49,069 research outputs found
On Security Research Towards Future Mobile Network Generations
Over the last decades, numerous security and privacy issues in all three
active mobile network generations have been revealed that threaten users as
well as network providers. In view of the newest generation (5G) currently
under development, we now have the unique opportunity to identify research
directions for the next generation based on existing security and privacy
issues as well as already proposed defenses. This paper aims to unify security
knowledge on mobile phone networks into a comprehensive overview and to derive
pressing open research questions. To achieve this systematically, we develop a
methodology that categorizes known attacks by their aim, proposed defenses,
underlying causes, and root causes. Further, we assess the impact and the
efficacy of each attack and defense. We then apply this methodology to existing
literature on attacks and defenses in all three network generations. By doing
so, we identify ten causes and four root causes of attacks. Mapping the attacks
to proposed defenses and suggestions for the 5G specification enables us to
uncover open research questions and challenges for the development of
next-generation mobile networks. The problems of unsecured pre-authentication
traffic and jamming attacks exist across all three mobile generations. They
should be addressed in the future, in particular, to wipe out the class of
downgrade attacks and, thereby, strengthen the users' privacy. Further advances
are needed in the areas of inter-operator protocols as well as secure baseband
implementations. Additionally, mitigations against denial-of-service attacks by
smart protocol design represent an open research question.Comment: Survey: 198 citations, 25 pages, 4 tables, 3 figure
Aware: Controlling App Access to I/O Devices on Mobile Platforms
Smartphones' cameras, microphones, and device displays enable users to
capture and view memorable moments of their lives. However, adversaries can
trick users into authorizing malicious apps that exploit weaknesses in current
mobile platforms to misuse such on-board I/O devices to stealthily capture
photos, videos, and screen content without the users' consent. Contemporary
mobile operating systems fail to prevent such misuse of I/O devices by
authorized apps due to lack of binding between users' interactions and accesses
to I/O devices performed by these apps. In this paper, we propose Aware, a
security framework for authorizing app requests to perform operations using I/O
devices, which binds app requests with user intentions to make all uses of
certain I/O devices explicit. We evaluate our defense mechanisms through
laboratory-based experimentation and a user study, involving 74 human subjects,
whose ability to identify undesired operations targeting I/O devices increased
significantly. Without Aware, only 18% of the participants were able to
identify attacks from tested RAT apps. Aware systematically blocks all the
attacks in absence of user consent and supports users in identifying 82% of
social-engineering attacks tested to hijack approved requests, including some
more sophisticated forms of social engineering not yet present in available
RATs. Aware introduces only 4.79% maximum performance overhead over operations
targeting I/O devices. Aware shows that a combination of system defenses and
user interface can significantly strengthen defenses for controlling the use of
on-board I/O devices
A Semi-distributed Reputation Based Intrusion Detection System for Mobile Adhoc Networks
A Mobile Adhoc Network (MANET) is a cooperative engagement of a collection of
mobile nodes without any centralized access point or infrastructure to
coordinate among the peers. The underlying concept of coordination among nodes
in a cooperative MANET has induced in them a vulnerability to attacks due to
issues like lack of fixed infrastructure, dynamically changing network
topology, cooperative algorithms, lack of centralized monitoring and management
point, and lack of a clear line of defense. We propose a semi-distributed
approach towards Reputation Based Intrusion Detection System (IDS) that
combines with the DSR routing protocol for strengthening the defense of a
MANET. Our system inherits the features of reputation from human behavior,
hence making the IDS socially inspired. It has a semi-distributed architecture
as the critical observation results of the system are neither spread globally
nor restricted locally. The system assigns maximum weightage to self
observation by nodes for updating any reputation values, thus avoiding the need
of a trust relationship between nodes. Our system is also unique in the sense
that it features the concepts of Redemption and Fading with a robust Path
Manager and Monitor system. Simulation studies show that DSR fortified with our
system outperforms normal DSR in terms of the packet delivery ratio and routing
overhead even when up to half of nodes in the network behave as malicious.
Various parameters introduced such as timing window size, reputation update
values, congestion parameter and other thresholds have been optimized over
several simulation test runs of the system. By combining the semi-distributed
architecture and other design essentials like path manager, monitor module,
redemption and fading concepts; Our system proves to be robust enough to
counter most common attacks in MANETs.Comment: Adhoc Networking, Security, Promiscuous Mode, Reputation Based
Intrusion Detection Syste
Bluetooth Security Protocol Analysis and Improvements
Since its creation, Bluetooth has transformed itself from a cable replacement technology to a wireless technology that connects people and machines. Bluetooth has been widely adapted on mobile phones and PDAs. Many other vendors in other industries are integrating Bluetooth into their products. Although vendors are adapting to the technology, Bluetooth hasnât been a big hit among users. Security remains a major concern. Poor implementation of the Bluetooth architecture on mobile devices leads to some high profiled Bluetooth hacks. Weak security protocol designs expose the Bluetooth system to some devastating protocol attacks. This paper first explores four Bluetooth protocol-level attacks in order to get deeper insights into the weakness of the Bluetooth security design. It then proposes enhancements to defense against those attacks. Performance comparison will be given based on the implementation of those enhancements on a software based Bluetooth simulator
Patrol Detection for Replica Attacks on Wireless Sensor Networks
Replica attack is a critical concern in the security of wireless sensor networks. We employ mobile nodes as patrollers to detect replicas distributed in different zones in a network, in which a basic patrol detection protocol and two detection algorithms for stationary and mobile modes are presented. Then we perform security analysis to discuss the defense strategies against the possible attacks on the proposed detection protocol. Moreover, we show the advantages of the proposed protocol by discussing and comparing the communication cost and detection probability with some existing methods
Spear or Shield: Leveraging Generative AI to Tackle Security Threats of Intelligent Network Services
Generative AI (GAI) models have been rapidly advancing, with a wide range of
applications including intelligent networks and mobile AI-generated content
(AIGC) services. Despite their numerous applications and potential, such models
create opportunities for novel security challenges. In this paper, we examine
the challenges and opportunities of GAI in the realm of the security of
intelligent network AIGC services such as suggesting security policies, acting
as both a ``spear'' for potential attacks and a ``shield'' as an integral part
of various defense mechanisms. First, we present a comprehensive overview of
the GAI landscape, highlighting its applications and the techniques
underpinning these advancements, especially large language and diffusion
models. Then, we investigate the dynamic interplay between GAI's spear and
shield roles, highlighting two primary categories of potential GAI-related
attacks and their respective defense strategies within wireless networks. A
case study illustrates the impact of GAI defense strategies on energy
consumption in an image request scenario under data poisoning attack. Our
results show that by employing an AI-optimized diffusion defense mechanism,
energy can be reduced by 8.7%, and retransmission count can be decreased from
32 images, without defense, to just 6 images, showcasing the effectiveness of
GAI in enhancing network security
On Defending Against Label Flipping Attacks on Malware Detection Systems
Label manipulation attacks are a subclass of data poisoning attacks in
adversarial machine learning used against different applications, such as
malware detection. These types of attacks represent a serious threat to
detection systems in environments having high noise rate or uncertainty, such
as complex networks and Internet of Thing (IoT). Recent work in the literature
has suggested using the -Nearest Neighboring (KNN) algorithm to defend
against such attacks. However, such an approach can suffer from low to wrong
detection accuracy. In this paper, we design an architecture to tackle the
Android malware detection problem in IoT systems. We develop an attack
mechanism based on Silhouette clustering method, modified for mobile Android
platforms. We proposed two Convolutional Neural Network (CNN)-type deep
learning algorithms against this \emph{Silhouette Clustering-based Label
Flipping Attack (SCLFA)}. We show the effectiveness of these two defense
algorithms - \emph{Label-based Semi-supervised Defense (LSD)} and
\emph{clustering-based Semi-supervised Defense (CSD)} - in correcting labels
being attacked. We evaluate the performance of the proposed algorithms by
varying the various machine learning parameters on three Android datasets:
Drebin, Contagio, and Genome and three types of features: API, intent, and
permission. Our evaluation shows that using random forest feature selection and
varying ratios of features can result in an improvement of up to 19\% accuracy
when compared with the state-of-the-art method in the literature.Comment: 21 pages, 6 figures, 4 tables, NCAA Springer Journa
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning
Users in various web and mobile applications are vulnerable to attribute
inference attacks, in which an attacker leverages a machine learning classifier
to infer a target user's private attributes (e.g., location, sexual
orientation, political view) from its public data (e.g., rating scores, page
likes). Existing defenses leverage game theory or heuristics based on
correlations between the public data and attributes. These defenses are not
practical. Specifically, game-theoretic defenses require solving intractable
optimization problems, while correlation-based defenses incur large utility
loss of users' public data.
In this paper, we present AttriGuard, a practical defense against attribute
inference attacks. AttriGuard is computationally tractable and has small
utility loss. Our AttriGuard works in two phases. Suppose we aim to protect a
user's private attribute. In Phase I, for each value of the attribute, we find
a minimum noise such that if we add the noise to the user's public data, then
the attacker's classifier is very likely to infer the attribute value for the
user. We find the minimum noise via adapting existing evasion attacks in
adversarial machine learning. In Phase II, we sample one attribute value
according to a certain probability distribution and add the corresponding noise
found in Phase I to the user's public data. We formulate finding the
probability distribution as solving a constrained convex optimization problem.
We extensively evaluate AttriGuard and compare it with existing methods using a
real-world dataset. Our results show that AttriGuard substantially outperforms
existing methods. Our work is the first one that shows evasion attacks can be
used as defensive techniques for privacy protection.Comment: 27th Usenix Security Symposium, Privacy protection using adversarial
example
Security in Mobile Edge Caching with Reinforcement Learning
Mobile edge computing usually uses cache to support multimedia contents in 5G
mobile Internet to reduce the computing overhead and latency. Mobile edge
caching (MEC) systems are vulnerable to various attacks such as denial of
service attacks and rogue edge attacks. This article investigates the attack
models in MEC systems, focusing on both the mobile offloading and the caching
procedures. In this paper, we propose security solutions that apply
reinforcement learning (RL) techniques to provide secure offloading to the edge
nodes against jamming attacks. We also present light-weight authentication and
secure collaborative caching schemes to protect data privacy. We evaluate the
performance of the RL-based security solution for mobile edge caching and
discuss the challenges that need to be addressed in the future
Security and Privacy Challenges in Cognitive Wireless Sensor Networks
Wireless sensor networks (WSNs) have attracted a lot of interest in the
research community due to their potential applicability in a wide range of
real-world practical applications. However, due to the distributed nature and
their deployments in critical applications without human interventions and
sensitivity and criticality of data communicated, these networks are vulnerable
to numerous security and privacy threats that can adversely affect their
performance. These issues become even more critical in cognitive wireless
sensor networks (CWSNs) in which the sensor nodes have the capabilities of
changing their transmission and reception parameters according to the radio
environment under which they operate in order to achieve reliable and efficient
communication and optimum utilization of the network resources. This chapter
presents a comprehensive discussion on the security and privacy issues in CWSNs
by identifying various security threats in these networks and various defense
mechanisms to counter these vulnerabilities. Various types of attacks on CWSNs
are categorized under different classes based on their natures and targets, and
corresponding to each attack class, appropriate security mechanisms are also
discussed. Some critical research issues on security and privacy in CWSNs are
also identified.Comment: 36 pages, 4 figures, 2 tables. The book chapter is accepted for
publication in 201
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