Most of the communication protocols in the Industrial Control System (ICS) are vulnerable to cyber-attacks. Initially, the network protocol was designed for reliable performance, and thus did not incorporate data transmission security features. Therefore, ICS requires adequate data transmission security. This paper suggests improving the security of data transmission through a dynamic symmetric four-key-generators system, wherein the system anticipates cyberattacks by generating four keys before encryption. It involves four generators: a random initial key generator, a keystream generator, a key scheduling algorithm generator, and a pseudo-random number algorithm generator. In the receiver section, the system generates three keys before decryption to ensure data confidentiality and to avoid cyberattacks. The test results show that the proposed system key space is ≈22048 bits, meaning that the key is more secure from brute force attacks. As a result, the cipher data have a correlation value of 0.00007. The entropy value is 7.99, indicating that the cipher data is more secure. Also, speed tests show that the processing time still qualifies as real-time.

[http://www.inass.org/2021/2021022835.pdf]

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Agfianto Eko Putra on March 5th, 2021

Program Studi Teknik Elektro, Universitas Muria Kudus mengadakan acara Kuliah Tamu 2020 dengan topik FPGA dan aplikasinya. Nara sumber: Dr. Agfianto Eko Putra, M.Si. (Program Studi S1 Elektronika dan Instrumentasi, Departemen Ilmu Komputer dan Elektronika, Fak. MIPA, Universitas Gadjah Mada, Yogyakarta)

Selamat menonton dan semoga bermanfaat, Terima kasih.

[https://www.youtube.com/watch?v=zo9y-tKsWD8]

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Control system is widely used to monitor and control various industrial processes, such as oil and gas, smart grids, nuclear power plants, water treatment, telecommunication, chemicals, etc. Industrial Control Systems (ICS) require a secure communication network and real-time processing. However, most network protocols are designed only for reliable performance and are not equipped with adequate security systems. This study proposes to increase the security of data transmission but still pay attention to the speed test analysis to fulfill the real-time process. We introduce the BRC4 super encryption method with a combination of Beaufort and RC4 ciphers to improve data security, but still comply with real-time processing. The test results show that the proposed random initial key with at least 16 characters key length has a key space of (2) 128 bits. That means the proposed system is secure from brute forces attacks. The correlation and entropy analysis results for the largest data (IL-3) were 0.0001 and 7.99. Both show that cipher data’s security increases because cipher and plain data are very different and have very high randomness. The speed test results with the longest total time (encryption and decryption) and various key length combinations for IL-1, IL-2, and IL-3 data were 0.0303 seconds, 0.1555 seconds, and 0.7372 seconds, respectively. These results indicate that the proposed system still fulfills the real-time processing.

https://doi.org/10.1109/ISRITI51436.2020.9315339

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Rekaman live Sabtu, 4 Juli 2020

Pada Roundtable ini akan membahas mengenai Digital Signal Processing dan Deep Learning dengan narasumber:

  • Dr. Agfianto Eko Putra (Dosen DIKE, Fak. MIPA, Universitas Gadjah Mada, Ketua Aptikom Daerah Istimewa Yogyakarta) — Pembahasan: digital signal processing untuk kasus speech signal dan seismic dengan MATLAB;
  • M. Octaviano Pratama, M.Kom (Chief Scientist BISA AI) — Pembahasan: klasifikasi low level dan high level speech feature dengan Deep Learning;
klik disini untuk link YOUTUBE-nya atau langsung PLAY saja video-nya. Terima kasih dan semoga bermanfaat.

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In this study, we use Lora-based data communication for communication between boats and database management systems for vessel. The maximum distance from the Lora is 10 km, and in order to be able to monitor fishermen along the fisherman’s coast in fishing, it is necessary to have a multi-gateway Lora to increase fishermen’s tracking coverage. The system is built using multi gateway Lora installed on the coast. Devices built with a Lora client are installed on a boat with GPS input, the technique of sending data on the device to the gateway uses a time base with a 1-minute period. To minimize data on the gateway that intersects the range of other gateways, a time flag is made to determine the data at a certain time to determine the trajectory of the boat. The result this research is system-monitoring vessel, which is successfully built and can provide fishing boats along the coast with long-range coverage. Based on experiment, multi gateway can be implemented by creating an identical ID gateway, the Lora client will broadcast to the nearest gateway. The maximum distance obtained is 1.35Km NLOS and to cover 20 Km of beach length is needed 15 gateways.

[http://doi.org/10.1088/1757-899x/850/1/012035]

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Implementation of LPWAN very large application, such as remote location, agriculture, mining, and transportation. This paper discusses for data format on implementation small vessel tracking for traditional fisherman. data format is very important for tracking system can be monitoring vessel simple. data format can be optimized perform on IoT network, such as delay, bandwidth, range and power consumption energy. Data format with 68 Bytes Strings stream to gateway Lora every 3 seconds. Data format for gateway to Broker server utilize MQTT format with 4-way connection, such as connect, connect ack, publish data and disconnect. Based on experiment, data format can use robust for small vessel and can implemented on this application.

[http://doi.org/10.1088/1742-6596/1450/1/012074]

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by Kurniasari, E., Putra, A.E., and Agoestien, N.G.

Lifting and modulus operations are the main operations on the RSA algorithm which require long computation time. Implementation of the operation of the lift and modulus on hardware devices requiring more resources than other arithmetic operations. Montgomery modular multiplication, the method which can be used to simplify the operation of lift and modulus, is implemented on FPGA, to speed up the computing process. The implementation results in this study, which is done using VHDL on the Xilinx Artix 7 series FPGA, were able to work at a maximum frequency of 133.76 MHz, requiring 17.66% LUT (11,195 of 63,400) and 7.14% of IOBs (15 of 210).

(DOI: 10.1109/ICST47872.2019.9166353)

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by Afianah, N., Putra, A.E., and Dharmawan, A.

The studies related to the synthesis of backpropagation artificial neural network algorithms are still based on the direct synthesis, so it requires an effort to modify the algorithm into hardware language so it can be optimized, synthesized and implemented into the FPGA. The High-Level Synthesis (HLS) is a software compiler which able to convert C specifications into Register Transfer Level (RTL) form, which can be synthesized into FPGAs. So the designer can focus on the optimization of the algorithm itself, including speed and resource optimization. This paper discus the results of the synthesis of backpropagation artificial neural network algorithms using HLS (High-Level Synthesis) software. The C-synthesis results based on the Zynq7000 FPGA showed an accuracy of 96.56%, were able to be clocked with a period of around 9.37 ns, with resource usage of 18% for BRAM_18K, 67% for DSP48E, 25% for FF and 71% for LUT. While the utilization difference is not significant compare to the previous studies, the optimization effort using an HLS tools need to be taken into account.

(DOI: 10.1109/ICST47872.2019.9166209)

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Rangkuti, A.H., Harjoko, A., and Putra, A.E.

This research evolves feature extraction algorithms in overcoming difficulties in classifying batik images that encounter changes in scale and rotation. the algorithm is multiscale and multilevel extended center symmetric local binary pattern (M2ECS-LBP). In utilizing this algorithm using several types of windows to obtain optimal feature extraction results, ranging from the size of 6×6, 9×9, 12 x 12 and 15×15 or a combination of several windows. However, for the use of algorithm carried out sequentially, it also requires a special strategy to obtain optimal image feature extraction results to support performance accuracy in the classification. The results of classification accuracy using K-Nearest neighborhood had reached up until the percentage to 78,4 – 81.7 percent of the image undergoing changes in scale and rotation. However, if the batik image undergoes a change in scale but the rotation is the same then the accuracy percentage can reach 98-99 percent. This algorithm is a very powerful breakthrough with lighter computing techniques than other algorithms. This research can be continued to recognize moving images, expected with maximum accuracy.

[click here]

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Random Forest (RF) is known as one of the best classifiers in many fields. They are parallelizable, fast to train and to predict, robust to outlier, handle unbalanced data, have low bias, and moderate variance. Apart from these advantages, there are still opportunities to increase RF efficiency. The absence of recommendations regarding the number of trees involved in RF ensembles could make the number of trees very large. This can increase the computational complexity of RF. Recommendations for not pruning the decision tree further aggravates the condition. This research attempts to build an efficient RF ensemble while maintaining its accuracy, especially in problem activity. Data collection is performed using an accelerometer sensor on a smartphone device. The data used in this research are collected from five peoples who perform 11 different activities. Each activity is carried out five times to enrich the data. This study uses two steps to improve the efficiency of the classification of the activity: 1) Optimal splitting criteria for activity classification, 2) Measured pruning to limit the tree depth in RF ensemble. The first method in this study can be applied to determine the splitting criteria that are most suitable for the classification problem of activities using Random Forest. In this case, the decision model built using the Gini Index can produce the highest accuracy. The second method proposed in this research successfully builds less complex pruned-tree without reducing its classification accuracy. The research results showed that the method applied to the Random Forest in this study was able to produce a decision model that was simple but yet accurate to classify activity.

[https://dx.doi.org/10.14569/IJACSA.2019.0100658]

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