1,415 research outputs found
Using HTML5 to Prevent Detection of Drive-by-Download Web Malware
The web is experiencing an explosive growth in the last years. New
technologies are introduced at a very fast-pace with the aim of narrowing the
gap between web-based applications and traditional desktop applications. The
results are web applications that look and feel almost like desktop
applications while retaining the advantages of being originated from the web.
However, these advancements come at a price. The same technologies used to
build responsive, pleasant and fully-featured web applications, can also be
used to write web malware able to escape detection systems. In this article we
present new obfuscation techniques, based on some of the features of the
upcoming HTML5 standard, which can be used to deceive malware detection
systems. The proposed techniques have been experimented on a reference set of
obfuscated malware. Our results show that the malware rewritten using our
obfuscation techniques go undetected while being analyzed by a large number of
detection systems. The same detection systems were able to correctly identify
the same malware in its original unobfuscated form. We also provide some hints
about how the existing malware detection systems can be modified in order to
cope with these new techniques.Comment: This is the pre-peer reviewed version of the article: \emph{Using
HTML5 to Prevent Detection of Drive-by-Download Web Malware}, which has been
published in final form at \url{http://dx.doi.org/10.1002/sec.1077}. This
article may be used for non-commercial purposes in accordance with Wiley
Terms and Conditions for Self-Archivin
NEMESYS: Enhanced Network Security for Seamless Service Provisioning in the Smart Mobile Ecosystem
As a consequence of the growing popularity of smart mobile devices, mobile
malware is clearly on the rise, with attackers targeting valuable user
information and exploiting vulnerabilities of the mobile ecosystems. With the
emergence of large-scale mobile botnets, smartphones can also be used to launch
attacks on mobile networks. The NEMESYS project will develop novel security
technologies for seamless service provisioning in the smart mobile ecosystem,
and improve mobile network security through better understanding of the threat
landscape. NEMESYS will gather and analyze information about the nature of
cyber-attacks targeting mobile users and the mobile network so that appropriate
counter-measures can be taken. We will develop a data collection infrastructure
that incorporates virtualized mobile honeypots and a honeyclient, to gather,
detect and provide early warning of mobile attacks and better understand the
modus operandi of cyber-criminals that target mobile devices. By correlating
the extracted information with the known patterns of attacks from wireline
networks, we will reveal and identify trends in the way that cyber-criminals
launch attacks against mobile devices.Comment: Accepted for publication in Proceedings of the 28th International
Symposium on Computer and Information Sciences (ISCIS'13); 9 pages; 1 figur
Spatiotemporal patterns and predictability of cyberattacks
A relatively unexplored issue in cybersecurity science and engineering is
whether there exist intrinsic patterns of cyberattacks. Conventional wisdom
favors absence of such patterns due to the overwhelming complexity of the
modern cyberspace. Surprisingly, through a detailed analysis of an extensive
data set that records the time-dependent frequencies of attacks over a
relatively wide range of consecutive IP addresses, we successfully uncover
intrinsic spatiotemporal patterns underlying cyberattacks, where the term
"spatio" refers to the IP address space. In particular, we focus on analyzing
{\em macroscopic} properties of the attack traffic flows and identify two main
patterns with distinct spatiotemporal characteristics: deterministic and
stochastic. Strikingly, there are very few sets of major attackers committing
almost all the attacks, since their attack "fingerprints" and target selection
scheme can be unequivocally identified according to the very limited number of
unique spatiotemporal characteristics, each of which only exists on a
consecutive IP region and differs significantly from the others. We utilize a
number of quantitative measures, including the flux-fluctuation law, the Markov
state transition probability matrix, and predictability measures, to
characterize the attack patterns in a comprehensive manner. A general finding
is that the attack patterns possess high degrees of predictability, potentially
paving the way to anticipating and, consequently, mitigating or even preventing
large-scale cyberattacks using macroscopic approaches
Spatiotemporal Patterns and Predictability of Cyberattacks
Y.C.L. was supported by Air Force Office of Scientific Research (AFOSR) under grant no. FA9550-10-1-0083 and Army Research Office (ARO) under grant no. W911NF-14-1-0504. S.X. was supported by Army Research Office (ARO) under grant no. W911NF-13-1-0141. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
A local feature engineering strategy to improve network anomaly detection
The dramatic increase in devices and services that has characterized modern societies in recent decades, boosted by the exponential growth of ever faster network connections and the predominant use of wireless connection technologies, has materialized a very crucial challenge in terms of security. The anomaly-based intrusion detection systems, which for a long time have represented some of the most efficient solutions to detect intrusion attempts on a network, have to face this new and more complicated scenario. Well-known problems, such as the difficulty of distinguishing legitimate activities from illegitimate ones due to their similar characteristics and their high degree of heterogeneity, today have become even more complex, considering the increase in the network activity. After providing an extensive overview of the scenario under consideration, this work proposes a Local Feature Engineering (LFE) strategy aimed to face such problems through the adoption of a data preprocessing strategy that reduces the number of possible network event patterns, increasing at the same time their characterization. Unlike the canonical feature engineering approaches, which take into account the entire dataset, it operates locally in the feature space of each single event. The experiments conducted on real-world data showed that this strategy, which is based on the introduction of new features and the discretization of their values, improves the performance of the canonical state-of-the-art solutions
From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods
Over the last five years there has been an increase in the frequency and diversity of network attacks. This holds true, as more and more organisations admit compromises on a daily basis. Many misuse and anomaly based Intrusion Detection Systems (IDSs) that rely on either signatures, supervised or statistical methods have been proposed in the literature, but their trustworthiness is debatable. Moreover, as this work uncovers, the current IDSs are based on obsolete attack classes that do not reflect the current attack trends. For these reasons, this paper provides a comprehensive overview of unsupervised and hybrid methods for intrusion detection, discussing their potential in the domain. We also present and highlight the importance of feature engineering techniques that have been proposed for intrusion detection. Furthermore, we discuss that current IDSs should evolve from simple detection to correlation and attribution. We descant how IDS data could be used to reconstruct and correlate attacks to identify attackers, with the use of advanced data analytics techniques. Finally, we argue how the present IDS attack classes can be extended to match the modern attacks and propose three new classes regarding the outgoing network communicatio
State of the Art Intrusion Detection System for Cloud Computing
The term Cloud computing is not new anymore in computing technology. This form of computing technology previously considered only as marketing term, but today Cloud computing not only provides innovative improvements in resource utilisation but it also creates a new opportunities in data protection mechanisms where the advancement of intrusion detection technologies are blooming rapidly. From the perspective of security, Cloud computing also introduces concerns about data protection and intrusion detection mechanism. This paper surveys, explores and informs researchers about the latest developed Cloud Intrusion Detection Systems by providing a comprehensive taxonomy and investigating possible solutions to detect intrusions in cloud computing systems. As a result, we provide a comprehensive review of Cloud Intrusion Detection System research, while highlighting the specific properties of Cloud Intrusion Detection System. We also present taxonomy on the key issues in Cloud Intrusion Detection System area and discuss the different approaches taken to solve the issues. We conclude the paper with a critical analysis of challenges that have not fully solved
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