Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT

Cybersecurity Risk-Responsibility Taxonomy: The Role of Cybersecurity Social Responsibility in Small Enterprises on Risk of Data Breach

With much effort being placed on the physical, procedural, and technological solutions for Information Systems (IS) cybersecurity, research studies tend to focus their efforts on large organizations while overlooking very smaller organizations (below 50 employees). This study addressed the failure to prevent data breaches in Very Small Enterprises (VSEs). VSEs contribute significantly to the economy, however, are more prone to cyber-attacks due to the limited risk mitigations on their systems and low cybersecurity skills of their employees. VSEs utilize Point-of-Sale (POS) systems that are exposed to cyberspace, however, they are often not equipped to prevent complex cybersecurity issues that can result in them being at risk to a data breach. In addition, the absence of federal laws that force VSEs to adhere to standards such as the Payment Card Industry Data Security Standard (PCI-DSS) leaves it up to the discretion of the VSEs to invest in cybersecurity countermeasures aimed at preventing a data breach. Therefore, this study investigated the role that cybersecurity social responsibility plays in motivating the owners of these companies to engage in cybersecurity measures geared at preventing data breaches.This study developed and validated using Subject Matter Experts (SMEs) a cybersecurity risk-responsibility taxonomy using the constructs of VSEs’ owners’ perceived cybersecurity social responsibility (CySR) and risk of data breach (RDB) in order to better understand their level of exposure to a data breach. Exploratory Factor Analysis (EFA) using Principal Component Analysis (PCA) was conducted to extract the significant factors for CySR and RDB. The study also addressed whether there were significant differences in VSEs owners’ perceived RDB and perceived CySR based on three demographics: (1) type of industry, (2) implementation of chip technology, (3) compliance with PCI-DSS. This study was conducted in three phases. Phase 1 utilized a panel of 13 information security SMEs and used the Delphi technique to review characteristics for RDB and CySR that were derived from literature. The results of the expert review were subjected to further validation by means of a pilot study using a small sample of the study population (Phase 2). The pilot study population included 20 organizations with number of employees ranging from less than five to 50 total employees across seven different industries. Phase 3 of the study included the main data collection using the modified survey instrument from the pilot study. 105 VSEs anonymously participated in the main data collection phase of the study. The collected data was subjected data EFA which identified three factors comprised of 15 items for RDB and two factors comprised of 13 items for CySR. In addition, descriptive statistics was obtained and evaluated to determine if significant differences exist in VSEs owners’ perceived RDB based on type of industry, implementation of Europay, Mastercard and Visa (EMV) chip technology and, compliance with PCI-DSS. One-way Analysis of variance (ANOVA) was used to evaluate whether significant differences existed based on the VSEs demographics. The results of the study indicated that there was a statistically significant difference in both RDB and CySR for industry, use of EMV Chip and, PCI-DSS compliance. This study demonstrates that there is a relationship between CySR and cybersecurity and that the CySR instrument could be used to assess cybersecurity practices in small businesses. In addition, this study may assist organizations in understanding and mitigating cybersecurity data breaches

Hardware acceleration for power efficient deep packet inspection

The rapid growth of the Internet leads to a massive spread of malicious attacks like viruses and malwares, making the safety of online activity a major concern. The use of Network Intrusion Detection Systems (NIDS) is an effective method to safeguard the Internet. One key procedure in NIDS is Deep Packet Inspection (DPI). DPI can examine the contents of a packet and take actions on the packets based on predefined rules. In this thesis, DPI is mainly discussed in the context of security applications. However, DPI can also be used for bandwidth management and network surveillance. DPI inspects the whole packet payload, and due to this and the complexity of the inspection rules, DPI algorithms consume significant amounts of resources including time, memory and energy. The aim of this thesis is to design hardware accelerated methods for memory and energy efficient high-speed DPI. The patterns in packet payloads, especially complex patterns, can be efficiently represented by regular expressions, which can be translated by the use of Deterministic Finite Automata (DFA). DFA algorithms are fast but consume very large amounts of memory with certain kinds of regular expressions. In this thesis, memory efficient algorithms are proposed based on the transition compressions of the DFAs. In this work, Bloom filters are used to implement DPI on an FPGA for hardware acceleration with the design of a parallel architecture. Furthermore, devoted at a balance of power and performance, an energy efficient adaptive Bloom filter is designed with the capability of adjusting the number of active hash functions according to current workload. In addition, a method is given for implementation on both two-stage and multi-stage platforms. Nevertheless, false positive rates still prevents the Bloom filter from extensive utilization; a cache-based counting Bloom filter is presented in this work to get rid of the false positives for fast and precise matching. Finally, in future work, in order to estimate the effect of power savings, models will be built for routers and DPI, which will also analyze the latency impact of dynamic frequency adaption to current traffic. Besides, a low power DPI system will be designed with a single or multiple DPI engines. Results and evaluation of the low power DPI model and system will be produced in future

Dagstuhl News January - December 2008

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic