scholarly journals Keeping the Smart Home Private with Smart(er) IoT Traffic Shaping

2019 ◽  
Vol 2019 (3) ◽  
pp. 128-148 ◽  
Author(s):  
Noah Apthorpe ◽  
Danny Yuxing Huang ◽  
Dillon Reisman ◽  
Arvind Narayanan ◽  
Nick Feamster
Keyword(s):  
Smart Home ◽  
Transport Layer ◽  
Virtual Private Networks ◽  
Traffic Patterns ◽  
Traffic Shaping ◽  
User Interactions ◽  
Internet Service ◽  
Passive Network ◽  
Iot Devices ◽  
User Activities

Abstract The proliferation of smart home Internet of things (IoT) devices presents unprecedented challenges for preserving privacy within the home. In this paper, we demonstrate that a passive network observer (e.g., an Internet service provider) can infer private in-home activities by analyzing Internet traffic from commercially available smart home devices even when the devices use end-to-end transport-layer encryption. We evaluate common approaches for defending against these types of traffic analysis attacks, including firewalls, virtual private networks, and independent link padding, and find that none sufficiently conceal user activities with reasonable data overhead. We develop a new defense, “stochastic traffic padding” (STP), that makes it difficult for a passive network adversary to reliably distinguish genuine user activities from generated traffic patterns designed to look like user interactions. Our analysis provides a theoretical bound on an adversary’s ability to accurately detect genuine user activities as a function of the amount of additional cover traffic generated by the defense technique.

scholarly journals A egis +

2021 ◽  
Vol 2 (1) ◽  
pp. 1-33
Author(s):  
Amit Kumar Sikder ◽  
Leonardo Babun ◽  
A. Selcuk Uluagac
Keyword(s):  
Smart Home ◽  
Real Life ◽  
Security Risks ◽  
User Interactions ◽  
Malicious Behavior ◽  
Usage Patterns ◽  
Device User ◽  
And Performance ◽  
Malicious Behaviors ◽  
User Activities

The introduction of modern Smart Home Systems (SHSs) is redefining the way we perform everyday activities. Today, myriad SHS applications and the devices they control are widely available to users. Specifically, users can easily download and install the apps from vendor-specific app markets, or develop their own, to effectively implement their SHS solutions. However, despite their benefits, app-based SHSs unfold diverse security risks. Several attacks have already been reported to SHSs and current security solutions only consider smart home devices and apps individually to detect malicious actions, rather than the context of the SHS as a whole. Thus, the current security solutions applied to SHSs cannot capture user activities and sensor-device-user interactions in a holistic fashion. To address these limitations, in this article, we introduce A egis +, a novel context-aware platform-independent security framework to detect malicious behavior in an SHS. Specifically, A egis + observes the states of the connected smart home entities (sensors and devices) for different user activities and usage patterns in an SHS and builds a contextual model to differentiate between malicious and benign behavior. We evaluated the efficacy and performance of A egis + in multiple smart home settings (i.e., single bedroom, double bedroom, duplex) and platforms (i.e., Samsung SmartThings, Amazon Alexa) where real users perform day-to-day activities using real SHS devices. We also measured the performance of A egis + against five different malicious behaviors. Our detailed evaluation shows that A egis + can detect malicious behavior in SHS with high accuracy (over 95%) and secure the SHS regardless of the smart home layout and platforms, device configurations, installed apps, controller devices, and enforced user policies. Finally, A egis + yields minimum overhead to the SHS, ensuring effective deployability in real-life smart environments.

scholarly journals IoT Traffic: Modeling and Measurement Experiments

IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 140-162
Author(s):  
Hung Nguyen-An ◽  
Thomas Silverston ◽  
Taku Yamazaki ◽  
Takumi Miyoshi
Keyword(s):  
Smart Home ◽  
Large Scale ◽  
Traffic Modeling ◽  
The Novel ◽  
Network Behavior ◽  
Performance Accuracy ◽  
Traffic Generator ◽  
Multiple Devices ◽  
Iot Devices ◽  
The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

scholarly journals GAMPAL: an anomaly detection mechanism for Internet backbone traffic by flow size prediction with LSTM-RNN

2021 ◽  
Author(s):  
Taku Wakui ◽  
Takao Kondo ◽  
Fumio Teraoka
Keyword(s):  
Anomaly Detection ◽  
Short Term Memory ◽  
Denial Of Service ◽  
General Purpose ◽  
Traffic Information ◽  
Traffic Patterns ◽  
Detection Mechanism ◽  
Internet Service ◽  
Internet Backbone ◽  
Backbone Network

AbstractThis paper proposes a general-purpose anomaly detection mechanism for Internet backbone traffic named GAMPAL (General-purpose Anomaly detection Mechanism using Prefix Aggregate without Labeled data). GAMPAL does not require labeled data to achieve general-purpose anomaly detection. For scalability to the number of entries in the BGP RIB (Border Gateway Protocol Routing Information Base), GAMPAL introduces prefix aggregate. The BGP RIB entries are classified into prefix aggregates, each of which is identified with the first three AS (Autonomous System) numbers in the AS_PATH attribute. GAMPAL establishes a prediction model for traffic sizes based on past traffic sizes. It adopts a LSTM-RNN (Long Short-Term Memory Recurrent Neural Network) model that focuses on the periodicity of the Internet traffic patterns at a weekly scale. The validity of GAMPAL is evaluated using real traffic information, BGP RIBs exported from the WIDE backbone network (AS2500), a nationwide backbone network for research and educational organizations in Japan, and the dataset of an ISP (Internet Service Provider) in Spain. As a result, GAMPAL successfully detects anomalies such as increased traffic due to an event, DDoS (Distributed Denial of Service) attacks targeted at a stub organization, a connection failure, an SSH (Secure Shell) scan attack, and anomaly spam.

scholarly journals A Secure and Scalable Smart Home Gateway to Bridge Technology Fragmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3587
Author(s):  
Ezequiel Simeoni ◽  
Eugenio Gaeta ◽  
Rebeca I. García-Betances ◽  
Dave Raggett ◽  
Alejandro M. Medrano-Gil ◽  
...  
Keyword(s):  
Smart Home ◽  
Home Automation ◽  
Dynamic Configuration ◽  
Home Gateway ◽  
Security Authentication ◽  
Living Lab ◽  
Automation Systems ◽  
Authentication And Authorization ◽  
Iot Devices ◽  
Bridge Technology

Internet of Things (IoT) technologies are already playing an important role in our daily activities as we use them and rely on them to increase our abilities, connectivity, productivity and quality of life. However, there are still obstacles to achieving a unique interface able to transfer full control to users given the diversity of protocols, properties and specifications in the varied IoT ecosystem. Particularly for the case of home automation systems, there is a high degree of fragmentation that limits interoperability, increasing the complexity and costs of developments and holding back their real potential of positively impacting users. In this article, we propose implementing W3C’s Web of Things Standard supported by home automation ontologies, such as SAREF and UniversAAL, to deploy the Living Lab Gateway that allows users to consume all IoT devices from a smart home, including those physically wired and using KNX® technology. This work, developed under the framework of the EC funded Plan4Act project, includes relevant features such as security, authentication and authorization provision, dynamic configuration and injection of devices, and devices abstraction and mapping into ontologies. Its deployment is explained in two scenarios to show the achieved technology’s degree of integration, the code simplicity for developers and the system’s scalability: one consisted of external hardware interfacing with the smart home, and the other of the injection of a new sensing device. A test was executed providing metrics that indicate that the Living Lab Gateway is competitive in terms of response performance.

HomeTec Software for Security Aspects of Smart Home Devices Based on IoT

2021 ◽  
pp. 5-16
Author(s):  
Parth Rustagi ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Smart Home ◽  
Denial Of Service ◽  
Security Protocols ◽  
The Internet ◽  
Security Threats ◽  
Deep Dive ◽  
Cost Cutting ◽  
Full Control ◽  
Iot Devices ◽  
Service Data

As useful as it gets to connect devices to the internet to make life easier and more comfortable, it also opens the gates to various cyber threats. The connection of Smart Home devices to the internet makes them vulnerable to malicious hackers that infiltrate the system. Hackers can penetrate these systems and have full control over devices. This can lead to denial of service, data leakage, invasion of privacy, etc. Thus security is a major aspect of Smart home devices. However, many companies manufacturing these Smart Home devices have little to no security protocols in their devices. In the process of making the IoT devices cheaper, various cost-cutting is done on the security protocols in IoT devices. In some way, many manufactures of the devices don’t even consider this as a factor to build upon. This leaves the devices vulnerable to attacks. Various authorities have worked upon to standardize the security aspects for the IoT and listed out guidelines for manufactures to follow, but many fail to abide by them. This paper introduces and talks about the various threats, various Security threats to Smart Home devices. It takes a deep dive into the solutions for the discussed threats. It also discusses their prevention. Lastly, it discusses various preventive measures and good practices to be incorporated to protect devices from any future attacks.

scholarly journals DDoS Detection on Internet of Things using Unsupervised Algorithms

2021 ◽  
Vol 297 ◽  
pp. 01005
Author(s):  
Hailyie Tekleselassie
Keyword(s):  
Learning Algorithms ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Transport Layer ◽  
Accuracy Score ◽  
Ddos Attacks ◽  
Knowledge Based ◽  
Ddos Detection ◽  
Iot Devices ◽  
Unsupervised Algorithms

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either “abnormal” or “normal” using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

scholarly journals Towards Secure and Privacy-Preserving IoT Enabled Smart Home: Architecture and Experimental Study

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6131
Author(s):  
Mamun Abu-Tair ◽  
Soufiene Djahel ◽  
Philip Perry ◽  
Bryan Scotney ◽  
Unsub Zia ◽  
...  
Keyword(s):  
Experimental Study ◽  
Smart Home ◽  
Privacy Preservation ◽  
Smart Cities ◽  
Security And Privacy ◽  
Test Cases ◽  
Security Threats ◽  
Hardware Implementations ◽  
Cryptographic Algorithms ◽  
Iot Devices

Internet of Things (IoT) technology is increasingly pervasive in all aspects of our life and its usage is anticipated to significantly increase in future Smart Cities to support their myriad of revolutionary applications. This paper introduces a new architecture that can support several IoT-enabled smart home use cases, with a specified level of security and privacy preservation. The security threats that may target such an architecture are highlighted along with the cryptographic algorithms that can prevent them. An experimental study is performed to provide more insights about the suitability of several lightweight cryptographic algorithms for use in securing the constrained IoT devices used in the proposed architecture. The obtained results showed that many modern lightweight symmetric cryptography algorithms, as CLEFIA and TRIVIUM, are optimized for hardware implementations and can consume up to 10 times more energy than the legacy techniques when they are implemented in software. Moreover, the experiments results highlight that CLEFIA significantly outperforms TRIVIUM under all of the investigated test cases, and the latter performs 100 times worse than the legacy cryptographic algorithms tested.

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