Managing Process Variants as an Information Resource

Many business solutions provide best practice process templates, both generic as well as for specific industry sectors. However, it is often the variance from template solutions that provide organizations with intellectual capital and competitive differentiation. In this paper, we present a modeling framework that is conducive to constrained variance, by supporting user driven process adaptations. The focus of the paper is on providing a means of utilizing the adaptations effectively for process improvement through effective management of the process variants repository (PVR). In particular, we will provide deliberations towards a facility to provide query functionality for PVR that is specifically targeted for effective search and retrieval of process variants

Enhancement performance of random forest algorithm via one hot encoding for IoT IDS

The random forest algorithm is one of important supervised machine learning (ML) algorithms. In the present paper, the accuracy of the results of the random forest (RF) algorithm has been improved by the use of the One Hot Encoding method. The Intrusion Detection System (IDS) can be defined as a system that can predict security vulnerabilities within network traffic and is located out of range on a network infrastructure. It does not affect the efficiency of the built-in network because it analyzes a copy of the built-in traffic flow and reports results to the administrator by giving alerts. However, since IDS is a listening system only, it cannot take automatic action to prevent an attack or security vulnerability detected from infecting the system, it provides information about the source address to start the break-in, the address of the target and the type of suspected attack. The IoTID20 dataset is used to verify the improved algorithm, where this dataset is having three targets, the proposed system is compared with the state-of-art approaches and shows superiority over them

FANET optimization: a destination path flow model

Closed-loop routing in flying ad hoc networks (FANET) arises as a result of the quick changes of communication links and topology. As such, causing link breakage during information dissemination. This paper proposed a destination path flow model to improve the communication link in FANET. The models utilized Smell Agent Optimization and Particle Swarm Optimization algorithms in managing link establishment between communicating nodes. The modeled scenario depicts the practical application of FANET in media and sports coverage where only one vendor is given the license for live coverage and must relay to other vendors. Three different scenarios using both optimization Algorithms were presented. From the result obtained, the SAO optimizes the bandwidth costs much better than PSO with a percentage improvement of 10.46%, 4.04% and 3.66% with respect to the 1st, 2nd and 3rd scenarios respectively. In the case of communication delay between the FANET nodes, the PSO has a much better communication delay over SAO with percentage improvement of 40.89%, 50.26% and 68.85% in the first, second and third scenarios respectively

Women's role in reproductive health decision making and vulnerability to STD and HIV/AIDS in Ekiti, Nigeria

An exploratory study of women’s role in reproductive decision making in Ekiti shows that women in the state are increasingly taking active decisions on matters affecting their daily lives. More women than ever before believed that they could take decisions on family size, when to have a baby and choice of spacing period. The cultural barrier against short postpartum abstinence appeared to have diminished and sex during lactation was not considered a major cultural and religious taboo. Knowledge of contraception has become universal in recent years, and the majority of women take decisions on the method and timing of family planning. All women who used family planning considered their decision in this regard very important. The ability of women to take decisions on these issues may not only enhance their bargaining power but also reduce their vulnerability to STDs including AIDS from diseased or high-risk partners

Modern and Lightweight Component-based Symmetric Cipher Algorithms: A Review

Information security, being one of the corner stones of network and communication technology, has been evolving tremendously to cope with the parallel evolution of network security threats. Hence, cipher algorithms in the core of the information security process have more crucial role to play here, with continuous need for new and unorthodox designs to meet the increasing complexity of the applications environment that keep offering challenges to the current existing cipher algorithms. The aim of this review is to present symmetric cipher main components, the modern and lightweight symmetric cipher algorithms design based on the components that utilized in cipher design, highlighting the effect of each component and the essential component among them, how the modern cipher has modified to lightweight cipher by reducing the number and size of these components, clarify how these components give the strength for symmetric cipher versus asymmetric of cipher. Moreover, a new classification of cryptography algorithms to four categories based on four factors is presented. Finally, some modern and lightweight symmetric cipher algorithms are selected, presented with a comparison between them according to their components by taking into considerations the components impact on security, performance, and resource requirements

ENSEMBLE MACHINE LEARNING APPROACH FOR IOT INTRUSION DETECTION SYSTEMS

The rapid growth and development of the Internet of Things (IoT) have had an important impact on various industries, including smart cities, the medical profession, autos, and logistics tracking. However, with the benefits of the IoT come security concerns that are becoming increasingly prevalent. This issue is being addressed by developing intelligent network intrusion detection systems (NIDS) using machine learning (ML) techniques to detect constantly changing network threats and patterns. Ensemble ML represents the recent direction in the ML field. This research proposes a new anomaly-based solution for IoT networks utilizing ensemble ML algorithms, including logistic regression, naive Bayes, decision trees, extra trees, random forests, and gradient boosting. The algorithms were tested on three different intrusion detection datasets. The ensemble ML method achieved an accuracy of 98.52% when applied to the UNSW-NB15 dataset, 88.41% on the IoTID20 dataset, and 91.03% on the BoTNeTIoT-L01-v2 dataset

Enhancement performance of random forest algorithm via one hot encoding for IoT IDS

The random forest algorithm is one of important supervised machine learning (ML) algorithms. In the present paper, the accuracy of the results of the random forest (RF) algorithm has been improved by the use of the One Hot Encoding method. The Intrusion Detection System (IDS) can be defined as a system that can predict security vulnerabilities within network traffic and is located out of range on a network infrastructure. It does not affect the efficiency of the built-in network because it analyzes a copy of the built-in traffic flow and reports results to the administrator by giving alerts. However, since IDS is a listening system only, it cannot take automatic action to prevent an attack or security vulnerability detected from infecting the system, it provides information about the source address to start the break-in, the address of the target and the type of suspected attack. The IoTID20 dataset is used to verify the improved algorithm, where this dataset is having three targets, the proposed system is compared with the state-of-art approaches and shows superiority over them

Honeyword Generation Using a Proposed Discrete Salp Swarm Algorithm

إن كلمات العسل (Honeywords) هي كلمات مرور مزيفة مرافقة لكلمة المرور الحقيقية والتي تدعى كلمة السكر. يعد نظام كلمات مرور العسل نظامًا فعالاً لاكتشاف اختراق كلمات المرور مصمم لاكتشاف اختراق كلمة المرور بسهولة من أجل تحسين أمان كلمات المرور المشفرة. لكل مستخدم ، سيكون لملف كلمة المرور الخاص بنظام الكلمات العسلية كلمة مرور واحدة حقيقية مشفرة مصحوبة بالعديد من كلمات المرور المزيفة المشفرة. إذا قام شخص دخيل بسرقة ملف كلمات المرور من النظام ونجح في اختراق كلمات المرور محاولا تسجيل الدخول إلى حسابات المستخدمين ، فسيكتشف نظام كلمات المرور هذه المحاولة من خلال مدقق العسل. (Honeychecker) مدقق العسل هو خادمًا إضافيًا يميز كلمة المرور الحقيقية عن كلمات المرور المزيفة ويطلق إنذارًا إذا قام شخص دخيل بتسجيل الدخول باستخدام كلمة مرور العسل. تم اقتراح العديد من طرق توليد كلمات العسل خلال البحوث السابقة، مع وجود قيود على عمليات إنشاء كلمات العسل الخاصة بهم ، ونجاح محدود في توفير جميع ميزات كلمات العسل المطلوبة ، والتعرض للعديد من مشكلات كلمات العسل. سيقدم هذا العمل طريقة جديدة لتوليد كلمات العسل تستخدم خوارزمية سرب عنب البحر المتقطعة. خوارزمية سرب عنب البحر هي خوارزمية تحسين مستوحاة من الأحياء تحاكي سلوك سرب عنب البحر في بيئتها الطبيعية. تم استخدام  خوارزمية سرب عنب البحر لحل مجموعة متنوعة من مشاكل التحسين. ستعمل طريقة توليد الكلمات العسلية المقترحة على تحسين عملية توليد كلمات العسل وتحسين ميزات كلمات العسل والتغلب على عيوب التقنيات السابقة. ستوضح هذه الدراسة العديد من الاستراتيجيات السابقة لتوليد الكلمات العسلية، ووصف الطريقة المقترحة، وفحص النتائج التجريبية، ومقارنة طريقة إنتاج كلمات العسل الجديدة بالطرق السابقة.Honeywords are fake passwords that serve as an accompaniment to the real password, which is called a “sugarword.” The honeyword system is an effective password cracking detection system designed to easily detect password cracking in order to improve the security of hashed passwords. For every user, the password file of the honeyword system will have one real hashed password accompanied by numerous fake hashed passwords. If an intruder steals the password file from the system and successfully cracks the passwords while attempting to log in to users’ accounts, the honeyword system will detect this attempt through the honeychecker. A honeychecker is an auxiliary server that distinguishes the real password from the fake passwords and triggers an alarm if intruder signs in using a honeyword. Many honeyword generation approaches have been proposed by previous research, all with limitations to their honeyword generation processes, limited success in providing all required honeyword features, and susceptibility to many honeyword issues. This work will present a novel honeyword generation method that uses a proposed discrete salp swarm algorithm. The salp swarm algorithm (SSA) is a bio-inspired metaheuristic optimization algorithm that imitates the swarming behavior of salps in their natural environment. SSA has been used to solve a variety of optimization problems. The presented honeyword generation method will improve the generation process, improve honeyword features, and overcome the issues of previous techniques. This study will demonstrate numerous previous honeyword generating strategies, describe the proposed methodology, examine the experimental results, and compare the new honeyword production method to those proposed in previous research