2,079 research outputs found
Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection
In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology
and framework for efficient and effective real-time malware detection,
leveraging the best of conventional machine learning (ML) and deep learning
(DL) algorithms. In PROPEDEUTICA, all software processes in the system start
execution subjected to a conventional ML detector for fast classification. If a
piece of software receives a borderline classification, it is subjected to
further analysis via more performance expensive and more accurate DL methods,
via our newly proposed DL algorithm DEEPMALWARE. Further, we introduce delays
to the execution of software subjected to deep learning analysis as a way to
"buy time" for DL analysis and to rate-limit the impact of possible malware in
the system. We evaluated PROPEDEUTICA with a set of 9,115 malware samples and
877 commonly used benign software samples from various categories for the
Windows OS. Our results show that the false positive rate for conventional ML
methods can reach 20%, and for modern DL methods it is usually below 6%.
However, the classification time for DL can be 100X longer than conventional ML
methods. PROPEDEUTICA improved the detection F1-score from 77.54% (conventional
ML method) to 90.25%, and reduced the detection time by 54.86%. Further, the
percentage of software subjected to DL analysis was approximately 40% on
average. Further, the application of delays in software subjected to ML reduced
the detection time by approximately 10%. Finally, we found and discussed a
discrepancy between the detection accuracy offline (analysis after all traces
are collected) and on-the-fly (analysis in tandem with trace collection). Our
insights show that conventional ML and modern DL-based malware detectors in
isolation cannot meet the needs of efficient and effective malware detection:
high accuracy, low false positive rate, and short classification time.Comment: 17 pages, 7 figure
Multi-level analysis of Malware using Machine Learning
Multi-level analysis of Malware using Machine Learnin
WOPR: A Dynamic Cybersecurity Detection and Response Framework
Malware authors develop software to exploit the flaws in any platform and application which suffers a vulnerability in its defenses, be it through unpatched known attack vectors or zero-day attacks for which there is no current solution. It is the responsibility of cybersecurity personnel to monitor, detect, respond to and protect against such incidents that could affect their organization. Unfortunately, the low number of skilled, available cybersecurity professionals in the job market means that many positions go unfilled and cybersecurity threats are unknowingly allowed to negatively affect many enterprises.The demand for a greater cybersecurity posture has led several organizations to de- velop automated threat analysis tools which can be operated by less-skilled infor- mation security analysts and response teams. However, the diverse needs and organizational factors of most businesses presents a challenge for a “one size fits all” cybersecurity solution. Organizations in different industries may not have the same regulatory and standards compliance concerns due to processing different forms and classifications of data. As a result, many common security solutions are ill equipped to accurately model cybersecurity threats as they relate to each unique organization.We propose WOPR, a framework for automated static and dynamic analysis of software to identify malware threats, classify the nature of those threats, and deliver an appropriate automated incident response. Additionally, WOPR provides the end user the ability to adjust threat models to fit the risks relevant to an organization, allowing for bespoke automated cybersecurity threat management. Finally, WOPR presents a departure from traditional signature-based detection found in anti-virus and intrusion detection systems through learning system-level behavior and matching system calls with malicious behavior
The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis
In recent years, mobile devices (e.g., smartphones and tablets) have met an
increasing commercial success and have become a fundamental element of the
everyday life for billions of people all around the world. Mobile devices are
used not only for traditional communication activities (e.g., voice calls and
messages) but also for more advanced tasks made possible by an enormous amount
of multi-purpose applications (e.g., finance, gaming, and shopping). As a
result, those devices generate a significant network traffic (a consistent part
of the overall Internet traffic). For this reason, the research community has
been investigating security and privacy issues that are related to the network
traffic generated by mobile devices, which could be analyzed to obtain
information useful for a variety of goals (ranging from device security and
network optimization, to fine-grained user profiling).
In this paper, we review the works that contributed to the state of the art
of network traffic analysis targeting mobile devices. In particular, we present
a systematic classification of the works in the literature according to three
criteria: (i) the goal of the analysis; (ii) the point where the network
traffic is captured; and (iii) the targeted mobile platforms. In this survey,
we consider points of capturing such as Wi-Fi Access Points, software
simulation, and inside real mobile devices or emulators. For the surveyed
works, we review and compare analysis techniques, validation methods, and
achieved results. We also discuss possible countermeasures, challenges and
possible directions for future research on mobile traffic analysis and other
emerging domains (e.g., Internet of Things). We believe our survey will be a
reference work for researchers and practitioners in this research field.Comment: 55 page
Predicting Cyber Events by Leveraging Hacker Sentiment
Recent high-profile cyber attacks exemplify why organizations need better
cyber defenses. Cyber threats are hard to accurately predict because attackers
usually try to mask their traces. However, they often discuss exploits and
techniques on hacking forums. The community behavior of the hackers may provide
insights into groups' collective malicious activity. We propose a novel
approach to predict cyber events using sentiment analysis. We test our approach
using cyber attack data from 2 major business organizations. We consider 3
types of events: malicious software installation, malicious destination visits,
and malicious emails that surpassed the target organizations' defenses. We
construct predictive signals by applying sentiment analysis on hacker forum
posts to better understand hacker behavior. We analyze over 400K posts
generated between January 2016 and January 2018 on over 100 hacking forums both
on surface and Dark Web. We find that some forums have significantly more
predictive power than others. Sentiment-based models that leverage specific
forums can outperform state-of-the-art deep learning and time-series models on
forecasting cyber attacks weeks ahead of the events
On the Feasibility of Malware Authorship Attribution
There are many occasions in which the security community is interested to
discover the authorship of malware binaries, either for digital forensics
analysis of malware corpora or for thwarting live threats of malware invasion.
Such a discovery of authorship might be possible due to stylistic features
inherent to software codes written by human programmers. Existing studies of
authorship attribution of general purpose software mainly focus on source code,
which is typically based on the style of programs and environment. However,
those features critically depend on the availability of the program source
code, which is usually not the case when dealing with malware binaries. Such
program binaries often do not retain many semantic or stylistic features due to
the compilation process. Therefore, authorship attribution in the domain of
malware binaries based on features and styles that will survive the compilation
process is challenging. This paper provides the state of the art in this
literature. Further, we analyze the features involved in those techniques. By
using a case study, we identify features that can survive the compilation
process. Finally, we analyze existing works on binary authorship attribution
and study their applicability to real malware binaries.Comment: FPS 201
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