17,675 research outputs found
Classifying Web Exploits with Topic Modeling
This short empirical paper investigates how well topic modeling and database
meta-data characteristics can classify web and other proof-of-concept (PoC)
exploits for publicly disclosed software vulnerabilities. By using a dataset
comprised of over 36 thousand PoC exploits, near a 0.9 accuracy rate is
obtained in the empirical experiment. Text mining and topic modeling are a
significant boost factor behind this classification performance. In addition to
these empirical results, the paper contributes to the research tradition of
enhancing software vulnerability information with text mining, providing also a
few scholarly observations about the potential for semi-automatic
classification of exploits in the existing tracking infrastructures.Comment: Proceedings of the 2017 28th International Workshop on Database and
Expert Systems Applications (DEXA).
http://ieeexplore.ieee.org/abstract/document/8049693
Impact assessment for vulnerabilities in open-source software libraries
Software applications integrate more and more open-source software (OSS) to
benefit from code reuse. As a drawback, each vulnerability discovered in
bundled OSS potentially affects the application. Upon the disclosure of every
new vulnerability, the application vendor has to decide whether it is
exploitable in his particular usage context, hence, whether users require an
urgent application patch containing a non-vulnerable version of the OSS.
Current decision making is mostly based on high-level vulnerability
descriptions and expert knowledge, thus, effort intense and error prone. This
paper proposes a pragmatic approach to facilitate the impact assessment,
describes a proof-of-concept for Java, and examines one example vulnerability
as case study. The approach is independent from specific kinds of
vulnerabilities or programming languages and can deliver immediate results
A Practical Approach to Protect IoT Devices against Attacks and Compile Security Incident Datasets
open access articleThe Internet of Things (IoT) introduced the opportunity of remotely manipulating home appliances (such as heating systems, ovens, blinds, etc.) using computers and mobile devices. This idea fascinated people and originated a boom of IoT devices together with an increasing demand that was difficult to support. Many manufacturers quickly created hundreds of devices implementing functionalities but neglected some critical issues pertaining to device security. This oversight gave rise to the current situation where thousands of devices remain unpatched having many security issues that manufacturers cannot address after the devices have been produced and deployed. This article presents our novel research protecting IOT devices using Berkeley Packet Filters (BPFs) and evaluates our findings with the aid of our Filter.tlk tool, which is able to facilitate the development of BPF expressions that can be executed by GNU/Linux systems with a low impact on network packet throughput
Experimental Analysis of Subscribers' Privacy Exposure by LTE Paging
Over the last years, considerable attention has been given to the privacy of
individuals in wireless environments. Although significantly improved over the
previous generations of mobile networks, LTE still exposes vulnerabilities that
attackers can exploit. This might be the case of paging messages, wake-up
notifications that target specific subscribers, and that are broadcasted in
clear over the radio interface. If they are not properly implemented, paging
messages can expose the identity of subscribers and furthermore provide
information about their location. It is therefore important that mobile network
operators comply with the recommendations and implement the appropriate
mechanisms to mitigate attacks. In this paper, we verify by experiment that
paging messages can be captured and decoded by using minimal technical skills
and publicly available tools. Moreover, we present a general experimental
method to test privacy exposure by LTE paging messages, and we conduct a case
study on three different LTE mobile operators
IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT
With the rapid growth of the Internet-of-Things (IoT), concerns about the
security of IoT devices have become prominent. Several vendors are producing
IP-connected devices for home and small office networks that often suffer from
flawed security designs and implementations. They also tend to lack mechanisms
for firmware updates or patches that can help eliminate security
vulnerabilities. Securing networks where the presence of such vulnerable
devices is given, requires a brownfield approach: applying necessary protection
measures within the network so that potentially vulnerable devices can coexist
without endangering the security of other devices in the same network. In this
paper, we present IOT SENTINEL, a system capable of automatically identifying
the types of devices being connected to an IoT network and enabling enforcement
of rules for constraining the communications of vulnerable devices so as to
minimize damage resulting from their compromise. We show that IOT SENTINEL is
effective in identifying device types and has minimal performance overhead
Methodologies to develop quantitative risk evaluation metrics
The goal of this work is to advance a new methodology to measure a severity cost for each host using the Common Vulnerability Scoring System (CVSS) based on base, temporal and environmental metrics by combining related sub-scores to produce a unique severity cost by modeling the problem's parameters in to a mathematical framework. We build our own CVSS Calculator using our equations to simplify the calculations of the vulnerabilities scores and to benchmark with other models. We design and develop a new approach to represent the cost assigned to each host by dividing the scores of the vulnerabilities to two main levels of privileges, user and root, and we classify these levels into operational levels to identify and calculate the severity cost of multi steps vulnerabilities. Finally we implement our framework on a simple network, using Nessus scanner as tool to discover known vulnerabilities and to implement the results to build and represent our cost centric attack graph
- …