Efficient Implementation of IEEE 802.11i Wi-Fi Security (WPA2-PSK) Standard Using FPGA

The rationale behind the thesis was to design efficient implementations of cryptography algorithms used for Wi-Fi Security as per IEEE 802.11i Wi-Fi Security (WPA2-PSK) standard. The focus was on software implementation of Password-Based Key Derivation Function 2 (PBKDF2) using Keyed-Hash Message Authentication Code (HMAC)-SHA1, which is used for authentication, and, hardware implementation of AES-256 cipher, which is used for data confidentiality. In this thesis, PBKDF2 based on HMAC-SHA1 was implemented on software using C programming language, and, AES-256 was implemented on hardware using Verilog HDL. The overall implementation was designed and tested on Nexys4 FPGA board. The performance of the implementation was compared with other existing designs. Latency (us) was used as the performance metric for PBKDF2, whereas, throughput (Gb/s), resource utilization (Number of Slices), efficiency (Kb/s per slice) and latency (ns) were used as performance metrics for AES-256. MRF24WG0MA PMOD Wi-Fi module was the 2.4 GHz Wi-Fi module which was interfaced with Nexys4 FPGA board for wireless communication. When the correct security credentials were entered in the implemented system interfaced to the Wi-Fi module, it was successfully authenticated by a 2.4 GHz wireless router (or mobile hotspot) configured to work in WPA2-PSK security mode. Once this system was authenticated, the implemented AES-256 cipher within the system was used to provide a layer of encryption over the data being communicated in the network

A Novel Hash Scheme Based on SNP-PLCM

AbstractBy combining the traditional iteration structure of Hash function with the dynamic S-boxes, a novel keyed Hash function is presented. The proposed approach can give a chaotic Hash value by means of the lookup table of functions and chaotic dynamic S-box. Compared with the existing chaotic Hash functions, this method improves computational performance of Hash system by using the chaotic S-box substitution. Theoretical and experimental results show that the proposed method has not only strong one way property, sensitivity to initial conditions and chaotic system's parameters, but also high speed

Searchable Symmetric Encryption and its applications

In the age of personalized advertisement and online identity profiles, people’s personal information is worth more to corporations than ever. Storing data in the cloud is increasing in popularity due to bigger file sizes and people just storing more information digitally. The leading cloud storage providers require insight into what users store on their servers. This forces users to trust their cloud storage provider not to misuse their information. This opens the possibility that private information is sold to hackers or is made publicly available on the internet. However, the more realistic case is that the service provider sells or misuses your metadata for use in personalized advertisements or other, less apparent purposes. This thesis will explore Searchable Sym- metric Encryption (SSE) algorithms and how we can utilize them to make a more secure cloud storage serviceMasteroppgave i informatikkINF399MAMN-PROGMAMN-IN

The Case for Learned Index Structures

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible

Secure Remote Control and Configuration of FPX Platform in Gigabit Ethernet Environment

Because of its flexibility and high performance, reconfigurable logic functions implemented on the Field-programmable Port Extender (FPX ) are well suited for implementing network processing such as packet classification, filtering and intrusion detection functions. This project focuses on two key aspects of the FPX system. One is providing a Gigabit Ethernet interface by designing logic for a FPGA which is located on a line card. Address Resolution Protocol (ARP) packets are handled in hardware and Ethernet frames are processed and transformed into cells suitable for standard FPX application. The other effort is to provide a secure channel to enable remote control and configuration of the FPX system through public internet. A suite of security hardware cores were implemented that include the Advanced Encryption Standard (AES), Triple Data Encryption Standard (3DES), Hashed Message Authentication Code (HMAC), Message Digest Version 5 (MD5) and Secure Hash Algorithm (SHA-1). An architecture and an associated protocol have been developed which provide a secure communication channel between a control console and a hardware-based reconfigurable network node. This solution is unique in that it does not require a software process to run on the network stack, so that it has both higher performance and prevents the node from being hacked using traditional vulnerabilities found in common operating systems. The mechanism can be applied to the design and implementation of re-motely managed FPX systems. A hardware module called the Secure Control Packet Processor (SCPP) has been designed for a FPX based firewall. It utilizes AES or 3DES in Error Propagation Block Chaining (EPBC) mode to ensure data confidentiality and data integrity. There is also an authenticated engine that uses HMAC. to generate the acknowledgments. The system can protect the FPX system against attacks that may be sent over the control and configuration channel. Based on this infrastructure, an enhanced protocol is addressed that provides higher efficiency and can defend against replay attack. To support that, a control cell encryption module was designed and tested in the FPX system

Visual Search at eBay

In this paper, we propose a novel end-to-end approach for scalable visual search infrastructure. We discuss the challenges we faced for a massive volatile inventory like at eBay and present our solution to overcome those. We harness the availability of large image collection of eBay listings and state-of-the-art deep learning techniques to perform visual search at scale. Supervised approach for optimized search limited to top predicted categories and also for compact binary signature are key to scale up without compromising accuracy and precision. Both use a common deep neural network requiring only a single forward inference. The system architecture is presented with in-depth discussions of its basic components and optimizations for a trade-off between search relevance and latency. This solution is currently deployed in a distributed cloud infrastructure and fuels visual search in eBay ShopBot and Close5. We show benchmark on ImageNet dataset on which our approach is faster and more accurate than several unsupervised baselines. We share our learnings with the hope that visual search becomes a first class citizen for all large scale search engines rather than an afterthought.Comment: To appear in 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017. A demonstration video can be found at https://youtu.be/iYtjs32vh4