28,297 research outputs found
Stuck in Traffic (SiT) Attacks: A Framework for Identifying Stealthy Attacks that Cause Traffic Congestion
Recent advances in wireless technologies have enabled many new applications
in Intelligent Transportation Systems (ITS) such as collision avoidance,
cooperative driving, congestion avoidance, and traffic optimization. Due to the
vulnerable nature of wireless communication against interference and
intentional jamming, ITS face new challenges to ensure the reliability and the
safety of the overall system. In this paper, we expose a class of stealthy
attacks -- Stuck in Traffic (SiT) attacks -- that aim to cause congestion by
exploiting how drivers make decisions based on smart traffic signs. An attacker
mounting a SiT attack solves a Markov Decision Process problem to find
optimal/suboptimal attack policies in which he/she interferes with a
well-chosen subset of signals that are based on the state of the system. We
apply Approximate Policy Iteration (API) algorithms to derive potent attack
policies. We evaluate their performance on a number of systems and compare them
to other attack policies including random, myopic and DoS attack policies. The
generated policies, albeit suboptimal, are shown to significantly outperform
other attack policies as they maximize the expected cumulative reward from the
standpoint of the attacker
IoT Security Vulnerabilities and Predictive Signal Jamming Attack Analysis in LoRaWAN
Internet of Things (IoT) gains popularity in recent times due to its flexibility, usability, diverse applicability and ease of
deployment. However, the issues related to security is less explored. The IoT devices are light weight in nature and have low
computation power, low battery life and low memory. As incorporating security features are resource expensive, IoT devices are
often found to be less protected and in recent times, more IoT devices have been routinely attacked due to high profile security
flaws. This paper aims to explore the security vulnerabilities of IoT devices particularly that use Low Power Wide Area Networks
(LPWANs). In this work, LoRaWAN based IoT security vulnerabilities are scrutinised and loopholes are identified. An attack was
designed and simulated with the use of a predictive model of the device data generation. The paper demonstrated that by predicting
the data generation model, jamming attack can be carried out to block devices from sending data successfully. This research will
aid in the continual development of any necessary countermeasures and mitigations for LoRaWAN and LPWAN functionality of
IoT networks in general
Malware in the Future? Forecasting of Analyst Detection of Cyber Events
There have been extensive efforts in government, academia, and industry to
anticipate, forecast, and mitigate cyber attacks. A common approach is
time-series forecasting of cyber attacks based on data from network telescopes,
honeypots, and automated intrusion detection/prevention systems. This research
has uncovered key insights such as systematicity in cyber attacks. Here, we
propose an alternate perspective of this problem by performing forecasting of
attacks that are analyst-detected and -verified occurrences of malware. We call
these instances of malware cyber event data. Specifically, our dataset was
analyst-detected incidents from a large operational Computer Security Service
Provider (CSSP) for the U.S. Department of Defense, which rarely relies only on
automated systems. Our data set consists of weekly counts of cyber events over
approximately seven years. Since all cyber events were validated by analysts,
our dataset is unlikely to have false positives which are often endemic in
other sources of data. Further, the higher-quality data could be used for a
number for resource allocation, estimation of security resources, and the
development of effective risk-management strategies. We used a Bayesian State
Space Model for forecasting and found that events one week ahead could be
predicted. To quantify bursts, we used a Markov model. Our findings of
systematicity in analyst-detected cyber attacks are consistent with previous
work using other sources. The advanced information provided by a forecast may
help with threat awareness by providing a probable value and range for future
cyber events one week ahead. Other potential applications for cyber event
forecasting include proactive allocation of resources and capabilities for
cyber defense (e.g., analyst staffing and sensor configuration) in CSSPs.
Enhanced threat awareness may improve cybersecurity.Comment: Revised version resubmitted to journa
The Mason Test: A Defense Against Sybil Attacks in Wireless Networks Without Trusted Authorities
Wireless networks are vulnerable to Sybil attacks, in which a malicious node
poses as many identities in order to gain disproportionate influence. Many
defenses based on spatial variability of wireless channels exist, but depend
either on detailed, multi-tap channel estimation - something not exposed on
commodity 802.11 devices - or valid RSSI observations from multiple trusted
sources, e.g., corporate access points - something not directly available in ad
hoc and delay-tolerant networks with potentially malicious neighbors. We extend
these techniques to be practical for wireless ad hoc networks of commodity
802.11 devices. Specifically, we propose two efficient methods for separating
the valid RSSI observations of behaving nodes from those falsified by malicious
participants. Further, we note that prior signalprint methods are easily
defeated by mobile attackers and develop an appropriate challenge-response
defense. Finally, we present the Mason test, the first implementation of these
techniques for ad hoc and delay-tolerant networks of commodity 802.11 devices.
We illustrate its performance in several real-world scenarios
The Cycle of (Legal) Violence? Child Abuse and Military Aspirations
Most prior research on military enlistment has focused on characteristics that can be used to identify potential recruits, but has rarely looked at the psychological histories of those recruits. Data on Wisconsin seniors in 1957 from the Wisconsin Longitudinal Study was used to build a large profile of socio-economic controls for testing the “cycle of violence” hypothesis – that physical abuse in childhood leads to violent adult impulses – as manifested through aspirations for a military career. Results were generated using a probit model with reported military aspirations as the dependent variable. For (mostly Caucasian) male Wisconsin respondents in 1957, retrospective self-reports of physical abuse by the respondents’ fathers was associated with an (average) increase in probability of an aspiration to a military career of approximately 8%, which may be underestimated due to underreporting of abuse. The relationship of military aspiration to verbal abuse and physical abuse by the respondent’s mother was unclear, likely due to collinearity or alternative, negative abuse outcomes that make military life unappealing. There are two significant implications to these results: first, that military employment serves as a psychologically similar but alternative outcome to domestic abuse or violent crime, except without the associated stigma; and second, that military life presents challenges that reward psychological adaptations and defenses deriving from childhood victimization, thereby increasing its appeal to child abuse victims.
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