A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system

A Gaurav, BB Gupta, PK Panigrahi - Enterprise Information …, 2023 - Taylor & Francis
ABSTRACT The Internet of Things (IoT) is a relatively new technology that has piqued
academics' and business information systems' attention in recent years. The Internet of …

Systematic classification of side-channel attacks: A case study for mobile devices

R Spreitzer, V Moonsamy, T Korak… - … surveys & tutorials, 2017 - ieeexplore.ieee.org
Side-channel attacks on mobile devices have gained increasing attention since their
introduction in 2007. While traditional side-channel attacks, such as power analysis attacks …

Robust smartphone app identification via encrypted network traffic analysis

VF Taylor, R Spolaor, M Conti… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
The apps installed on a smartphone can reveal much information about a user, such as their
medical conditions, sexual orientation, or religious beliefs. In addition, the presence or …

Flowprint: Semi-supervised mobile-app fingerprinting on encrypted network traffic

T Van Ede, R Bortolameotti, A Continella… - Network and distributed …, 2020 - par.nsf.gov
Mobile-application fingerprinting of network traffic is valuable for many security solutions as
it provides insights into the apps active on a network. Unfortunately, existing techniques …

Mamadroid: Detecting android malware by building markov chains of behavioral models (extended version)

L Onwuzurike, E Mariconti, P Andriotis… - ACM Transactions on …, 2019 - dl.acm.org
As Android has become increasingly popular, so has malware targeting it, thus motivating
the research community to propose different detection techniques. However, the constant …

Fuzzing: State of the art

H Liang, X Pei, X Jia, W Shen… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
As one of the most popular software testing techniques, fuzzing can find a variety of
weaknesses in a program, such as software bugs and vulnerabilities, by generating …

Mamadroid: Detecting android malware by building markov chains of behavioral models

E Mariconti, L Onwuzurike, P Andriotis… - arXiv preprint arXiv …, 2016 - arxiv.org
The rise in popularity of the Android platform has resulted in an explosion of malware threats
targeting it. As both Android malware and the operating system itself constantly evolve, it is …

Appscanner: Automatic fingerprinting of smartphone apps from encrypted network traffic

VF Taylor, R Spolaor, M Conti… - 2016 IEEE European …, 2016 - ieeexplore.ieee.org
Automatic fingerprinting and identification of smartphone apps is becoming a very attractive
data gathering technique for adversaries, network administrators, investigators and …

Analyzing android encrypted network traffic to identify user actions

M Conti, LV Mancini, R Spolaor… - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
Mobile devices can be maliciously exploited to violate the privacy of people. In most attack
scenarios, the adversary takes the local or remote control of the mobile device, by …

Multi-classification approaches for classifying mobile app traffic

G Aceto, D Ciuonzo, A Montieri, A Pescapé - Journal of Network and …, 2018 - Elsevier
The growing usage of smartphones in everyday life is deeply (and rapidly) changing the
nature of traffic traversing home and enterprise networks, and the Internet. Different tools …