scholarly journals Machine learning Algorithm of Intrusion Detection System

2021 ◽  
pp. 1-12
Author(s):  
Rozin Majeed Abdullah ◽  
Adnan Mohsin Abdulazeez ◽  
Adel Al-Zebari
Keyword(s):  
Urban Communities ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithm ◽  
Security Issues ◽  
The World ◽  
Organization Framework ◽  
Huge Variety ◽  
The Web

Web of thing (WoT) is a gifted answer for interface and access each gadget through the web. Consistently the gadget includes increments with huge variety fit as a fiddle, size, use and intricacy. In this paper Since WoT drives the world and changes individuals' lives with its wide scope of administrations and applications. In any case, WoT offers various types of assistance through applications, it faces serious security issues and powerless against assaults, for example, sinkhole assault, overhang dropping, forswearing of administration assaults. So on, the Interruption recognition framework is utilized to recognize such assaults when the organization's security is penetrated. Given a scale extension of Web of Things for a practical asset the executives in brilliant urban communities, a legitimate plan of an interruption recognition framework IDS is basic to protect the future organization framework from interlopers. With the development of associated things, the most broadly utilized brought together cloud-based IDS regularly suers from high inertness and organization overhead, subsequently coming about in lethargy to assaults and moderate recognition of pernicious clients.

scholarly journals Intelligent and Effective Intrusion Detection System using Machine Learning Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 237-240
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Training Time

Intrusion Detection System observes the network traffic and identifies the attack and also inform the admin to corrective action. Powerful Intrusion Detection system is required for detection to various modern attack. There is need of efficient Intrusion Detection system .The focus of IDS research is the application of machine Learning and Deep Learning techniques. Projected work is combination of Deep Learning Technique in which Non Symmetric Deep Auto Encoder and Machine Learning Algorithm, Support Vector Machine Classifier is used to develop the Model. Stack power of the Non symmetric Deep Auto Encoder and Quickness with exactness of the SVM makes the Model very efficient. This Model not only improves the accuracy value but also improve recall and precision. It also cause the reduction of training time .To evaluate the performance of the Model and do the analysis the special Data set which are used are KDD CUP and NSL KDD Dataset.

scholarly journals Routing Attacs pada Internet Of Things Berbasis Smart Intrution Detecion System

2020 ◽  
Vol 7 (2) ◽  
pp. 329
Author(s):  
Eka Lailatus Sofa ◽  
Subiyanto Subiyanto
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Routing Attacks

<p class="Abstrak"><em>Internet of Things</em> (IoT) telah memasuki berbagai aspek kehidupan manusia, diantaranya <em>smart city, smart home, smart street, </em>dan<em> smart industry </em>yang memanfaatkan internet untuk memantau informasi yang dibutuhkan<em>.</em> Meskipun sudah dienkripsi dan diautentikasi, protokol jaringan <a title="IPv6" href="https://en.wikipedia.org/wiki/IPv6">IPv6</a> over Low-Power Wireless <a title="Personal area network" href="https://en.wikipedia.org/wiki/Personal_area_network">Personal Area Networks</a> (6LoWPAN) yang dapat menghubungkan benda-benda yang terbatas sumber daya di IoT masih belum dapat diandalkan. Hal ini dikarenakan benda-benda tersebut masih dapat terpapar oleh <em>routing attacks</em> yang berasal dari jaringan 6LoWPAN dan internet. Makalah ini menyajikan kinerja <em>Smart Intrusion Detection System</em> berdasarkan <em>Compression Header Analyzer</em> untuk menganalisis model <em>routing attacks</em> lainnya pada jaringan IoT. IDS menggunakan <em>compression header</em> 6LoWPAN sebagai fitur untuk <em>machine learning algorithm</em> dalam mempelajari jenis <em>routing attacks</em>. Skenario simulasi dikembangkan untuk mendeteksi <em>routing attacks</em> berupa <em>selective forwarding attack</em> dan <em>sinkhole attack</em>. Pengujian dilakukan menggunakan <em>feature selection</em> dan <em>machine learning algorithm</em>. <em>Feature selection</em> digunakan untuk menentukan fitur signifikan yang dapat membedakan antara aktivitas normal dan abnormal. Sementara <em>machine learning algorithm</em> digunakan untuk mengklasifikasikan <em>routing attacks</em> pada jaringan IoT. Ada tujuh <em>machine learning algorithm</em> yang digunakan dalam klasifikasi antara lain <em>Random Forest, Random Tree, J48, Bayes Net, JRip, SMO,</em> dan <em>Naive Bayes</em>. Hasil percobaan disajikan untuk menunjukkan kinerja <em>Smart Intrusion Detection System</em> berdasarkan <em>Compression Header Analyzer</em> dalam menganalisis <em>routing attacks</em>. Hasil evaluasi menunjukkan bahwa IDS ini dapat mendeteksi antara serangan dan <em>non-</em>serangan.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Internet of Things (IoT) has entered various aspects of human life including smart city, smart home, smart street, and smart industries that use the internet to get the information they need. Even though it's encrypted and authenticated, Internet protocol  <a title="IPv6" href="https://en.wikipedia.org/wiki/IPv6">IPv6</a> over Low-Power Wireless <a title="Personal area network" href="https://en.wikipedia.org/wiki/Personal_area_network">Personal Area Networks</a> (6LoWPAN) networks that can connect limited resources to IoT are still unreliable. This is because these objects can still be exposed to attacks from 6LoWPAN and the internet. This paper presents the performance of an Smart Intrusion Detection System based on Compression Header Analyzer to analyze other routing attack models on IoT networks. IDS uses a 6LoWPAN compression header as a feature for machine learning algorithms in learning the types of routing attacks. Simulation scenario was developed to detect routing attacks in the form of selective forwarding and sinkhole. Testing is done using the feature selection and machine learning algorithm. Feature selection is used to determine significant features that can distinguish between normal and abnormal activities. While machine learning algorithm is used to classify attacks on IoT networks. There were seven machine learning algorithms used in the classification including Random Forests, Random Trees, J48, Bayes Net, JRip, SMO, and Naive Bayes. Experiment Results to show the results of the Smart Intrusion Detection System based on Compression Header Analyzer in analyzing routing attacks. The evaluation results show that this IDS can protect between attacks and non-attacks.</em><strong></strong></p><p class="Abstrak"><em><strong><br /></strong></em></p>

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

scholarly journals DSSAM: digitally signed secure acknowledgement method for mobile ad hoc network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ashutosh Srivastava ◽  
Sachin Kumar Gupta ◽  
Mohd Najim ◽  
Nitesh Sahu ◽  
Geetika Aggarwal ◽  
...  
Keyword(s):  
Digital Signature ◽  
Intrusion Detection System ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Detection System ◽  
Source Routing ◽  
Security Issues ◽  
Dynamic Source ◽  
Mobile Ad Hoc

AbstractMobile ad hoc network (MANET) is an infrastructure-less, self-motivated, arbitrary, self-configuring, rapidly changing, multi-hop network that is self-possessing wireless bandwidth-conscious links without centrally managed router support. In such a network, wireless media is easy to snoop. It is firm to the surety to access any node, easier to insertion of bad elements or attackers for malicious activities in the network. Therefore, security issues become one of the significant considerations for such kind of networks. The deployment of an effective intrusion detection system is important in order to provide protection against various attacks. In this paper, a Digitally Signed Secure Acknowledgement Method (DSSAM) with the use of the RSA digital signature has been proposed and simulated. Three different parameters are considered, namely secure acknowledgment, node authentication, and packet authentication for study. This article observes the DSSAM performance and compares it with two existing standard methods, namely Watchdog and 2-ACK under standard Dynamic Source Routing (DSR) routing environment. In the end, it is noticed that the rate of detection of malicious behaviour is better in the case of the proposed method. However, associated overheads are high. A trade-off between performance and overhead has been considered.

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