Prediksi serangan pada jaringan komputer surabaya secara real-time menggunakan metode hidden markov model (hmm)

Penyerangan yang akan dilakukan oleh intruder biasanya tidak terduga oleh sistem administrator. Namun kondisi suatu jaringan komputer dapat diprediksi dengan mengamati perubahan urutan kejadian penyerangan. SQL Injection adalah jenis serangan yang akan diprediksi dan dilakukan uji coba untuk Tugas Akhir ini. Pada tugas akhir ini, diterapkan metode pemodelan statistika yaitu Hidden Markov Model (HMM) untuk memprediksi serangan yang mungkin terjadi pada jaringan komputer. Percobaan prediksi dilakukan pada sistem jaringan Institut Teknologi Sepuluh Nopember. Pemakaian metode HMM pada Tugas Akhir ini dikarenakan HMM memiliki karakteristik berupa perubahan pada state internal yang berdasarkan waktu (Markov Model) akan tetapi tidak nampak (hidden) dari luar sistem sehingga tidak dapat langsung dilakukan pengamatan. Namun karena jumlah state internal yang terbatas dan state saat ini (current state) bergantung pada state sebelumnya, sehingga dapat melakukan pengamatan pada suatu yang berhubungan (misalnya variabel yang berkorelasi) dengan state sebelumnya. Dari hasil penerapan metode Hidden Markov Model untuk memprediksi kondisi suatu server atau jaringan komputer dari serangan SQL Injection dihasilkan rata-rata akurasi 25.54%. Sedangkan untuk hasil prediksi serangan DDoS dari data DARPA 2000 rata-rata akurasi adalah 49.04%. Oleh karena itu, penerapan metode Hidden Markov Model dirasa lebih cocok untuk prediksi serangan DDoS dari pada serangan SQL Injection pada suatu server atau jaringan komputer. ========================================================= ============================================ Attacks to be committed by intruders are usually unexpected by the system administrator. However, the condition of a computer network can be predicted from the sequence of attacks. SQL Injection is a type of attack that will be predicted and tested for this Final Project. In this final project, Hidden Markov Model (HMM) is used to predict the attacks that occur on computer network. The system prediction is tested on the network system of Institut Teknologi Sepuluh Nopember. HMM has the characteristic of a change in the internal state based on time (Markov Model) but it is not visible (hidden) from the outside of the system so that it can not be directly observed. However, due to the limited number of internal states and the current state depends on the previous state, so as to observe a corresponding (eg correlated variable) with the previous state. From the implementation of Hidden Markov Model method, it results 25,54% as an average of accuaration prediction for SQLInjection attack. And for DDoS attack it results average of accuration predisction is 49.04%. DDoS attacks are the result of intrusion detection form DARPA 2000. Therefore, implementattion of Hidden Markov Model method is more suitable for DDoS attack prediction than SQL Injection attack on a server or computer network

Comparison of Genomes using High-Performance Parallel Computing

Comparison of the DNA sequences and genes of two genomes can be useful to investigate the common functionalities of the corresponding organisms and get a better understanding of how the genes or groups of genes are organized and involved in several functions. In this paper we use high-performance parallel computing to compare the whole genomes of two organisms, namely Xanthomonas axonopodis pv. citri and Xanthomonas campestris pv. campestris, each with more than five million basepairs. Our purpose is two-fold. First we intend to exploit the high-performance power of a cluster of low-cost microcomputers, propose a parallel solution to this problem, and show its feasibility with implementation and performance results. Second we do additional comparisons of the two genomes by locating and compare not only the homologous genes (expressed in terms of the 20-letter amino acids) but also compare the regions or gaps (in terms of the 4letter DNA nucleotides) between the corresponding homologous genes. We have implemented the proposed comparison strategy to compare the two genomes Xanthomonas axonopodis pv. citri (Xac) and Xanthomonas campestris pv. campestris (Xcc). The parallel platform used is a Beowulf cluster of 64 nodes consisting of low cost microcomputers. Xac has 5,175,554 base pairs and 4,313 proteincoding genes while Xcc has 5,076,187 base pairs and 4,182 protein-coding genes. The parallel solution is based on the dynamic programming approach and presents not only less processing time, but also better quality results as compared to approaches based on Blast and EGG. ∗ Partially supported by CNPq. † Partially supported by FINEP-PRONEX-SAI Proc. No