588 research outputs found
IPhone Securtity Analysis
The release of Apple’s iPhone was one of the most intensively publicized product releases in the history of mobile devices. While the iPhone wowed users with its exciting design and features, it also outraged many for not allowing installation of third party applications and for working exclusively with AT&T wireless services for the first two years. Software attacks have been developed to get around both limitations. The development of those attacks and further evaluation revealed several vulnerabilities in iPhone security. In this paper, we examine several of the attacks developed for the iPhone as a way of investigating the iPhone’s security structure. We also analyze the security holes that have been discovered and make suggestions for improving iPhone security
Case Study On Social Engineering Techniques for Persuasion
There are plenty of security software in market; each claiming the best,
still we daily face problem of viruses and other malicious activities. If we
know the basic working principal of such malware then we can very easily
prevent most of them even without security software. Hackers and crackers are
experts in psychology to manipulate people into giving them access or the
information necessary to get access. This paper discusses the inner working of
such attacks. Case study of Spyware is provided. In this case study, we got
100% success using social engineering techniques for deception on Linux
operating system, which is considered as the most secure operating system. Few
basic principal of defend, for the individual as well as for the organization,
are discussed here, which will prevent most of such attack if followed.Comment: 7 Page
Computer Viruses, Attacks, and Security Methods
with the fast growth of the Internet, computer threats and viruses have become a very serious issue for us, which attract public attention. Therefore, the distribution of computer viruses and worms were discussed in this study. This paper focuses on the effects of computer viruses. The main area of this paperis a brief discussion on computer viruses and security or detection methods. This study is very useful and helpful for computer users to use the different methods, possible steps to protect their system and information from any kind of possible attacks on their system
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
A Threat to Cyber Resilience : A Malware Rebirthing Botnet
This paper presents a threat to cyber resilience in the form of a conceptual model of a malware rebirthing botnet which can be used in a variety of scenarios. It can be used to collect existing malware and rebirth it with new functionality and signatures that will avoid detection by AV software and hinder analysis. The botnet can then use the customized malware to target an organization with an orchestrated attack from the member machines in the botnet for a variety of malicious purposes, including information warfare applications. Alternatively, it can also be used to inject known malware signatures into otherwise non malicious code and traffic to overloading the sensors and processing systems employed by intrusion detection and prevention systems to create a denial of confidence of the sensors and detection systems. This could be used as a force multiplier in asymmetric warfare applications to create confusion and distraction whilst attacks are made on other defensive fronts
Flooding attacks to internet threat monitors (ITM): Modeling and counter measures using botnet and honeypots
The Internet Threat Monitoring (ITM),is a globally scoped Internet monitoring
system whose goal is to measure, detect, characterize, and track threats such
as distribute denial of service(DDoS) attacks and worms. To block the
monitoring system in the internet the attackers are targeted the ITM system. In
this paper we address flooding attack against ITM system in which the attacker
attempt to exhaust the network and ITM's resources, such as network bandwidth,
computing power, or operating system data structures by sending the malicious
traffic. We propose an information-theoretic frame work that models the
flooding attacks using Botnet on ITM. Based on this model we generalize the
flooding attacks and propose an effective attack detection using Honeypots
Malware Detection Using Dynamic Analysis
In this research, we explore the field of dynamic analysis which has shown promis- ing results in the field of malware detection. Here, we extract dynamic software birth- marks during malware execution and apply machine learning based detection tech- niques to the resulting feature set. Specifically, we consider Hidden Markov Models and Profile Hidden Markov Models. To determine the effectiveness of this dynamic analysis approach, we compare our detection results to the results obtained by using static analysis. We show that in some cases, significantly stronger results can be obtained using our dynamic approach
MalFox: Camouflaged Adversarial Malware Example Generation Based on Conv-GANs Against Black-Box Detectors
Deep learning is a thriving field currently stuffed with many practical
applications and active research topics. It allows computers to learn from
experience and to understand the world in terms of a hierarchy of concepts,
with each being defined through its relations to simpler concepts. Relying on
the strong capabilities of deep learning, we propose a convolutional generative
adversarial network-based (Conv-GAN) framework titled MalFox, targeting
adversarial malware example generation against third-party black-box malware
detectors. Motivated by the rival game between malware authors and malware
detectors, MalFox adopts a confrontational approach to produce perturbation
paths, with each formed by up to three methods (namely Obfusmal, Stealmal, and
Hollowmal) to generate adversarial malware examples. To demonstrate the
effectiveness of MalFox, we collect a large dataset consisting of both malware
and benignware programs, and investigate the performance of MalFox in terms of
accuracy, detection rate, and evasive rate of the generated adversarial malware
examples. Our evaluation indicates that the accuracy can be as high as 99.0%
which significantly outperforms the other 12 well-known learning models.
Furthermore, the detection rate is dramatically decreased by 56.8% on average,
and the average evasive rate is noticeably improved by up to 56.2%
- …