media broadcasting server for IPTV system. Denial of Service(DoS) over wire network and wireless network consisted of three types of attacks: TCP Flood, UDP Flood and ICMP Flood by Honey Pot, where the Honeyd would be used. In this simulation, a computer or a server in the network map needed to be secured by the inactivity firewalls or other security tools for the intrusion of the detection systems and Honey Pot. The network intrusion detection system used in this experiment was SNORT (www.snort.org) developed in the form of the Open Source operating system-Linux. The results showed that, from every experiment, the internal attacks had shown more threat than the external attacks. In addition, attacks occurred through LAN network posted 50% more disturb than attacks occurred on WIFI. Also, the external attacks through LAN posted 95% more attacks than through WIFI. However, the number of attacks presented by TCP, UDP and ICMP were insignificant. This result has supported the assumption that Honey Pot was able to help detecting the intrusion. In average, 16% of the attacks was detected by Honey Pot in every experiment.

With an aim of provisioning fast, reliable and low cost services to the users, the cloud-computing technology has progressed leaps and bounds. But, adjacent to its development is ever increasing ability of malicious users to compromise its security from outside as well as inside. The Network Intrusion Detection System (NIDS) techniques has gone a long way in detection of known and unknown attacks. The methods of detection of intrusion and deployment of NIDS in cloud environment are dependent on the type of services being rendered by the cloud. It is also important that the cloud administrator is able to determine the malicious intensions of the attackers and various methods of attack. In this paper, we carry out the integration of NIDS module and Honeypot Networks in Cloud environment with objective to mitigate the known and unknown attacks. We also propose method to generate and update signatures from information derived from the proposed integrated model. Using sandboxing environment, we perform dynamic malware analysis of binaries to derive conclusive evidence of malicious attacks.

Multiple string matching plays a fundamental role in network intrusion detection systems. Automata-based multiple string matching algorithms like AC, SBDM and SBOM are widely used in practice, but the huge memory usage of automata prevents them from being applied to a large-scale pattern set. Meanwhile, poor cache locality of huge automata degrades the matching speed of algorithms. Here we propose a space-efficient multiple string matching algorithm BVM, which makes use of bit-vector and succinct hash table to replace the automata used in factor-searching-based algorithms. Space complexity of the proposed algorithm is O(rm2 + ΣpϵP |p|), that is more space-efficient than the classic automata-based algorithms. Experiments on datasets including Snort, ClamAV, URL blacklist and synthetic rules show that the proposed algorithm significantly reduces memory usage and still runs at a fast matching speed. Above all, BVM costs less than 0.75% of the memory usage of AC, and is capable of matching millions of patterns efficiently.

Multiple string matching plays a fundamental role in network intrusion detection systems. Automata-based multiple string matching algorithms like AC, SBDM and SBOM are widely used in practice, but the huge memory usage of automata prevents them from being applied to a large-scale pattern set. Meanwhile, poor cache locality of huge automata degrades the matching speed of algorithms. Here we propose a space-efficient multiple string matching algorithm BVM, which makes use of bit-vector and succinct hash table to replace the automata used in factor-searching-based algorithms. Space complexity of the proposed algorithm is O(rm2 + ΣpϵP |p|), that is more space-efficient than the classic automata-based algorithms. Experiments on datasets including Snort, ClamAV, URL blacklist and synthetic rules show that the proposed algorithm significantly reduces memory usage and still runs at a fast matching speed. Above all, BVM costs less than 0.75% of the memory usage of AC, and is capable of matching millions of patterns efficiently.