Advanced technologies for homeland defense and security

In recent years significant work has been undertaken by both national and local government agencies to protect critical data and infrastructures from cyber attacks. As a result, many Homeland Security solutions have been newly developed and deployed, requiring the design of new computer systems technologies, as well as techniques allowing security procedures to be executed on many different areas, from system, communication and organization security to the security of the state to ensure its defense capabilities. Advanced security technologies based on the intelligent algorithms can analyze group behavior and interpret complex patterns or situations, and they are increasingly becoming the building blocks of the aforementioned Homeland Security solutions. In this context, we believe that it is worth noting research that combines security and defense aspects with achievements in designing advanced systems for the acquisition and sophisticated semantic analysis of complex image patterns and group behaviors. Such systems use modern cognitive models of semantic interpretation, and can be applied to develop algorithms and protocols used for the security of computer systems themselves, but also to ensure the confidentiality and security of communication networks. They can also be employed to develop globally understood safe solutions connected with activities to strengthen national defense capability. Threats to critical infrastructures, e.g. from organized crime and terrorism, endanger human life directly and indirectly. Resilience of critical infrastructures has gained importance as a core concept to cope with such threats. In general, this means strengthening those infrastructures to prevent or mitigate such threats and to consistently deliver the intended services in a trustworthy way even in changing and challenging situations. The information and communication infrastructure is the one of the primary critical infrastructures. Hence, it has been the central target from cyber attacks. As a consequence, effective response capabilities must be properly organized and closely coordinated to protect the infrastructure because, at the time of a cyber-attack, it is not possible to immediately determine whether the attacker is a script kiddie, an insider, a rogue actor (organized crime, terrorist organization, or radical), or a nation state. Considering the broad yet interesting problems from the homeland defense and security areas, we have decided to publish a Special Issue on “Advanced Technologies for Homeland Defense and Security”, which presents selected subjects concerning practical and methodological solutions from such fields as: homeland security and information processing, personal security and biometric, target recognition, sensor and data analysis in secure environment, embedded systems in internal or external security, knowledge-based Internet security, privacy and trust for Internet services, and biologically inspired security systems. This SI is a collection of 10 papers carefully selected as the best from total 35 publications submitted on these subjects. Each accepted article has been subject to a rigorous peer review procedure and has been assessed by several independent reviewers. In the paper entitled “A Simulation Study of Ad Hoc Networking of UAVs with Opportunistic Resource Utilization Networks” (Lilien et al., 2014) a specialized ad hoc network for unmanned aerial vehicles (UAVs) has been proposed. Particularly the application of Opportunistic Resource Utilization Networks (Oppnets), a novel type of MANETs, for UAV ad hoc networking was discussed. The Oppnets provide middleware to facilitate building flexible and adaptive distributed systems that provide all kinds of resources or services to the requesting application via a helper mechanism. The authors performed simulation of a homeland defense using case for Oppnets that involves detecting a suspicious watercraft. The simulation results show that Oppnets are a promising framework for high-performance ad hoc UAV networking, and provide excellent performance even under imperfect conditions. In the paper “Text Analysis for Detecting Terrorism-Related Articles on the Web” (Choi et al., 2014) the classification procedures for text messages were presented to reveal the terrorism-related web documents. The proposed method uses the word similarity, and is based on WordNet hierarchy with N-gram data frequency. Their experimental results show this technique is effective for extracting context words from the text and identifying terrorism-related documents. The paper entitled “A Botnet-based Command and Control Approach Relying on Swarm Intelligence” (Castiglione et al., 2014) was focused on a new more robust and scalable botnet-based command and control architecture, aiming at wiping off any rigid master–slave relationship and autonomizing the bot operating roles, with significant agility gains in the communication infrastructure. It relies on swarm intelligence and in particular on stigmergic communication, ensuring spontaneous, implicit coordination and collaboration among the independent bot agents. Described techniques may constitute the basis for an evolutionary malware-based control and management scheme, which can be used in several homeland security scenarios in strategic military or intelligence operations. In the paper “Cognitive Systems and Bio-inspired Computing in Homeland Security” (Ogiela and Ogiela 2014) some biological models for data protection and securing confidential information were presented. Very unique personal information and individual biometrics lead to extracting information classes necessary for personal data protection processes. Such processes can be used to secure various types of data. Adding semantic data analysis processes to computer systems, which are used to conceal processes of personal data management offers opportunities for semantically analyzing and interpreting strategic data while it is being decrypted. The paper entitled “Detecting Mobile Malware Threats to Homeland Security Through Static Analysis” (Seo et al., 2014) discussed 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66