Acquaintance Management Algorithm Based on the Multi-Class Risk-Cost Analysis for Collaborative Intrusion Detection Network

The collaborative intrusion detection network (CIDN) framework provides collaboration capability among intrusion detection systems (IDS). Collaboration selection is done by an acquaintance management algorithm. A recent study developed an effective acquaintance management algorithm by the use of binary risk analysis and greedy-selection-sort based methods. However, most algorithms do not pay attention to the possibility of wrong responses in multi-botnet attacks. The greedy-based acquaintance management algorithm also leads to a poor acquaintance selection processing time when there is a high number of IDS candidates. The growing number of advanced distributed denial of service (DDoS) attacks make acquaintance management potentially end up with an unreliable CIDN acquaintance list, resulting in low decision accuracy. This paper proposes an acquaintance management algorithm based on multi-class risk-cost analysis and merge-sort selection methods. The algorithm implements merge risk-ordered selection to reduce computation complexity. The simulation result showed the reliability of CIDN in reducing the acquaintance selection processing time decreased and increasing the decision accuracy.

Analysis of Merge Sort and Bubble Sort in Python, PHP, JavaScript, and C language

Computer Science is all about the solving problems with the help of algorithms and sorting is one of the basic operations for any problem solving method. In sorting, the arrangement of data or objects in any particular order is done with the help of algorithms. There is more than one method available and also includes a wide range of choices in a programming language. These languages serve a different purpose in their field of the area but some can be used interchangeably for the same purpose especially for a server-side language like JavaScript can also implement for server-side tasks and right now it is being widely used all over the internet. Here this paper analyzed these languages with merge sort and bubble sort with the languages of the latest public stable versions for an idea of the performance of these languages because they are pretty much interchangeable for different uses in the market the only difference these server side languages have is their architecture. This paper compared these languages’ capabilities with merge sort and bubble sort by executing them and observing them in terms of time by giving them different numbers of inputs. Analytics used an array of 2500, 5000, 7500, and 10000 lengths of an array that passes through these algorithms and noted the execution time to get a better idea of the capabilities of these languages. With this method, observes that in the latest public version of all languages python performs faster in merge sort while JavaScript performs better in bubble sort in executing 10000 inputs.

Efficient External Sorting for Memory-Constrained Embedded Devices with Flash Memory

Embedded devices are ubiquitous in areas of industrial and environmental monitoring, health and safety, and consumer appliances. A common use case is data collection, processing, and performing actions based on data analysis. Although many Internet of Things (IoT) applications use the embedded device simply for data collection, there are benefits to having more data processing done closer to data collection to reduce network transmissions and power usage and provide faster response. This work implements and evaluates algorithms for sorting data on embedded devices with specific focus on the smallest memory devices. In devices with less than 4 KB of available RAM, the standard external merge sort algorithm has limited application as it requires a minimum of three memory buffers and is not flash-aware. The contribution is a memory-optimized external sorting algorithm called no output buffer sort (NOBsort) that reduces the minimum memory required for sorting, has excellent performance for sorted or near-sorted data, and sorts on external memory such as SD cards or raw flash chips. When sorting large datasets, no output buffer sort reduces I/O and execution time by between 20% to 35% compared to standard external merge sort.

Recombinant Sort: N-Dimensional Cartesian Spaced Algorithm Designed from Synergetic Combination of Hashing, Bucket, Counting and Radix Sort

Sorting is an essential operation which is widely used and is fundamental to some very basic day to day utilities like searches, databases, social networks and much more. Optimizing this basic operation in terms of complexity as well as efficiency is cardinal. Optimization is achieved with respect to space and time complexities of the algorithm. In this paper, a novel left-field N-dimensional cartesian spaced sorting method is proposed by combining the best characteristics of bucket sort, counting sort and radix sort, in addition to employing hashing and dynamic programming for making the method more efficient. Comparison between the proposed sorting method and various existing sorting methods like bubble sort, insertion sort, selection sort, merge sort, heap sort, counting sort, bucket sort, etc., has also been performed. The time complexity of the proposed model is estimated to be linear i.e.

Perbandingan Efisiensi Algoritma Sorting dalam Penggunaan Bandwidth

The most used algorithm is the sorting algorithm. There have been many popping sorting algorithms that can be used, in this study researchers took three sorting algorithms namely Insertion Sort, Selection Sort, and Merge Sort. As for this study will analyze the comparison of execution time and memory usage by considering the number of enter data of each algorithm used. The data used in this study is ukhuwah NET network bandwidth usage data connected in the Faculty of Computer Science in the form of double data types. After implementing and analyzing in terms of execution time merge sort algorithm has a faster execution time in sorting data with an average execution time value of 108.593777 ms on the 3000 data count. While in the same amount of data for the most execution time is the Selection Sort algorithm with a large execution time of 144.498144 ms, in terms of memory usage with the amount of data3000 Merge Sort Algorithm has the highest memory usage compared to the other two algorithms which is 21,444 MB while the other two algorithms have a succession of memory usage of 20,837 MB and 20,325MB.

Analisis Perbandingan Efisiensi Algoritma Brute Force dan Divide and Conquer dalam Proses Pengurutan Angka

Abstrak— Perbandingan algoritma dibutuhkan untuk mengetahui tingkat efisiensi suatu algoritma. Penelitian ini membandingkan efisiensi dari dua strategi algoritma sort yang sudah ada yaitu brute force dan divide and conquer. Algoritma brute force yang akan diuji adalah bubble sort dan selection sort. Algoritma divide and conquer yang akan diuji adalah quick sort dan merge sort. Cara yang dilakuakn dalam penelitian ini adalah melakukan tes dengan data sebanyak 50 sampai 100000 untuk setiap algoritma. Tes dilakukan dengan menggunakan bahasa pemrograman JavaScript. Hasil dari penelitian ini adalah algoritma quick sort dengan strategi divide and conquer memiliki efisiensi yang baik serta running time yang cepat dan algoritma bubble sort dengan strategi brute force memiliki efisiensi yang buruk serta running time yang lama. Kata Kunci – Efisiensi, algoritma, brute force, divide and conquer, bubble sort, selection sort, quick sort, merge sort