A survey of intrusion detection techniques in Cloud

Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks

CONDOR: A Hybrid IDS to Offer Improved Intrusion Detection

Intrusion Detection Systems are an accepted and very useful option to monitor, and detect malicious activities. However, Intrusion Detection Systems have inherent limitations which lead to false positives and false negatives; we propose that combining signature and anomaly based IDSs should be examined. This paper contrasts signature and anomaly-based IDSs, and critiques some proposals about hybrid IDSs with signature and heuristic capabilities, before considering some of their contributions in order to include them as main features of a new hybrid IDS named CONDOR (COmbined Network intrusion Detection ORientate), which is designed to offer superior pattern analysis and anomaly detection by reducing false positive rates and administrator intervention

Technologies, Methodologies and Challenges in Network Intrusion Detection and Prevention Systems

This paper presents an overview of the technologies and the methodologies used in Network Intrusion Detection and Prevention Systems (NIDPS). Intrusion Detection and Prevention System (IDPS) technologies are differentiated by types of events that IDPSs can recognize, by types of devices that IDPSs monitor and by activity. NIDPSs monitor and analyze the streams of network packets in order to detect security incidents. The main methodology used by NIDPSs is protocol analysis. Protocol analysis requires good knowledge of the theory of the main protocols, their definition, how each protocol works

Intrusion detection based on behavioral rules for the bytes of the headers of the data units in IP networks

Nowadays, communications through computer networks are of utmost importance for the normal functioning of organizations, worldwide transactions and content delivery. These networks are threatened by all kinds of attacks, leading to traffic anomalies that will eventually disrupt the normal behaviour of the networks, exploring specific breaches on a system component or exhausting network resources. Automatic detection of these network anomalies comprises one of the most important resources for network administration, and Intrusion Detection Systems(IDSs) are amongst the systems responsible for this automatic detection. This dissertation starts from the assumption that it is possible to use machine learning to, consistently and automatically, produce rules for an intrusion detector based on statistics for the first 64 bytes of the headers of Internet Protocol (IP) packets. The survey on the state of the art on related works and currently available IDSs shows that the specific approach taken here is worth to be explored. The decision tree learning algorithm known as C4.5 is identified as a suitable means to produce the aforementioned rules, due to the similarity between their syntax and the tree structure. Several rules are then devised using the ML approach for several attacks. The attacks were the same used in a previous work, in which the rules were devised manually. Both rule sets are then compared to show that, in fact, it is possible to construct rules using the approach taken herein, and that the rules created resorting to the C4.5 algorithm are superior to the ones devised after thorough human analysis of several statistics calculated for the bytes of the headers of the packets. To compare them, each rule set was used to detect intrusions in third party traces containing attacks and in live traffic during simulation of attacks. Most of the attacks producing noticeable impact on the headers were detected by both rule sets, but the results for the third party traces were better in the case of the ML devised rules, providing a clear evidence for the aforementioned assumptions.Hoje em dia, as comunicações através de redes informáticas são da maior importância para o normal funcionamento das organizações, transações mundiais e entrega de conteúdos. Essas redes são ameaçadas por todo o tipo de ataques, levando a anomalias no tráfego, que eventualmente vão corromper o normal funcionamento da rede, explorando falhas específicas num componente de um sistema, ou esgotando os recursos de rede. A deteção automática dessas anomalias de rede é um dos recursos mais importantes para os administradores de rede, e os Sistemas de Deteção de Intrusões estão entre os sistemas responsáveis por essa deteção. Esta dissertação tem como ponto de partida, a assunção que é possível usar mecanismos de aprendizagem automática para produzir, de modo consistente e automático, regras para a deteção de intrusões, baseadas em estatísticas dos primeiros 64 bytes dos cabeçalhos dos pacotes IP. O estudo sobre o estado da arte em trabalhos da área, e em sistemas de deteção atualmente disponíveis, mostrou que o método usado nesta dissertação merece ser estudado. O algoritmo de árvores de decisão C4.5 foi identificado como um meio apropriado para produzir as regras já referidas, devido à semelhança entre a sintaxe das mesmas e a estrutura em árvore deste algoritmo. Várias regras foram depois produzidas para vários tipos de ataque, usando a abordagem por aprendizagem automática. Os ataques tomados em consideração foram os mesmos que foram utilizados num trabalho anterior, em que a regras foram concebidas manualmente. Ambos os conjuntos de regras são depois comparados, para mostrar que, de facto, é possível construir regras através da abordagem utilizada nesta dissertação, e que as regras criadas através do algoritmo C4.5 são superiores às que foram criadas através de análise humana das várias estatísticas calculadas para os bytes dos cabeçalhos dos pacotes. Para as comparar, cada conjunto de regras foi utilizado para detetar intrusões em registos de tráfego disponíveis na Internet contendo ataques e em tráfego em tempo real, durante a simulação de ataques. A maioria dos ataques que produz um forte impacto nos cabeçalhos dos pacotes foi detetado por ambos os conjuntos, mas os resultados com os registos retirados da Internet foram melhores para as regras produzidas por aprendizagem automática, dando uma prova clara para o que foi previamente assumido

Analysis of intrusion prevention methods

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2004Includes bibliographical references (leaves: 105-108)Text in English; Abstract: Turkish and Englishviii, 108 leavesToday, the pace of the technological development and improvements has compelled the development of new and more complex applications. The obligatory of application development in a short time to rapidly changing requirements causes skipping of some stages, mostly the testing stage, in the software development cycle thus, leads to the production of applications with defects. These defects are, later, discovered by intruders to be used to penetrate into computer systems. Current security technologies, such as firewalls, intrusion detection systems, honeypots, network-based antivirus systems, are insufficient to protect systems against those, continuously increasing and rapid-spreading attacks. Intrusion Prevention System (IPS) is a new technology developed to block today.s application-specific, data-driven attacks that spread in the speed of communication. IPS is the evolved and integrated state of the existing technologies; it is not a new approach to network security. In this thesis, IPS products of various computer security appliance developer companies have been analyzed in details. At the end of these analyses, the requirements of network-based IPSs have been identified and an architecture that fits those requirements has been proposed. Also, a sample network-based IPS has been developed by modifying the open source application Snort

Holistic Network Defense: Fusing Host and Network Features for Attack Classification

This work presents a hybrid network-host monitoring strategy, which fuses data from both the network and the host to recognize malware infections. This work focuses on three categories: Normal, Scanning, and Infected. The network-host sensor fusion is accomplished by extracting 248 features from network traffic using the Fullstats Network Feature generator and from the host using text mining, looking at the frequency of the 500 most common strings and analyzing them as word vectors. Improvements to detection performance are made by synergistically fusing network features obtained from IP packet flows and host features, obtained from text mining port, processor, logon information among others. In addition, the work compares three different machine learning algorithms and updates the script required to obtain network features. Hybrid method results outperformed host only classification by 31.7% and network only classification by 25%. The new approach also reduces the number of alerts while remaining accurate compared with the commercial IDS SNORT. These results make it such that even the most typical users could understand alert classification messages

Application Adaptive Bandwidth Management Using Real-Time Network Monitoring.

Application adaptive bandwidth management is a strategy for ensuring secure and reliable network operation in the presence of undesirable applications competing for a network’s crucial bandwidth, covert channels of communication via non-standard traffic on well-known ports, and coordinated Denial of Service attacks. The study undertaken here explored the classification, analysis and management of the network traffic on the basis of ports and protocols used, type of applications, traffic direction and flow rates on the East Tennessee State University’s campus-wide network. Bandwidth measurements over a nine-month period indicated bandwidth abuse of less than 0.0001% of total network bandwidth. The conclusion suggests the use of the defense-in-depth approach in conjunction with the KHYATI (Knowledge, Host hardening, Yauld monitoring, Analysis, Tools and Implementation) paradigm to ensure effective information assurance