scholarly journals Malware Classification Based on Multilayer Perception and Word2Vec for IoT Security

2022 ◽  
Vol 22 (1) ◽  
pp. 1-22
Author(s):  
Yanchen Qiao ◽  
Weizhe Zhang ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Smart Cities ◽  
Iot Security ◽  
Multilayer Perception ◽  
Learning Network ◽  
Binary File ◽  
Malware Classification ◽  
Improve Accuracy ◽  
Different Types ◽  
Iot Devices ◽  
Deep Learning Network

With the construction of smart cities, the number of Internet of Things (IoT) devices is growing rapidly, leading to an explosive growth of malware designed for IoT devices. These malware pose a serious threat to the security of IoT devices. The traditional malware classification methods mainly rely on feature engineering. To improve accuracy, a large number of different types of features will be extracted from malware files in these methods. That brings a high complexity to the classification. To solve these issues, a malware classification method based on Word2Vec and Multilayer Perception (MLP) is proposed in this article. First, for one malware sample, Word2Vec is used to calculate a word vector for all bytes of the binary file and all instructions in the assembly file. Second, we combine these vectors into a 256x256x2-dimensional matrix. Finally, we designed a deep learning network structure based on MLP to train the model. Then the model is used to classify the testing samples. The experimental results prove that the method has a high accuracy of 99.54%.

scholarly journals Survey of DoS/DDoS attacks in IoT

2021 ◽  
Vol 3 (1) ◽  
pp. 23-28
Author(s):  
Rozan Khader ◽  
Derar Eleyan
Keyword(s):  
Smart Cities ◽  
Denial Of Service ◽  
Wearable Devices ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Iot Security ◽  
Counter Measures ◽  
Security Issues ◽  
Iot Devices ◽  
Internet Of Thing

The term internet of thing (IoT) has gained much popularity in the last decade. Which can be defined as various connected devices over the internet. IoT has rapidly  spread to include all aspects of our lives. For instance, smart houses, smart cities, and variant wearable devices. IoT devices work to do their desired goals, which is to develop a person life with his/her minimal involvement. At the same time, IoT devices have many weaknesses, which attackers exploit to affect these devices security. Denial of Service (DoS) and Distributed Denial of Service (DDoS) are considered the most common attacks that strike IoT security. The main aim of these attacks is to make victim systems down and inaccessible for legitimate users by malicious malware. This paper objective is to discuss and review security issues related to DoS/DDoS Attacks and their counter measures i.e. prevention based on IoT devices layers structure.

scholarly journals Malware Classification Using Machine Learning and Image Processing

2022 ◽  
Vol 10 (1) ◽  
pp. 531-541
Author(s):  
Zainab Mushtaq
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Computer Security ◽  
Transfer Learning ◽  
Learning Technology ◽  
Test Dataset ◽  
Learning Network ◽  
Malware Classification ◽  
Learning Technique ◽  
Deep Learning Network

Abstract: Malware is routinely used for illegal reasons, and new malware variants are discovered every day. Computer vision in computer security is one of the most significant disciplines of research today, and it has witnessed tremendous growth in the preceding decade due to its efficacy. We employed research in machine-learning and deep-learning technology such as Logistic Regression, ANN, CNN, transfer learning on CNN, and LSTM to arrive at our conclusions. We have published analysis-based results from a range of categorization models in the literature. InceptionV3 was trained using a transfer learning technique, which yielded reasonable results when compared with other methods such as LSTM. On the test dataset, the transferring learning technique was about 98.76 percent accurate, while on the train dataset, it was around 99.6 percent accurate. Keywords: Malware, illegal activity, Deep learning, Network Security,

Implications of Cybersecurity Breaches in LPWANs

2021 ◽  
pp. 1-18
Author(s):  
Åke Axeland ◽  
Henrik Hagfeldt ◽  
Magnus Carlsson ◽  
Lina Lagerquist Sergel ◽  
Ismail Butun
Keyword(s):  
Long Range ◽  
Smart Cities ◽  
Wide Area ◽  
Area Network ◽  
Wide Area Network ◽  
Iot Security ◽  
Security Breaches ◽  
Adequate Knowledge ◽  
Iot Devices ◽  
Political Decisions

With the contrast of limited performance and big responsibility of IoT devices, potential security breaches can have serious impacts in means of safety and privacy. Potential consequences of attacks on IoT devices could be leakage of individuals daily habits and political decisions being influenced. While the consequences might not be avoidable in their entirety, adequate knowledge is a fundamental part of realizing the importance of IoT security and during the assessment of damages following a breach. This chapter will focus on two low-powered wide area network (LPWAN) technologies, narrow-band iot (NB-IoT) and long-range wide area network (LoRaWAN). Further, three use cases will be considered—healthcare, smart cities, and industry—which all to some degree rely on IoT devices. It is shown that with enough knowledge of possible attacks and their corresponding implications, more secure IoT systems can be developed.

scholarly journals Different Dimensions of IOT Security

2020 ◽  
Vol 8 (5) ◽  
pp. 860-863
Keyword(s):  
Intrusion Detection System ◽  
Detection System ◽  
Cyber Attacks ◽  
Security And Privacy ◽  
Security Threats ◽  
Iot Security ◽  
Different Types ◽  
Different Dimensions ◽  
Iot Devices ◽  
Privacy Issues

IOT is wirelessly connecting things to the internet using sensors, RFID’s and remotely accessing and managing them over our phone or through our voice. IOT uses various communication protocols such as Zigbee, 6LowPan, Bluetooth and has bi directional communication for exchange of information. The database for IOT is cloud which is also vulnerable to security threats. The increasing amount of popularity of IoT and its pervasive usage has made it more recurrent to prominent cyber-attacks such as botnet attack, IoT ransom ware, DOS attack, RFID hack. The challenges faced by IoT are to stop hackers from stealing data, having unattended access to the device and performing malicious activities. There are many techniques which can be used to secure IoT devices such as using a secure encrypted Wi-Fi network, using digital signature for authenticity, updating to latest patches, installing Intrusion Detection System. We’ll also be assessing various IoT devices and threats associated with them in real time environment and the level of harm these threats can cause to the device if they are not properly mitigated or eradicated. In this paper we’ll also be addressing different types of risks associated with different IOT devices and approaches to solve the security and privacy issues

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

scholarly journals UAVs in rail damage image diagnostics supported by deep-learning networks

2021 ◽  
Vol 11 (1) ◽  
pp. 339-348
Author(s):  
Piotr Bojarczak ◽  
Piotr Lesiak
Keyword(s):  
Deep Learning ◽  
Head Width ◽  
Learning Networks ◽  
Image Brightness ◽  
Learning Network ◽  
Rail Head ◽  
Image Recording ◽  
Python Language ◽  
Deep Learning Network ◽  
Efficiency Rate

Abstract The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.

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