Ternary content addressable memory for longest prefix matching based on random access memory on field programmable gate array

Conventional ternary content addressable memory (TCAM) provides access to stored data, which consists of '0', '1' and ‘don't care’, and outputs the matched address. Content lookup in TCAM can be done in a single cycle, which makes it very important in applications such as address lookup and deep-packet inspection. This paper proposes an improved TCAM architecture with fast update functionality. To support longest prefix matching (LPM), LPM logic are needed to the proposed TCAM. The latency of the proposed LPM logic is dependent on the number of matching addresses in address prefix comparison. In order to improve the throughput, parallel LPM logic is added to improve the throughput by 10× compared to the one without. Although with resource overhead, the cost of throughput per bit is less as compared to the one without parallel LPM logic

Desain Mobile Unit Instalasi Pengolahan Air Minum untuk Kondisi Darurat Bencana Banjir Menggunakan Membran Ultrafiltrasi

Banjir merupakan bencana alam yang paling sering terjadi di Indonesia, yaitu terdapat 5.051 kejadian sejak tahun 1.815 hingga tahun 2013. Penyediaan air minum yang aman dapat menjadi sumber masalah kesehatan utama setelah bencana alam, namun menyediakan air minum untuk penduduk yang terkena bencana adalah aktivitas yang menantang karena kontaminasi yang parah dan kurangnya akses terhadap infrastruktur. Sebuah sistem pengolahan air minum onsite untuk penduduk yang terkena bencana adalah solusi yang lebih berkelanjutan daripada mengangkut air minum kemasan, sehingga pengolahan air minum mobile merupakan salah satu solusi yang tepat untuk kondisi banjir. Produksi air minum secara mobile cocok menggunakan membran karena sifatnya yang modular dan prosesnya sederhana. Dalam kaitan ini maka perlu direncanakan desain instalasi pengolahan air minum mobile untuk kondisi darurat bencana banjir dengan menggunakan membran. Membran yang digunakan adalah membran ultrafiltrasi. Membran ultrafiltrasi mempunyai kelebihan yaitu dapat menahan atau menyaring makromolekul (bakteri, ragi), namun tekanan yang dibutuhan rendah sehingga energi yang diperlukan rendah. Pada perencanaan ini juga direncanakan unit-unit pengolahan sebelum dan sesudah membran ultrafiltrasi sehingga kualitas effluent memenuhi baku mutu sesuai dengan PERMENKES RI No. 492/MEN.KES/PER/IV/2010

Online Data Stream Learning and Classification with Limited Labels

Mining data streams such as Internet traffic andnetwork security is complex. Due to the difficulty of storage, datastreams analytics need to be done in one scan. This limits thetime to observe stream feature and hence, further complicatesthe data mining processes. Traditional supervised data miningwith batch training natural is not suitable to mine data streams.This paper proposes an algorithm for online data streamclassification and learning with limited labels using selective selftrainingsemi-supervised classification. The experimental resultsshow it is able to achieve up to 99.6% average accuracy for 10%labeled data and 98.6% average accuracy for 1% labeled data. Itcan classify up to 34K instances per second

Pre-filters in-transit malware packets detection in the network

Conventional malware detection systems cannot detect most of the new malware in the network without the availability of their signatures. In order to solve this problem, this paper proposes a technique to detect both metamorphic (mutated malware) and general (non-mutated) malware in the network using a combination of known malware sub-signature and machine learning classification. This network-based malware detection is achieved through a middle path for efficient processing of non-malware packets. The proposed technique has been tested and verified using multiple data sets (metamorphic malware, non-mutated malware, and UTM real traffic), this technique can detect most of malware packets in the network-based before they reached the host better than the previous works which detect malware in host-based. Experimental results showed that the proposed technique can speed up the transmission of more than 98% normal packets without sending them to the slow path, and more than 97% of malware packets are detected and dropped in the middle path. Furthermore, more than 75% of metamorphic malware packets in the test dataset could be detected. The proposed technique is 37 times faster than existing technique

Online Data Stream Learning and Classification with Limited Labels

Mining data streams such as Internet traffic andnetwork security is complex. Due to the difficulty of storage, datastreams analytics need to be done in one scan. This limits thetime to observe stream feature and hence, further complicatesthe data mining processes. Traditional supervised data miningwith batch training natural is not suitable to mine data streams.This paper proposes an algorithm for online data streamclassification and learning with limited labels using selective selftrainingsemi-supervised classification. The experimental resultsshow it is able to achieve up to 99.6% average accuracy for 10%labeled data and 98.6% average accuracy for 1% labeled data. Itcan classify up to 34K instances per second

Reconfigurable Logic Embedded Architecture of Support Vector Machine Linear Kernel

Support Vector  Machine  (SVM) is a linear  binary classifier  that  requires a  kernel  function  to  handle  non-linear problems.  Most  previous  SVM  implementations for  embedded systems  in literature were  built  targeting a certain  application; where analyses were done through comparison  with software im- plementations only. The impact  of different  application datasets towards  SVM hardware performance were not analyzed.  In this work,  we propose  a parameterizable linear  kernel  architecture that  is fully pipelined.  It  is prototyped and  analyzed  on Altera Cyclone  IV  platform   and  results  are  verified  with  equivalent software  model.  Further analysis  is  done  on  determining the effect  of  the  number of  features   and  support   vectors  on  the performance of the  hardware architecture. From  our  proposed linear  kernel  implementation, the number of features  determine the maximum  operating frequency  and amount  of logic resource utilization,  whereas  the  number of support   vectors  determines the  amount  of on-chip  memory  usage  and  also the  throughput of the system

Incremental High Throughput Network Traffic Classifier

Today’s network traffic are dynamic and fast. Con-ventional network traffic classification based on flow feature and data mining are not able to process traffic efficiently. Hardware based network traffic classifier is needed to be adaptable to dynamic network state and to provide accurate and updated classification at high speed. In this paper, a hardware architecture of online incremental semi-supervised algorithm is proposed. The hardware architecture is designed such that it is suitable to be incorporated in NetFPGA reference switch design. The experimental results on real datasets show that with only 10% of labeled data, the proposed architecture can perform online classification of network traffic at 1Gbps bitrate with 91% average accuracy without loosing any flows

Rejecting spam during SMTP sessions

This paper analyzes a spam rejection scheme at Simple Mail Transfer Protocol (SMTP) sessions. This scheme utilizes a layer-3 e-mail pre-classification technique to estimate e-mail classes before an SMTP session ends. We study the spam rejection scheme using discrete-time Markov chain analysis and analyze the performance of the proposed scheme under different e-mail traffic loads and service capacities. The proposed scheme reduces the e-mail volume to be queued and processed by e-mail servers. This reduces non-spam e-mail queuing delay and loss, and protects e-mail servers from being overloaded by spam traffic

Online peer-to-peer traffic identification based on complex events processing of traffic event signatures

Peer-to-Peer (P2P) applications are bandwidth-heavy and lead to network congestion. The masquerading nature of P2P traffic makes conventional methods of its identification futile. In order to manage and control P2P traffic efficiently preferably in the network, it is necessary to identify such traffic online and accurately. This paper proposes a technique for online P2P identification based on traffic events signatures. The experimental results show that it is able to identify P2P traffic on the fly with an accuracy of 97.7%, precision of 98% and recall of 99.2%