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

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

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

Gaming argumentation framework (GAF): Pfizer or AstraZeneca Vaccine of The COVID-19 as a case study

Dung’s argumentation frameworks (AF) were introduced in the last century it works with the justification of the argument. This framework analyzes attacks of arguments, it works away on the characteristics of arguments structures and words was used in the attack between each other, etc. These properties make this model attractive as it decreases most of the complexities included when applying the argumentation system. This system can be applied to different states such as to evaluate the arguments or with the supported argument to be defense and attacked arguments. In addition, the group of experts may be making argumentation about some cases. In the latter scenario, agents with potentially dissimilar arguments and/or opinions are used to evaluate the arguments, allowing for the consideration of several sets of arguments and attack relations. This framework is extended to propose a new system called gaming argumentation framework (GAF). It helps to make a decision about the current problem by making claims and attack determination to the arguments, then putting the result of these claims and attack determination to the game theory with two players to achieve the final results that help the decision-maker to decide about the current problem. Finally, compare this framework with other frameworks, and provide an example to explain how the proposed framework performs its intended purpose, where decision making is very important in the medical field therefore this paper taking the confusion on the COVID-19 vaccines as a case study to solve Pfizer or AstraZeneca problem and make the decision about this case

PAAD: POLITICAL ARABIC ARTICLES DATASET FOR AUTOMATIC TEXT CATEGORIZATION

Now day’s text Classification and Sentiment analysis is considered as one of the popular Natural Language Processing (NLP) tasks. This kind of technique plays significant role in human activities and has impact on the daily behaviours. Each article in different fields such as politics and business represent different opinions according to the writer tendency. A huge amount of data will be acquired through that differentiation. The capability to manage the political orientation of an online article automatically. Therefore, there is no corpus for political categorization was directed towards this task in Arabic, due to the lack of rich representative resources for training an Arabic text classifier. However, we introduce political Arabic articles dataset (PAAD) of textual data collected from newspapers, social network, general forum and ideology website. The dataset is 206 articles distributed into three categories as (Reform, Conservative and Revolutionary) that we offer to the research community on Arabic computational linguistics. We anticipate that this dataset would make a great aid for a variety of NLP tasks on Modern Standard Arabic, political text classification purposes. We present the data in raw form and excel file. Excel file will be in four types such as V1 raw data, V2 preprocessing, V3 root stemming and V4 light stemming

Human Body Posture Recognition Approaches: A Review

Human body posture recognition has become the focus of many researchers in recent years. Recognition of body posture is used in various applications, including surveillance, security, and health monitoring. However, these systems that determine the body’s posture through video clips, images, or data from sensors have many challenges when used in the real world. This paper provides an important review of how most essential ‎ hardware technologies are ‎used in posture recognition systems‎. These systems capture and collect datasets through ‎accelerometer sensors or computer vision. In addition, this paper presents a comparison ‎study with state-of-the-art in terms of accuracy. We also present the advantages and ‎limitations of each system and suggest promising future ideas that can increase the ‎efficiency of the existing posture recognition system. Finally, the most common datasets ‎applied in these systems are described in detail. It aims to be a resource to help choose one of the methods in recognizing the posture of the human body and the techniques that suit each method. It analyzes more than 80 papers between 2015 and 202

Tourism Companies Assessment via Social Media Using Sentiment Analysis

ازدادت وسائل التواصل الاجتماعي بشكل كبير وواضح لانها وسيلة إعلام للمستخدمين للتعبير عن مشاعرهم من خلال آلاف المنشورات والتعليقات حول شركات السياحة. وبالتالي ، يصعب على السائح قراءة جميع التعليقات لتحديد ما إذا كانت تلك الآراء إيجابية أم سلبية لتقييم نجاح الشركة. في هذه البحث,تم استخدام التنقيب عن النص لتصنيف المشاعر من خلال جمع مراجعات اللهجة العراقية حول شركات السياحة من الفيس بوك لتحليلها باستخدام تحليل المشاعر لتتبع المشاعر الموجوده في المنشورات والتعليقات. ثم تم تصنيفها إلى تعليق إيجابي أو سلبي أو محايد باستخدام Naïve Bayes, Rough Set Theory , K-Nearest Neighbor. من بين 71 شركة سياحة عراقية وجدت أن 28٪ من هذه الشركات لديها تقييم جيد جدا ، و 26٪ من هذه الشركات لديها تقييم جيد ، و 31٪ من هذه الشركات لديها تقييم متوسط ​​، و 4٪ من هذه الشركات لديها تقييم مقبول و 11٪ من هذه الشركات لديها تقييم سيء. ساعدت النتائج التجريبية الشركات على تحسين عملها وبرامجها واستجابة كافية وسريعة لمتطلبات العملاءIn recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor methods. After experimental results, it was determined that out of 71 tested Iraqi tourism companies, 28% from these companies have very good assessment, 26% from these companies have good assessment, 31% from these companies have medium assessment, 4% from these companies have acceptance assessment and 11% from these companies have bad assessment. These results helped the companies to improve their work and programs responding sufficiently and quickly to customer demands

Inhibitory Effects of Chalcone on the Replication of Poliovirus in Vitro

The compound chalcon originally is extracted form some plant and herbs, the studies of the antiviral activity of this compound were done in two cell line cultures the L2OB and RD, the compound relatively non toxic to both cell lines of the concentration of 32?g/ml or less ,the compound have significantly anti poliovirus activity in both L2OB cell line and RD cell line, we find that the concentration of 0.03 ?g/ml or more inhibit the 100TCDID50 of the poliovirus .The therapeutic index(TI)used in this study to evaluate the drug activity ,( TI is the ratio of dose of drug which is just toxic to the cells to the does which is just inhibit the viral multiplication, if this index more than one the margin of safety of drug is according great ) .In this study the TI of chalcone against poliovirus is 266,therefore this compound if used in man have little or no side effect

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

Short Text Semantic Similarity Measurement Approach Based on Semantic Network

Estimating the semantic similarity between short texts plays an increasingly prominent role in many fields related to text mining and natural language processing applications, especially with the large increase in the volume of textual data that is produced daily. Traditional approaches for calculating the degree of similarity between two texts, based on the words they share, do not perform well with short texts because two similar texts may be written in different terms by employing synonyms. As a result, short texts should be semantically compared. In this paper, a semantic similarity measurement method between texts is presented which combines knowledge-based and corpus-based semantic information to build a semantic network that represents the relationship between the compared texts and extracts the degree of similarity between them. Representing a text as a semantic network is the best knowledge representation that comes close to the human mind's understanding of the texts, where the semantic network reflects the sentence's semantic, syntactical, and structural knowledge. The network representation is a visual representation of knowledge objects, their qualities, and their relationships. WordNet lexical database has been used as a knowledge-based source while the GloVe pre-trained word embedding vectors have been used as a corpus-based source. The proposed method was tested using three different datasets, DSCS, SICK, and MOHLER datasets. A good result has been obtained in terms of RMSE and MAE