A taxonomy of attacks and a survey of defence mechanisms for semantic social engineering attacks

Social engineering is used as an umbrella term for a broad spectrum of computer exploitations that employ a variety of attack vectors and strategies to psychologically manipulate a user. Semantic attacks are the specific type of social engineering attacks that bypass technical defences by actively manipulating object characteristics, such as platform or system applications, to deceive rather than directly attack the user. Commonly observed examples include obfuscated URLs, phishing emails, drive-by downloads, spoofed web- sites and scareware to name a few. This paper presents a taxonomy of semantic attacks, as well as a survey of applicable defences. By contrasting the threat landscape and the associated mitigation techniques in a single comparative matrix, we identify the areas where further research can be particularly beneficial

Cyber and physical threats to the internet of everything

After over 40 years of the Internet faithfully serving the needs of the Earth’s human population for information, communication, and entertainment, we have now entered the era of the IoT. Of course, when we refer to the Internet, we also mean the Web and therefore the Web of Things (WoT), where distributed applications benefitting from networking through the Internet are no longer a privilege of humans. Things can also take full advantage of the capabilities, simplicity, and potential of Web technologies and protocols. Following current developments in this field, it is not difficult to see the inevitability of the convergence of the two worlds, of humans and of things, each using the Internet as a primary means of communication. Possibly the most appropriate term to describe this evolution has been proposed by Cisco: the Internet of Everything (IoE), which "brings together people, process, data, and things to make networked connections more relevant and valuable than ever before." In the IoE era, machines are equal to humans as Internet users. In an ecosystem in which everything is connected, and where physical and cyber converge and collaborate, the threats of the two worlds not only coexist, but also converge, creating a still largely unknown environment, in which an attack in cyberspace can propagate and have an adverse effect in physical space and vice versa. So how can we be prepared for and confront this new unknown? How can we study and learn from the ways this has been dealt with in the past? First, it is important to simplify the problem by attempting to identify the components of IoE and the threats and effects an attack can have in each one

A denial of service detector based on maximum likelihood detection and the random neural network

In spite of extensive research in defence against De- nial of Service (DoS), such attacks remain a predom- inant threat in today’s networks. Due to the sim- plicity of the concept and the availability of the rele- vant attack tools, launching a DoS attack is relatively easy, while defending a network resource against it is disproportionately difficult. The first step of any comprehensive protection scheme against DoS is the detection of its existence, ideally long before the de- structive traffic build-up. In this paper we propose a generic approach for DoS detection which uses multi- ple Bayesian classifiers and random neural networks (RNN). Our method is based on measuring various instantaneous and statistical variables describing the incoming network traffic, acquiring a likelihood esti- mation and fusing the information gathered from the individual input features using likelihood averaging and different architectures of RNNs. We present and compare seven different implementations of it and evaluate our experimental results obtained in a large networking testbed

Predicting the performance of users as human sensors of security threats in social media

While the human as a sensor concept has been utilised extensively for the detection of threats to safety and security in physical space, especially in emergency response and crime reporting, the concept is largely unexplored in the area of cyber security. Here, we evaluate the potential of utilising users as human sensors for the detection of cyber threats, specifically on social media. For this, we have conducted an online test and accompanying questionnaire-based survey, which was taken by 4,457 users. The test included eight realistic social media scenarios (four attack and four non-attack) in the form of screenshots, which the participants were asked to categorise as “likely attack” or “likely not attack”. We present the overall performance of human sensors in our experiment for each exhibit, and also apply logistic regression and Random Forest classifiers to evaluate the feasibility of predicting that performance based on different characteristics of the participants. Such prediction would be useful where accuracy of human sensors in detecting and reporting social media security threats is important. We identify features that are good predictors of a human sensor’s performance and evaluate them in both a theoretical ideal case and two more realistic cases, the latter corresponding to limited access to a user’s characteristics

Security in the internet of everything era - opening statement

Since Nikola Tesla’s “teleautomation”, it has taken almost 80 years for the general public to experience what culminated into the Internet of Things and another ten to truly accept it. The problem is that in recent years, a vast range of devices and systems were designed to support this new paradigm, but with little regard to security or privacy, despite the profound impact that breaches of either can have to a user’s “real life”

A biologically inspired denial of service detector using the random neural network

Several of today’s computing challenges have been met by resorting to and adapting optimal solutions that have evolved in nature. For example, autonomic communication net- works have started applying biologically-inspired methods to achieve some of their self-* properties. We build upon such methods to solve the recent problem of detection of Denial of Service networking attacks, by proposing a combination of Bayesian decision making and the Random Neural Networks (RNN) which are inspired by the random spiking behaviour of the biological neurons. Our approach is based on measuring various instantaneous and statistical variables describing the incoming network traffic, acquiring a likelihood estimation and fusing the information gathered from the individual input features using different architectures of the RNN. The experiments are conducted using the CPN networking protocol which is also based on the RNN

Likelihood ratios and recurrent random neural networks in detection of denial of service attacks

In a world that is becoming increasingly dependent on In- ternet communication, Denial of Service (DoS) attacks have evolved into a major security threat which is easy to launch but difficult to defend against. In order for DoS countermea- sures to be effective, the attack must be detected early and accurately. In this paper we propose a DoS detection tech- nique based on observation of the incoming traffic and a com- bination of traditional likelihood estimation with a recurrent random neural network (r-RNN) structure. We select input features that describe essential information on the incoming traffic and evaluate the likelihood ratios for each input, to fuse them with a r-RNN. We evaluate the performance of our method in terms of false alarm and correct detection rates with experiments on a large networking testbed, for a variety of input traffic

Wear it and share it: Wearables and security

As the amount of data generated by personal devices increases, supported by the trend of making these devices more personal (i.e., wearable, sewable), so too will the risks of personal privacy violation rise. From the technological perspective, it is important to follow privacy-by-design approaches, incorporating both data encryption and data anonymization techniques. From the perspective of enterprises and users, understanding that “wearing means sharing” is a valuable first step