scholarly journals Intelligent Botnet Detection Approach in Modern Applications

2021 ◽  
Vol 15 (16) ◽  
pp. 113
Author(s):  
Khattab M. Ali Alheeti ◽  
Ibrahim Alsukayti ◽  
Mohammed Alreshoodi
Keyword(s):  
Detection System ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Learning Networks ◽  
Detection Systems ◽  
Testing Dataset ◽  
Detection Approach ◽  
Secure Environment ◽  
Iot Devices ◽  
The Internet Of Things

<p class="0abstract">Innovative applications are employed to enhance human-style life. The Internet of Things (IoT) is recently utilized in designing these environments. Therefore, security and privacy are considered essential parts to deploy and successful intelligent environments. In addition, most of the protection systems of IoT are vulnerable to various types of attacks. Hence, intrusion detection systems (IDS) have become crucial requirements for any modern design. In this paper, a new detection system is proposed to secure sensitive information of IoT devices. However, it is heavily based on deep learning networks. The protection system can provide a secure environment for IoT. To prove the efficiency of the proposed approach, the system was tested by using two datasets; normal and fuzzification datasets. The accuracy rate in the case of the normal testing dataset was 99.30%, while was 99.42% for the fuzzification testing dataset. The experimental results of the proposed system reflect its robustness, reliability, and efficiency.</p>

A Survey of Authentication Schemes in the Internet of Things

2019 ◽  
Vol 6 (1) ◽  
pp. 15-30 ◽  
Author(s):  
Yasmine Labiod ◽  
Abdelaziz Amara Korba ◽  
Nacira Ghoualmi-Zine
Keyword(s):  
Internet Of Things ◽  
Security Requirements ◽  
Security And Privacy ◽  
The Internet ◽  
Privacy And Security ◽  
Depth Study ◽  
Authentication Schemes ◽  
Iot Devices ◽  
Privacy Issues ◽  
The Internet Of Things

In the recent years, the Internet of Things (IoT) has been widely deployed in different daily life aspects such as home automation, electronic health, the electric grid, etc. Nevertheless, the IoT paradigm raises major security and privacy issues. To secure the IoT devices, many research works have been conducted to counter those issues and discover a better way to remove those risks, or at least reduce their effects on the user's privacy and security requirements. This article mainly focuses on a critical review of the recent authentication techniques for IoT devices. First, this research presents a taxonomy of the current cryptography-based authentication schemes for IoT. In addition, this is followed by a discussion of the limitations, advantages, objectives, and attacks supported of current cryptography-based authentication schemes. Finally, the authors make in-depth study on the most relevant authentication schemes for IoT in the context of users, devices, and architecture that are needed to secure IoT environments and that are needed for improving IoT security and items to be addressed in the future.

scholarly journals BAT—Block Analytics Tool Integrated with Blockchain Based IoT Platform

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1525
Author(s):  
Chathurangi Edussuriya ◽  
Kasun Vithanage ◽  
Namila Bandara ◽  
Janaka Alawatugoda ◽  
Manjula Sandirigama ◽  
...  
Keyword(s):  
Security And Privacy ◽  
Sensitive Information ◽  
Pharmaceutical Drugs ◽  
Case Scenario ◽  
Real World Data ◽  
Data Set ◽  
Pharmaceutical Supply Chain ◽  
Iot Platform ◽  
Novel Approach ◽  
Iot Devices

The Internet of Things (IoT) is the novel paradigm of connectivity and the driving force behind state-of-the-art applications and services. However, the exponential growth of the number of IoT devices and services, their distributed nature, and scarcity of resources has increased the number of security and privacy concerns ranging from the risks of unauthorized data alterations to the potential discrimination enabled by data analytics over sensitive information. Thus, a blockchain based IoT-platform is introduced to address these issues. Built upon the tamper-proof architecture, the proposed access management mechanisms ensure the authenticity and integrity of data. Moreover, a novel approach called Block Analytics Tool (BAT), integrated with the platform is proposed to analyze and make predictions on data stored on the blockchain. BAT enables the data-analysis applications to be developed using the data stored in the platform in an optimized manner acting as an interface to off-chain processing. A pharmaceutical supply chain is used as the use case scenario to show the functionality of the proposed platform. Furthermore, a model to forecast the demand of the pharmaceutical drugs is investigated using a real-world data set to demonstrate the functionality of BAT. Finally, the performance of BAT integrated with the platform is evaluated.

scholarly journals Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4372 ◽  
Author(s):  
Yan Naung Soe ◽  
Yaokai Feng ◽  
Paulus Insap Santosa ◽  
Rudy Hartanto ◽  
Kouichi Sakurai
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Detection System ◽  
Rapid Development ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Detection Systems ◽  
Iot Devices ◽  
Artificial Neural Network Ann ◽  
Feature Selection Approach

With the rapid development and popularization of Internet of Things (IoT) devices, an increasing number of cyber-attacks are targeting such devices. It was said that most of the attacks in IoT environments are botnet-based attacks. Many security weaknesses still exist on the IoT devices because most of them have not enough memory and computational resource for robust security mechanisms. Moreover, many existing rule-based detection systems can be circumvented by attackers. In this study, we proposed a machine learning (ML)-based botnet attack detection framework with sequential detection architecture. An efficient feature selection approach is adopted to implement a lightweight detection system with a high performance. The overall detection performance achieves around 99% for the botnet attack detection using three different ML algorithms, including artificial neural network (ANN), J48 decision tree, and Naïve Bayes. The experiment result indicates that the proposed architecture can effectively detect botnet-based attacks, and also can be extended with corresponding sub-engines for new kinds of attacks.

scholarly journals Adversarial Samples on Android Malware Detection Systems for IoT Systems

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 974 ◽  
Author(s):  
Xiaolei Liu ◽  
Xiaojiang Du ◽  
Xiaosong Zhang ◽  
Qingxin Zhu ◽  
Hao Wang ◽  
...  
Keyword(s):  
Detection System ◽  
Fitness Function ◽  
Malware Detection ◽  
Security Analysis ◽  
Machine Learning Algorithms ◽  
Android Malware ◽  
Testing Framework ◽  
Detection Systems ◽  
Android Malware Detection ◽  
Iot Devices

Many IoT (Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a testing framework for learning-based Android malware detection systems (TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android application with a success rate of nearly 100% and can perform black-box testing on the system.

Image Validation with Virtualization in Blockchain Based Internet of Things

2020 ◽  
Vol 17 (5) ◽  
pp. 2388-2395
Author(s):  
M. Vivek Anand ◽  
S. Vijayalakshmi
Keyword(s):  
Internet Of Things ◽  
Data Privacy ◽  
The Internet ◽  
Intelligence Agency ◽  
Cyber Intelligence ◽  
Peer To Peer Networks ◽  
Secure Environment ◽  
Iot Devices ◽  
Processing Techniques ◽  
The Internet Of Things

IoT is changing the way for a world, where many of our daily objects will be connected with each other and will interact with their environment in order to collect information and automate certain tasks. IoT requires seamless authentication, data privacy, security, robustness against attacks, easy deployment, and self-maintenance. Protecting data in the internet of things is essential for making the IoT environment secure. In order to secure the data on the internet of things, the blockchain will provide distributed peer to peer networks. Blockchain-based internet of things is making a secure environment in the IoT environment. Data are stored in the form of images in IoT devices that are captured in various locations in the IoT environment for processing. Images are stored as data in the blockchain and it acts as a transaction. This paper expresses the environment of blockchain-based internet of things with image validation. This paper will explain this domain with an example of a criminal’s image identification with image processing techniques to provide better service to the cyber intelligence agency to find criminals easily. The identification of criminals is done by comparing the images of the criminals’ identification.

scholarly journals An Efficient and Packing-Resilient Two-Phase Android Cloned Application Detection Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Fang Lyu ◽  
Yaping Lin ◽  
Junfeng Yang
Keyword(s):  
Real World ◽  
Large Scale ◽  
Detection System ◽  
Malicious Code ◽  
Security And Privacy ◽  
Two Phase ◽  
Android Malware ◽  
Detection Approach ◽  
Application Industry ◽  
Selection Phase

The huge benefit of mobile application industry has attracted a large number of developers and attendant attackers. Application repackaging provides help for the distribution of most Android malware. It is a serious threat to the entire Android ecosystem, as it not only compromises the security and privacy of the app users but also plunders app developers’ income. Although massive approaches have been proposed to address this issue, plagiarists try to fight back through packing their malicious code with the help of commercial packers. Previous works either do not consider the packing issue or rely on time-consuming computations, which are not scalable for large-scale real-world scenario. In this paper, we propose FUIDroid, a novel two-phase app clones detection system that can detect the packed cloned app. FUIDroid includes a function-based fast selection phase to quickly select suspicious apps by analyzing apps’ description and a further UI-based accurate detection phase to refine the detection result. We evaluate our system on two sets of apps. The result from experiment on 320 packed samples demonstrates that FUIDroid is resilient to packed apps. The evaluation on more than 150,000 real-world apps shows the efficiency of FUIDroid in large-scale scenario.

scholarly journals Internet of Things: A Comprehensive Study on Machine Learning-based Intrusion Detection approaches

2021 ◽  
Author(s):  
Priyanka Gupta ◽  
Lokesh Yadav ◽  
Deepak Singh Tomar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Research Work ◽  
Security And Privacy ◽  
Iot Security ◽  
Advantages And Disadvantages ◽  
Iot Devices

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, healthcare, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats; detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT Intrusion Detection System is presented. The Machine learning methods are explored to provide an effective security solution for IoT Intrusion Detection systems. Then discussed the advantages and disadvantages of the selected methodology. Further, the datasets used in IoT security are also discussed. Finally, the examination of the open issues and directions for future trends are also provided.

scholarly journals Intelligent Electromagnetic Sensors for Non-Invasive Trojan Detection

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8288
Author(s):  
Ethan Chen ◽  
John Kan ◽  
Bo-Yuan Yang ◽  
Jimmy Zhu ◽  
Vanessa Chen
Keyword(s):  
Detection System ◽  
Sensitive Information ◽  
Malicious Attacks ◽  
Electromagnetic Sensors ◽  
Self Testing ◽  
Detection Approach ◽  
Good Energy ◽  
Extreme Edge ◽  
On Chip ◽  
Efficient Learning

Rapid growth of sensors and the Internet of Things is transforming society, the economy and the quality of life. Many devices at the extreme edge collect and transmit sensitive information wirelessly for remote computing. The device behavior can be monitored through side-channel emissions, including power consumption and electromagnetic (EM) emissions. This study presents a holistic self-testing approach incorporating nanoscale EM sensing devices and an energy-efficient learning module to detect security threats and malicious attacks directly at the front-end sensors. The built-in threat detection approach using the intelligent EM sensors distributed on the power lines is developed to detect abnormal data activities without degrading the performance while achieving good energy efficiency. The minimal usage of energy and space can allow the energy-constrained wireless devices to have an on-chip detection system to predict malicious attacks rapidly in the front line.

Privacy Dangers of Wearables and the Internet of Things

2016 ◽  
pp. 379-402 ◽  
Author(s):  
Scott Amyx
Keyword(s):  
Data Protection ◽  
Data Privacy ◽  
Security And Privacy ◽  
Employee Privacy ◽  
Privacy Concerns ◽  
Business Operations ◽  
Managerial Implications ◽  
Iot Devices ◽  
Privacy Issues ◽  
The Internet Of Things

This chapter identifies concerns about, and the managerial implications of, data privacy issues related to wearables and the IoT; it also offers some enterprise solutions to the complex concerns arising from the aggregation of the massive amounts of data derived from wearables and IoT devices. Consumer and employee privacy concerns are elucidated, as are the problems facing managers as data management and security become an important part of business operations. The author provides insight into how companies are currently managing data as well as some issues related to data security and privacy. A number of suggestions for improving the approach to data protection and addressing concerns about privacy are included. This chapter also examines trending issues in the areas of data protection and the IoT, and contains thought-provoking discussion questions pertaining to business, wearables/IoT data, and privacy issues.

Framework for Threat Analysis and Attack Modelling of Network Security Protocols

2020 ◽  
pp. 110-124
Author(s):  
Nachiket Athavale ◽  
Shubham Deshpande ◽  
Vikash Chaudhary ◽  
Jatin Chavan ◽  
S. S. Barde
Keyword(s):  
Network Security ◽  
Intrusion Detection ◽  
Detection System ◽  
Security Protocols ◽  
Intrusion Detection Systems ◽  
Sensitive Information ◽  
Detection Systems ◽  
Threat Analysis ◽  
Warning Message ◽  
System Administrator

Nowadays everything is computerized including banking and personal records. Also, to boost business profits, businessmen have changed their way of operations from physical way to electronic way, for example Flipkart. But as these developments benefit the developer they also increase the chance of exposing all of customer's personal details to malicious users. Hackers can enter into the system and can steal crucial or sensitive information about other authentic users and in case of banks leads to frauds. Security thus, becomes an important issue for all companies and banks. Intrusion detection systems help such companies by detecting in real time whether an intrusion is carried on or not. Here the authors are developing a signature based intrusion detection system which will scan incoming packets and send a warning message to system administrator. Also, the authors are implementing a framework and provide it to all the users so that developing intrusion detection based system similar to ours. The advantage of using framework is that it can be upgraded and re-defined whenever it is needed.

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