5,270 research outputs found

    Determining the effectiveness of deceptive honeynets

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    Over the last few years, incidents of network based intrusions have rapidly increased, due to the increase and popularity of various attack tools easily available for download from the Internet. Due to this increase in intrusions, the concept of a network defence known as Honeypots developed. These honeypots are designed to ensnare attackers and monitor their activities. Honeypots use the principles of deception such as masking, mimicry, decoying, inventing, repackaging and dazzling to deceive attackers. Deception exists in various forms. It is a tactic to survive and defeat the motives of attackers. Due to its presence in the nature, deception has been widely used during wars and now in Information Systems. This thesis considers the current state of honeypot technology as well as describes the framework of how to improve the effectiveness of honeypots through the effective use of deception. In this research, a legitimate corporate deceptive network is created using Honeyd (a type of honeypot) which is attacked and improved using empirical learning approach. The data collected during the attacking exercise were analysed, using various measures, to determine the effectiveness of the deception in the honeypot network created using honeyd. The results indicate that the attackers were deceived into believing the honeynet was a real network which instead was a deceptive network

    Incident Prioritisation for Intrusion Response Systems

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    The landscape of security threats continues to evolve, with attacks becoming more serious and the number of vulnerabilities rising. To manage these threats, many security studies have been undertaken in recent years, mainly focusing on improving detection, prevention and response efficiency. Although there are security tools such as antivirus software and firewalls available to counter them, Intrusion Detection Systems and similar tools such as Intrusion Prevention Systems are still one of the most popular approaches. There are hundreds of published works related to intrusion detection that aim to increase the efficiency and reliability of detection, prevention and response systems. Whilst intrusion detection system technologies have advanced, there are still areas available to explore, particularly with respect to the process of selecting appropriate responses. Supporting a variety of response options, such as proactive, reactive and passive responses, enables security analysts to select the most appropriate response in different contexts. In view of that, a methodical approach that identifies important incidents as opposed to trivial ones is first needed. However, with thousands of incidents identified every day, relying upon manual processes to identify their importance and urgency is complicated, difficult, error-prone and time-consuming, and so prioritising them automatically would help security analysts to focus only on the most critical ones. The existing approaches to incident prioritisation provide various ways to prioritise incidents, but less attention has been given to adopting them into an automated response system. Although some studies have realised the advantages of prioritisation, they released no further studies showing they had continued to investigate the effectiveness of the process. This study concerns enhancing the incident prioritisation scheme to identify critical incidents based upon their criticality and urgency, in order to facilitate an autonomous mode for the response selection process in Intrusion Response Systems. To achieve this aim, this study proposed a novel framework which combines models and strategies identified from the comprehensive literature review. A model to estimate the level of risks of incidents is established, named the Risk Index Model (RIM). With different levels of risk, the Response Strategy Model (RSM) dynamically maps incidents into different types of response, with serious incidents being mapped to active responses in order to minimise their impact, while incidents with less impact have passive responses. The combination of these models provides a seamless way to map incidents automatically; however, it needs to be evaluated in terms of its effectiveness and performances. To demonstrate the results, an evaluation study with four stages was undertaken; these stages were a feasibility study of the RIM, comparison studies with industrial standards such as Common Vulnerabilities Scoring System (CVSS) and Snort, an examination of the effect of different strategies in the rating and ranking process, and a test of the effectiveness and performance of the Response Strategy Model (RSM). With promising results being gathered, a proof-of-concept study was conducted to demonstrate the framework using a live traffic network simulation with online assessment mode via the Security Incident Prioritisation Module (SIPM); this study was used to investigate its effectiveness and practicality. Through the results gathered, this study has demonstrated that the prioritisation process can feasibly be used to facilitate the response selection process in Intrusion Response Systems. The main contribution of this study is to have proposed, designed, evaluated and simulated a framework to support the incident prioritisation process for Intrusion Response Systems.Ministry of Higher Education in Malaysia and University of Malay

    Safety and security management through an integrated multidisciplinary model and related integrated technological framework

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    The purpose of this paper is to illustrate a multidisciplinary model for safety and security management (IMMSSM) which can be implemented by means of a suitable Integrated Technological System Framework (ITSF) that can be based on Internet of Things (IoT)/Internet of Everything (IoE), showing also the significant role played by the integration of the elements that compose the model itself, thanks to a proper genetic algorithm studied for the specific context

    A Multi Agent System for Flow-Based Intrusion Detection

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    The detection and elimination of threats to cyber security is essential for system functionality, protection of valuable information, and preventing costly destruction of assets. This thesis presents a Mobile Multi-Agent Flow-Based IDS called MFIREv3 that provides network anomaly detection of intrusions and automated defense. This version of the MFIRE system includes the development and testing of a Multi-Objective Evolutionary Algorithm (MOEA) for feature selection that provides agents with the optimal set of features for classifying the state of the network. Feature selection provides separable data points for the selected attacks: Worm, Distributed Denial of Service, Man-in-the-Middle, Scan, and Trojan. This investigation develops three techniques of self-organization for multiple distributed agents in an intrusion detection system: Reputation, Stochastic, and Maximum Cover. These three movement models are tested for effectiveness in locating good agent vantage points within the network to classify the state of the network. MFIREv3 also introduces the design of defensive measures to limit the effects of network attacks. Defensive measures included in this research are rate-limiting and elimination of infected nodes. The results of this research provide an optimistic outlook for flow-based multi-agent systems for cyber security. The impact of this research illustrates how feature selection in cooperation with movement models for multi agent systems provides excellent attack detection and classification

    A deception based framework for the application of deceptive countermeasures in 802.11b wireless networks

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    The advance of 802.11 b wireless networking has been beset by inherent and in-built security problems. Network security tools that are freely available may intercept network transmissions readily and stealthily, making organisations highly vulnerable to attack. Therefore, it is incumbent upon defending organisations to take initiative and implement proactive defences against common network attacks. Deception is an essential element of effective security that has been widely used in networks to understand attack methods and intrusions. However, little thought has been given to the type and the effectiveness of the deception. Deceptions deployed in nature, the military and in cyberspace were investigated to provide an understanding of how deception may be used in network security. Deceptive network countermeasures and attacks may then be tested on a wireless honeypot as an investigation into the effectiveness of deceptions used in network security. A structured framework, that describes the type of deception and its modus operandi, was utilised to deploy existing honeypot technologies for intrusion detection. Network countermeasures and attacks were mapped to deception types in the framework. This enabled the honeypot to appear as a realistic network and deceive targets in varying deceptive conditions. The investigation was to determine if particular deceptive countermeasures may reduce the effectiveness of particular attacks. The effectiveness of deceptions was measured, and determined by the honeypot\u27s ability to fool the attacking tools used. This was done using brute force network attacks on the wireless honeypot. The attack tools provided quantifiable forensic data from network sniffing, scans, and probes of the wireless honeypot. The aim was to deceive the attack tools into believing a wireless network existed, and contained vulnerabilities that may be further exploited by the naive attacker

    Advanced Topics in Systems Safety and Security

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    This book presents valuable research results in the challenging field of systems (cyber)security. It is a reprint of the Information (MDPI, Basel) - Special Issue (SI) on Advanced Topics in Systems Safety and Security. The competitive review process of MDPI journals guarantees the quality of the presented concepts and results. The SI comprises high-quality papers focused on cutting-edge research topics in cybersecurity of computer networks and industrial control systems. The contributions presented in this book are mainly the extended versions of selected papers presented at the 7th and the 8th editions of the International Workshop on Systems Safety and Security—IWSSS. These two editions took place in Romania in 2019 and respectively in 2020. In addition to the selected papers from IWSSS, the special issue includes other valuable and relevant contributions. The papers included in this reprint discuss various subjects ranging from cyberattack or criminal activities detection, evaluation of the attacker skills, modeling of the cyber-attacks, and mobile application security evaluation. Given this diversity of topics and the scientific level of papers, we consider this book a valuable reference for researchers in the security and safety of systems

    Performance Metrics for Network Intrusion Systems

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    Intrusion systems have been the subject of considerable research during the past 33 years, since the original work of Anderson. Much has been published attempting to improve their performance using advanced data processing techniques including neural nets, statistical pattern recognition and genetic algorithms. Whilst some significant improvements have been achieved they are often the result of assumptions that are difficult to justify and comparing performance between different research groups is difficult. The thesis develops a new approach to defining performance focussed on comparing intrusion systems and technologies. A new taxonomy is proposed in which the type of output and the data scale over which an intrusion system operates is used for classification. The inconsistencies and inadequacies of existing definitions of detection are examined and five new intrusion levels are proposed from analogy with other detection-based technologies. These levels are known as detection, recognition, identification, confirmation and prosecution, each representing an increase in the information output from, and functionality of, the intrusion system. These levels are contrasted over four physical data scales, from application/host through to enterprise networks, introducing and developing the concept of a footprint as a pictorial representation of the scope of an intrusion system. An intrusion is now defined as “an activity that leads to the violation of the security policy of a computer system”. Five different intrusion technologies are illustrated using the footprint with current challenges also shown to stimulate further research. Integrity in the presence of mixed trust data streams at the highest intrusion level is identified as particularly challenging. Two metrics new to intrusion systems are defined to quantify performance and further aid comparison. Sensitivity is introduced to define basic detectability of an attack in terms of a single parameter, rather than the usual four currently in use. Selectivity is used to describe the ability of an intrusion system to discriminate between attack types. These metrics are quantified experimentally for network intrusion using the DARPA 1999 dataset and SNORT. Only nine of the 58 attack types present were detected with sensitivities in excess of 12dB indicating that detection performance of the attack types present in this dataset remains a challenge. The measured selectivity was also poor indicting that only three of the attack types could be confidently distinguished. The highest value of selectivity was 3.52, significantly lower than the theoretical limit of 5.83 for the evaluated system. Options for improving selectivity and sensitivity through additional measurements are examined.Stochastic Systems Lt
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