No abstract

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 websites 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.

Semantic social engineering attacks are a pervasive threat to computer and communication systems. By employing deception rather than by exploiting technical vulnerabilities, spear-phishing, obfuscated URLs, drive-by downloads, spoofed websites, scareware, and other attacks are able to circumvent traditional technical security controls and target the user directly. Our aim is to explore the feasibility of predicting user susceptibility to deception-based attacks through attributes that can be measured, preferably in real-time and in an automated manner. Toward this goal, we have conducted two experiments, the first on 4333 users recruited on the Internet, allowing us to identify useful high-level features through association rule mining, and the second on a smaller group of 315 users, allowing us to study these features in more detail. In both experiments, participants were presented with attack and non-attack exhibits and were tested in terms of their ability to distinguish between the two. Using the data collected, we have determined practical predictors of users' susceptibility against semantic attacks to produce and evaluate a logistic regression and a random forest prediction model, with the accuracy rates of .68 and .71, respectively. We have observed that security training makes a noticeable difference in a user's ability to detect deception attempts, with one of the most important features being the time since last self-study, while formal security education through lectures appears to be much less useful as a predictor. Other important features were computer literacy, familiarity, and frequency of access to a specific platform. Depending on an organisation's preferences, the models learned can be configured to minimise false positives or false negatives or maximise accuracy, based on a probability threshold. For both models, a threshold choice of 0.55 would keep both false positives and false negatives below 0.2.INDEX TERMS Security, cyber crime, social engineering, semantic attacks. 6910 2169-3536

In the past, home automation was a small market for technology enthusiasts. Interconnectivity between devices was down to the owner's technical skills and creativity, while security was non-existent or primitive, because cyber threats were also largely non-existent or primitive. This is not the case any more. The adoption of Internet of Things technologies, cloud computing, artificial intelligence and an increasingly wide range of sensing and actuation capabilities has led to smart homes that are more practical, but also genuinely attractive targets for cyber attacks. Here, we classify applicable cyber threats according to a novel taxonomy, focusing not only on the attack vectors that can be used, but also the potential impact on the systems and ultimately on the occupants and their domestic life. Utilising the taxonomy, we classify twenty five different smart home attacks, providing further examples of legitimate, yet vulnerable smart home configurations which can lead to second-order attack vectors. We then review existing smart home defence mechanisms and discuss open research problems. Reference Key security properties Vulnerabilities/challenges Security recommended Open problems identified Komninos et al. [1] Confidentiality Connected to Internet Auto-immunity to threats Resilience Physical tampering Reliability, availability Lin et al. [2] Confidentiality Phys./netw. accessibility Gateway architecture Auto-configuration Authentication Constrained resources Updates Access control Heterogeneity Nawir et al. [6] Smart meter integrity Remote connectivity Techn. countermeasures Standardisation Privacy Physical tampering Regulatory initiatives Impact evaluation, metrics Non-repudiation Malicious actuation Intrusion detection Authorisation Logging for audit/forensics Ziegeldorf et al.[5]

The notion that the human user is the weakest link in information security has been strongly, and, we argue, rightly contested in recent years. Here, we take a step further showing that the human user can in fact be the strongest link for detecting attacks that involve deception, such as application masquerading, spearphishing, WiFi evil twin and other types of semantic social engineering. Towards this direction, we have developed a human-as-a-security-sensor framework and a practical implementation in the form of Cogni-Sense, a Microsoft Windows prototype application, designed to allow and encourage users to actively detect and report semantic social engineering attacks against them. Experimental evaluation with 26 users of different profiles running Cogni-Sense on their personal computers for a period of 45 days has shown that human sensors can consistently outperform technical security systems. Making use of a machine learning based approach, we also show that the reliability of each report, and consequently the performance of each human sensor, can be predicted in a meaningful and practical manner. In an organisation that employs a human-as-a-security-sensor implementation, such as Cogni-Sense, an attack is considered to have been detected if at least one user has reported it. In our evaluation, a small organisation consisting only of the 26 participants of the experiment would have exhibited a missed detection rate below 10%, down from 81% if only technical security systems had been used. The results strongly point towards the need to actively involve the user not only in prevention through cyber hygiene and user-centric security design, but also in active cyber threat detection and reporting.