2 research outputs found
Performance Evaluation of Network Anomaly Detection Systems
Nowadays, there is a huge and growing concern about security in information and communication
technology (ICT) among the scientific community because any attack or anomaly in
the network can greatly affect many domains such as national security, private data storage,
social welfare, economic issues, and so on. Therefore, the anomaly detection domain is a broad
research area, and many different techniques and approaches for this purpose have emerged
through the years.
Attacks, problems, and internal failures when not detected early may badly harm an
entire Network system. Thus, this thesis presents an autonomous profile-based anomaly detection
system based on the statistical method Principal Component Analysis (PCADS-AD). This
approach creates a network profile called Digital Signature of Network Segment using Flow Analysis
(DSNSF) that denotes the predicted normal behavior of a network traffic activity through
historical data analysis. That digital signature is used as a threshold for volume anomaly detection
to detect disparities in the normal traffic trend. The proposed system uses seven traffic flow
attributes: Bits, Packets and Number of Flows to detect problems, and Source and Destination IP
addresses and Ports, to provides the network administrator necessary information to solve them.
Via evaluation techniques, addition of a different anomaly detection approach, and
comparisons to other methods performed in this thesis using real network traffic data, results
showed good traffic prediction by the DSNSF and encouraging false alarm generation and detection
accuracy on the detection schema.
The observed results seek to contribute to the advance of the state of the art in methods
and strategies for anomaly detection that aim to surpass some challenges that emerge from
the constant growth in complexity, speed and size of today’s large scale networks, also providing
high-value results for a better detection in real time.Atualmente, existe uma enorme e crescente preocupação com segurança em tecnologia
da informação e comunicação (TIC) entre a comunidade cientÃfica. Isto porque qualquer
ataque ou anomalia na rede pode afetar a qualidade, interoperabilidade, disponibilidade, e integridade
em muitos domÃnios, como segurança nacional, armazenamento de dados privados,
bem-estar social, questões econômicas, e assim por diante. Portanto, a deteção de anomalias
é uma ampla área de pesquisa, e muitas técnicas e abordagens diferentes para esse propósito
surgiram ao longo dos anos.
Ataques, problemas e falhas internas quando não detetados precocemente podem prejudicar
gravemente todo um sistema de rede. Assim, esta Tese apresenta um sistema autônomo
de deteção de anomalias baseado em perfil utilizando o método estatÃstico Análise de Componentes
Principais (PCADS-AD). Essa abordagem cria um perfil de rede chamado Assinatura Digital
do Segmento de Rede usando Análise de Fluxos (DSNSF) que denota o comportamento normal
previsto de uma atividade de tráfego de rede por meio da análise de dados históricos. Essa
assinatura digital é utilizada como um limiar para deteção de anomalia de volume e identificar
disparidades na tendência de tráfego normal. O sistema proposto utiliza sete atributos de fluxo
de tráfego: bits, pacotes e número de fluxos para detetar problemas, além de endereços IP e
portas de origem e destino para fornecer ao administrador de rede as informações necessárias
para resolvê-los.
Por meio da utilização de métricas de avaliação, do acrescimento de uma abordagem
de deteção distinta da proposta principal e comparações com outros métodos realizados nesta
tese usando dados reais de tráfego de rede, os resultados mostraram boas previsões de tráfego
pelo DSNSF e resultados encorajadores quanto a geração de alarmes falsos e precisão de deteção.
Com os resultados observados nesta tese, este trabalho de doutoramento busca contribuir
para o avanço do estado da arte em métodos e estratégias de deteção de anomalias,
visando superar alguns desafios que emergem do constante crescimento em complexidade, velocidade
e tamanho das redes de grande porte da atualidade, proporcionando também alta
performance. Ainda, a baixa complexidade e agilidade do sistema proposto contribuem para
que possa ser aplicado a deteção em tempo real
Designing substitution boxes based on chaotic map and globalized firefly algorithm
Cipher strength mainly depends on the robust structure and a well-designed interaction of the components in its framework. A significant component of a cipher system, which has a significant influence on the strength of the cipher system, is the substitution box or S-box. An S-box is a vital and most essential component of the cipher system due to its direct involvement in providing the system with resistance against certain known and potential cryptanalytic attacks. Hence, research in this area has increased since the late 1980s, but there are still several issues in the design and analysis of the S-boxes for cryptography purposes. Therefore, it is not surprising that the design of suitable S-boxes attracts a lot of attention in the cryptography community. Nonlinearity, bijectivity, strict avalanche criteria, bit independence criteria, differential probability, and linear probability are the major required cryptographic characteristics associated with a strong S-box. Different cryptographic systems requiring certain levels of these security properties. Being that S- boxes can exhibit a certain combination of cryptographic properties at differing rates, the design of a cryptographically strong S-box often requires the establishment of a trade-off between these properties when optimizing the property values. To date, many S-boxes designs have been proposed in the literature, researchers have advocated the adoption of metaheuristic based S-boxes design. Although helpful, no single metaheuristic claim dominance over their other countermeasure. For this reason, the research for a new metaheuristic based S-boxes generation is still a useful endeavour. This thesis aim to provide a new design for 8 × 8 S-boxes based on firefly algorithm (FA) optimization. The FA is a newly developed metaheuristic algorithm inspired by fireflies and their flash lighting process. In this context, the proposed algorithm utilizes a new design for retrieving strong S- boxes based on standard firefly algorithm (SFA). Three variations of FA have been proposed with an aim of improving the generated S-boxes based on the SFA. The first variation of FA is called chaotic firefly algorithm (CFA), which was initialized using discrete chaotic map to enhance the algorithm to start the search from good positions. The second variation is called globalized firefly algorithm (GFA), which employs random movement based on the best firefly using chaotic maps. If a firefly is brighter than its other counterparts, it will not conduct any search. The third variation is called globalized firefly algorithm with chaos (CGFA), which was designed as a combination of CFA initialization and GFA. The obtained result was compared with a previous S-boxes based on optimization algorithms. Overall, the experimental outcome and analysis of the generated S-boxes based on nonlinearity, bit independence criteria, strict avalanche criteria, and differential probability indicate that the proposed method has satisfied most of the required criteria for a robust S-box without compromising any of the required measure of a secure S-box