scholarly journals Malware Detection and Classification using Random Forest and Adaboost Algorithms

2019 ◽  
Vol 8 (10) ◽  
pp. 2863-2868
Keyword(s):  
Random Forest ◽  
Internet Of Things ◽  
State Of The Art ◽  
Gaussian Mixture ◽  
Statistical Techniques ◽  
The Internet ◽  
Bayes Classifier ◽  
Wooded Area ◽  
Malware Detection And Classification ◽  
The Internet Of Things

The chance of malware within the Internet of Things (IoT) surroundings is increasing due to a loss of detectors. This paper proposes a way to are expecting the intrusion of malware the usage of state-of the-art gadget mastering algorithms which could discover malware faster and greater appropriately, as compared with the existing methods (this is, payload, port-based, and statistical techniques). Clever workplace surroundings was implemented to capture the drift of packet datasets, where malware and normal packets were captured, and eleven features have been extracted from them. Four gadget getting to know algorithms (random forest, a guide vector gadget, AdaBoost, and a Gaussian mixture version–primarily based naive Bayes classifier) were investigated to implement the automatic malware monitoring gadget. Random wooded area and AdaBoost have to separate the malware and normal flows flawlessly, due to their ensemble structures, which could classify unbalanced and noisy datasets

scholarly journals Grid-PPPS: A Skyline Method for Efficiently Handling Top-kQueries in Internet of Things

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Sun-Young Ihm ◽  
Aziz Nasridinov ◽  
Young-Ho Park
Keyword(s):  
Internet Of Things ◽  
State Of The Art ◽  
Rapid Development ◽  
The Internet ◽  
Space Partitioning ◽  
Data Space ◽  
Index Building ◽  
Representative Skyline ◽  
The Internet Of Things ◽  
Grid Based

A rapid development in wireless communication and radio frequency technology has enabled the Internet of Things (IoT) to enter every aspect of our life. However, as more and more sensors get connected to the Internet, they generate huge amounts of data. Thus, widespread deployment of IoT requires development of solutions for analyzing the potentially huge amounts of data they generate. A top-kquery processing can be applied to facilitate this task. The top-kqueries retrievektuples with the lowest or the highest scores among all of the tuples in the database. There are many methods to answer top-kqueries, where skyline methods are efficient when considering all attribute values of tuples. The representative skyline methods are soft-filter-skyline (SFS) algorithm, angle-based space partitioning (ABSP), and plane-project-parallel-skyline (PPPS). Among them, PPPS improves ABSP by partitioning data space into a number of spaces using hyperplane projection. However, PPPS has a high index building time in high-dimensional databases. In this paper, we propose a new skyline method (called Grid-PPPS) for efficiently handling top-kqueries in IoT applications. The proposed method first performs grid-based partitioning on data space and then partitions it once again using hyperplane projection. Experimental results show that our method improves the index building time compared to the existing state-of-the-art methods.

scholarly journals Physical Unclonable Functions in the Internet of Things: State of the Art and Open Challenges

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3208 ◽  
Author(s):  
Armin Babaei ◽  
Gregor Schiele
Keyword(s):  
Internet Of Things ◽  
State Of The Art ◽  
The Internet ◽  
Physical Unclonable Functions ◽  
Iot Devices ◽  
Computational Resources ◽  
The Internet Of Things

Attacks on Internet of Things (IoT) devices are on the rise. Physical Unclonable Functions (PUFs) are proposed as a robust and lightweight solution to secure IoT devices. The main advantage of a PUF compared to the current classical cryptographic solutions is its compatibility with IoT devices with limited computational resources. In this paper, we investigate the maturity of this technology and the challenges toward PUF utilization in IoT that still need to be addressed.

scholarly journals MultiFuzz: A Coverage-Based Multiparty-Protocol Fuzzer for IoT Publish/Subscribe Protocols

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5194
Author(s):  
Yingpei Zeng ◽  
Mingmin Lin ◽  
Shanqing Guo ◽  
Yanzhao Shen ◽  
Tingting Cui ◽  
...  
Keyword(s):  
Internet Of Things ◽  
State Of The Art ◽  
State Transitions ◽  
The Internet ◽  
Message Queue ◽  
Single Input ◽  
Constrained Application Protocol ◽  
The Internet Of Things

The publish/subscribe model has gained prominence in the Internet of things (IoT) network, and both Message Queue Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) support it. However, existing coverage-based fuzzers may miss some paths when fuzzing such publish/subscribe protocols, because they implicitly assume that there are only two parties in a protocol, which is not true now since there are three parties, i.e., the publisher, the subscriber and the broker. In this paper, we propose MultiFuzz, a new coverage-based multiparty-protocol fuzzer. First, it embeds multiple-connection information in a single input. Second, it uses a message mutation algorithm to stimulate protocol state transitions, without the need of protocol specifications. Third, it uses a new desockmulti module to feed the network messages into the program under test. desockmulti is similar to desock (Preeny), a tool widely used by the community, but it is specially designed for fuzzing and is 10x faster. We implement MultiFuzz based on AFL, and use it to fuzz two popular projects Eclipse Mosquitto and libCoAP. We reported discovered problems to the projects. In addition, we compare MultiFuzz with AFL and two state-of-the-art fuzzers, MOPT and AFLNET, and find it discovering more paths and crashes.

scholarly journals IOT-BASED SMART ENVIRONMENTS: STATE OF THE ART, SECURITY THREATS AND SOLUTIONS

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 279-286
Author(s):  
G. Ikrissi ◽  
T. Mazri
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
State Of The Art ◽  
The Internet ◽  
Smart Environments ◽  
Security Threats ◽  
Smart Environment ◽  
Security Attacks ◽  
Security Issues ◽  
The Internet Of Things

Abstract. Smart environments provide many benefits to the users including comfort, convenience, energy efficiency, safety, automation, and service quality. The Internet of Things (IoT) has developed to become one of the widely used technologies in smart environments. Many security attacks and threats are generated by security flaws in IoT-based systems and devices, which may affect smart environments applications. As a result, security is one of the most important issues in any smart area or environment based on the IoT model. This paper presents an overview of smart environments based on IoT technology and highlights the main security issues and countermeasures in the four layers of smart environment IoT architecture. It also reviews some of the current solutions that ensure the security of information in smart environments applications.

scholarly journals ProTECting: An Application of Local Differential Privacy for IoT at the Edge in Smart Home Scenarios

2020 ◽  
Author(s):  
Israel De Castro Vidal ◽  
André Luís da Costa Mendonça ◽  
Franck Rousseau ◽  
Javam De Castro Machado
Keyword(s):  
Internet Of Things ◽  
Smart Home ◽  
Differential Privacy ◽  
State Of The Art ◽  
Smart Homes ◽  
The Internet ◽  
Smart Meter ◽  
Conventional Models ◽  
The Internet Of Things

With the growth of the Internet of Things (IoT) and Smart Homes, there is an ever-growing amount of data coming from within people's houses. These data are valuable for analysis and to discover patterns in order to improve services and produce resources more efficiently, e.g., using smart meter data to generate energy with less waste. Despite their high value for analysis, these data are intrinsically private and should be treated carefully. IoT data are fundamentally infinite, and this property makes it even more challenging to apply conventional models to achieve privacy. In this work, we propose a differentially private strategy to estimate frequencies of values in the context of Smart Home data, considering the infinite property of the data and focusing on getting better utility than state of the art.

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