Energy-efficient pipelined bloom filters for network intrusion detection

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Low-power bloom filter architecture for deep packet inspection

Bloom filters are frequently used to identify malicious content like viruses in high speed networks. However, architectures proposed to implement Bloom filters are not power efficient. In this letter, we propose a new Bloom filter architecture that exploits the well-known pipelining technique. Through power analysis we show that pipelining can reduce the power consumption of Bloom filters up to 90%, which leads to the energy-efficient implementation of intrusion detection systems. © 2006 IEEE

Increasing the power efficiency of Bloom filters for network string matching

Although software based techniques are widely accepted in computer security systems, there is a growing interest to utilize hardware opportunities in order to compensate for the network bandwidth increases. Recently, hardware based virus protection systems have started to emerge. These type of hardware systems work by identifying the malicious content and removing it from the network streams. In principle, they make use of string matching. Bit by bit, they compare the virus signatures with the bit strings in the network. The Bloom filters are ideal data structures for string matching. Nonetheless, they consume large power when many of them used in parallel to match different virus signatures. In this paper, we propose a new type of Bloom filter architecture which exploits well-known pipelining technique. © 2006 IEEE

A Pre-Merger Stage Galaxy Cluster: Abell 3733

The galaxy cluster Abell 3733 (A3733) is a very suitable candidate in addressing dynamical processes throughout galaxy cluster mergers. This study shows structural analysis results of A3733 (z = 0.038) based on X-ray and optical data. According to X-ray luminosity map, A3733 hosts two sub-structures separated in the sky by $\sim$ 0.25 Mpc, and the two distinct clumps are located in the East (A3733E) and the West (A3733W) directions. Both sub-structures are centred on two different brightest cluster galaxies (BCGs), and the X-ray and optical centroids of both BCGs substantially coincide with each other. The intracluster medium (ICM) temperatures of the sub-structures are estimated to be 2.79 keV for A3733E and 3.28 keV for A3733W. Both sub-structures are found to be hosting cool central gas (kT $\sim$ 1.5-2.5 keV) surrounded by hotter gas (kT $\sim$ 3.0-3.5 keV). Besides, the X-ray concentration parameters are found to be c $\sim$ 0.3 for each sub-structure. These results indicate the existence of cool centres for both sub-structures. The optical density map reveals a crowded galaxy population within the vicinity of A3733W. The high probable (% 88.2) dynamical binding model of A3733 suggests that the cores of sub-structures have a 3D separation of 0.27 Mpc and will collide in 0.14 Gyr with the relative in-falling velocity of 1936 km s$^{-1}$. As a conclusion, this study demonstrates some evidence suggesting that the A3733 system is in the pre-merger state.Comment: 9 pages, 7 Figures, published by MNRA

Adaptive Light Control System

An adaptive traffic light system for crossroads is to be developed with the control being the data obtained through fixed cameras attached to the light system. The control itself is to be adaptive as there is no need for collecting data during the time when there is no traffic at all. Thus the problem is to collect data adaptively and control the light system accordingly. The idea, of course, is not to have people wait for unnecessary amount of time along the way, while there is no traffic across roads. Though looks rather reasonable, a very good adaptive strategy and an accompanying algorithm need to be developed. The study group is asked for such an algorithm

Emitter Location Finding using Particle Swarm Optimization

Using several spatially separated receivers, nowadays positioning techniques, which are implemented to determine the location of the transmitter, are often required for several important disciplines such as military, security, medical, and commercial applications. In this study, localization is carried out by particle swarm optimization using time difference of arrival. In order to increase the positioning accuracy, time difference of arrival averaging based two new methods are proposed. Results are compared with classical algorithms and Cramer-Rao lower bound which is the theoretical limit of the estimation error

CMF-DFE Based Adaptive Blind Equalization Using Particle Swarm Optimization

The channel matched filter (CMF) is the optimum receiver providing the maximum signal to noise ratio (SNR) for the frequency selective channels. The output intersymbol interference (ISI) profile of the CMF convolved by the channel can be blindly obtained by using the autocorrelation of the received signal. Therefore, the inverse of the autocorrelation function can be used to equalize the channel passed through its own CMF. The only missing part to complete the proposed blind operation is the CMF coefficients. Therefore, in this work, the best training algorithm investigation is subjected for blind estimation of the CMF coefficients. The proposed method allows using more effective training algorithms for blind equalizations. However, the expected high performance training is obtained when the swarm intelligence is used. Unlike the stochastic gradient algorithms, the particle swarm optimization (PSO) is known to have fast convergence because its performance is independent of the characteristics of the systems used. The obtained mean square error (MSE) and bit error rate (BER) performances are promising for high performance real-time systems as an alternative to non-blind equalization techniques