Biblio

This paper proposes a hardware Self-Organizing Map (SOM) for high dimensional vectors. The proposed SOM is based on nested architecture with pipeline processing. Due to homogeneous modular structure, the nested architecture provides high expandability. The original nested SOM was designed to handle low-dimensional vectors with fully parallel computation, and it yielded very high performance. In this paper, the architecture is extended to handle much higher dimensional vectors by using sequential computation, which requires multiple clocks to process a single vector. To increase the performance, the proposed architecture employs pipeline computation, in which search of winner neuron and weight vector update are carried out simultaneously. Operable clock frequency for the system was 60 MHz, and its throughput reached 15012 million connection updates per second (MCUPS).

Packet classification is a key function in network security systems in SDN, which detect potential threats by matching the packet header bits and a given rule set. It needs to support multi-dimensional fields, large rule sets, and high throughput. Bit Vector-based packet classification methods can support multi-field matching and achieve a very high throughput, However, the range matching is still challenging. To address issue, this paper proposes a Range Supported Bit Vector (RSBV) algorithm for processing the range fields. RSBV uses specially designed codes to store the pre-computed results in memory, and the result of range matching is derived through pipelined Boolean operations. Through a two-dimensional modular architecture, the RSBV can operate at a high clock frequency and line-rate processing can be guaranteed. Experimental results show that for a 1K and 512-bit OpenFlow rule set, the RSBV can sustain a throughput of 520 Million Packets Per Second.

The intensity of DNA figuring will reinforce the current security on frameworks by opening up another probability of a half and half cryptographic framework. Here, we are exhibiting the DNA S-box for actualizing cryptographic algorithm. The DNA based S-Box is designed using vivado software and implemented using Artix-7 device. The main aim is to design the DNA based S-box to increase the security. Also pipelining and parallelism techniques are to be implement in future to increase the speed.

We introduce the strictly in-order core (SIC), a timing-predictable pipelined processor core. SIC is provably timing compositional and free of timing anomalies. This enables precise and efficient worst-case execution time (WCET) and multi-core timing analysis. SIC's key underlying property is the monotonicity of its transition relation w.r.t. a natural partial order on its microarchitectural states. This monotonicity is achieved by carefully eliminating some of the dependencies between consecutive instructions from a standard in-order pipeline design. SIC preserves most of the benefits of pipelining: it is only about 6-7% slower than a conventional pipelined processor. Its timing predictability enables orders-of-magnitude faster WCET and multi-core timing analysis than conventional designs.

FPGA implementation of MD5 hash algorithm is faster than its software counterpart, but a pre-image brute-force attack on MD5 hash still needs 2ˆ(128) iterations theoretically. This work attempts to improve the speed of the brute-force attack on the MD5 algorithm using hardware implementation. A full 64-stage pipelining is done for MD5 hash generation and three architectures are presented for guess password generation. A 32/34/26-instance parallelization of MD5 hash generator and password generator pair is done to search for a password that was hashed using the MD5 algorithm. Total performance of about 6G trials/second has been achieved using a single Virtex-7 FPGA device.

To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platforms, namely social media networks such as Twitter, are capable of aggregating a vast amount of cybersecurity-related sources. To process such information streams, we require scalable and efficient tools capable of identifying and summarizing relevant information for specified assets. This paper presents the processing pipeline of a novel tool that uses deep neural networks to process cybersecurity information received from Twitter. A convolutional neural network identifies tweets containing security-related information relevant to assets in an IT infrastructure. Then, a bidirectional long short-term memory network extracts named entities from these tweets to form a security alert or to fill an indicator of compromise. The proposed pipeline achieves an average 94% true positive rate and 91% true negative rate for the classification task and an average F1-score of 92% for the named entity recognition task, across three case study infrastructures.