Biblio

Cyber attacks, (e.g., DDoS), on computers connected to the Internet occur everyday. A DDoS attack in 2016 that used “Mirai botnet” generated over 600 Gbit/s traffic, which was twice as that of last year. In view of this situation, we can no longer adequately protect our computers using current end-point security solutions and must therefore introduce a new method of protection that uses distributed nodes, e.g., routers. We propose an Autonomous and Distributed Internet Security (AIS) infrastructure that provides two key functions: first, filtering source address spoofing packets (proactive filter), and second, filtering malicious packets that are observed at the end point (reactive filter) at the closest malicious packets origins. We also propose three types of Multi-Layer Binding Routers (MLBRs) to realize these functions. We implemented the MLBRs and constructed experimental systems to simulate DDoS attacks. Results showed that all malicious packets could be filtered by using the AIS infrastructure.

Emerging nonvolatile memory (NVM) devices are not limited to build nonvolatile memory macros. They can also be used in developing nonvolatile logics (nvLogics) for nonvolatile processors, security circuits for the internet of things (IoT), and computing-in-memory (CIM) for artificial intelligence (AI) chips. This paper explores the challenges in circuit designs of emerging memory devices for application in nonvolatile logics, security circuits, and CIM for deep neural networks (DNN). Several silicon-verified examples of these circuits are reviewed in this paper.

Genetic Algorithms are group of mathematical models in computational science by exciting evolution in AI techniques nowadays. These algorithms preserve critical information by applying data structure with simple chromosome recombination operators by encoding solution to a specific problem. Genetic algorithms they are optimizer, in which range of problems applied to it are quite broad. Genetic Algorithms with its global search includes basic principles like selection, crossover and mutation. Data structures, algorithms and human brain inspiration are found for classification of data and for learning which works using Neural Networks. Artificial Intelligence (AI) it is a field, where so many tasks performed naturally by a human. When AI conventional methods are used in a computer it was proved as a complicated task. Applying Neural Networks techniques will create an internal structure of rules by which a program can learn by examples, to classify different inputs than mining techniques. This paper proposes a phishing websites classifier using improved polynomial neural networks in genetic algorithm.

Never Alone (2016) is a generative large-scale urban screen video-sound installation, which presents the idea of generative choreographies amongst multiple video agents, or "digital performers". This generative installation questions how we navigate in urban spaces and the ubiquity and disruptive nature of encounters within the cities' landscapes. The video agents explore precarious movement paths along the façade inhabiting landscapes that are both architectural and emotional.

The Mobile Ad-hoc Networks (MANET) are suffering from network partitioning when there is group mobility and thus cannot efficiently provide connectivity to all nodes in the network. Autonomous Mobile Mesh Network (AMMNET) is a new class of MANET which will overcome the weakness of MANET, especially from network partitioning. However, AMMNET is vulnerable to routing attacks such as Blackhole attack in which malicious node can make itself as intragroup, intergroup or intergroup bridge router and disrupt the network. In AMMNET, To maintain connectivity, network survivability is an important aspect of reliable communication. Maintaning security is a challenge in the self organising nature of the topology. To address this weakness proposed approach measured the performance of the impact of security enhancement on AMMNET with the basis of bait detection scheme. Modified bait approach that will prevent blackhole node entering into the network and helps to maintain the reliability of the network. The proposed scheme uses the idea of Wumpus World concept from Artificial Intelligence. Modified bait scheme will prevent the blackhole attack and secures network.

Certain crimes are difficult to be committed by individuals but carefully organised by group of associates and affiliates loosely connected to each other with a single or small group of individuals coordinating the overall actions. A common starting point in understanding the structural organisation of criminal groups is to identify the criminals and their associates. Situations arise in many criminal datasets where there is no direct connection among the criminals. In this paper, we investigate ties and community structure in crime data in order to understand the operations of both traditional and cyber criminals, as well as to predict the existence of organised criminal networks. Our contributions are twofold: we propose a bipartite network model for inferring hidden ties between actors who initiated an illegal interaction and objects affected by the interaction, we then validate the method in two case studies on pharmaceutical crime and underground forum data using standard network algorithms for structural and community analysis. The vertex level metrics and community analysis results obtained indicate the significance of our work in understanding the operations and structure of organised criminal networks which were not immediately obvious in the data. Identifying these groups and mapping their relationship to one another is essential in making more effective disruption strategies in the future.

Automatic detection of TV advertisements is of paramount importance for various media monitoring agencies. Existing works in this domain have mostly focused on news channels using news specific features. Most commercial products use near copy detection algorithms instead of generic advertisement classification. A generic detector needs to handle inter-class and intra-class imbalances present in data due to variability in content aired across channels and frequent repetition of advertisements. Imbalances present in data make classifiers biased towards one of the classes and thus require special treatment. We propose to use tree of perceptrons to solve this problem. The training data available for each perceptron node is balanced using cluster based over-sampling and TOMEK link cleaning as we traverse the tree downwards. The trained perceptron node then passes the original unbalanced data to its children. This process is repeated recursively till we reach the leaf nodes. We call this new algorithm as "Progressively Balanced Perceptron Tree". We have also contributed a TV advertisements dataset consisting of 250 hours of videos recorded from five non-news TV channels of different genres. Experimentations on this dataset have shown that the proposed approach has comparatively superior and balanced performance with respect to six baseline methods. Our proposal generalizes well across channels, with varying training data sizes and achieved a top F1-score of 97% in detecting advertisements.

Pervasive Computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work we review these general issues and propose a Pervasive Computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some Artificial Intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones.

Security in mobile handsets of telecommunication standards such as GSM, Project 25 and TETRA is very important, especially when governments and military forces use handsets and telecommunication devices. Although telecommunication could be quite secure by using encryption, coding, tunneling and exclusive channel, attackers create new ways to bypass them without the knowledge of the legitimate user. In this paper we introduce a new, simple and economical circuit to warn the user in cases where the message is not encrypted because of manipulation by attackers or accidental damage. This circuit not only consumes very low power but also is created to sustain telecommunication devices in aspect of security and using friendly. Warning to user causes the best practices of telecommunication devices without wasting time and energy for fault detection.

The recent trend of mobile ad hoc network increases the ability and impregnability of communication between the mobile nodes. Mobile ad Hoc networks are completely free from pre-existing infrastructure or authentication point so that all the present mobile nodes which are want to communicate with each other immediately form the topology and initiates the request for data packets to send or receive. For the security perspective, communication between mobile nodes via wireless links make these networks more susceptible to internal or external attacks because any one can join and move the network at any time. In general, Packet dropping attack through the malicious node (s) is one of the possible attack in the mobile ad hoc network. This paper emphasized to develop an intrusion detection system using fuzzy Logic to detect the packet dropping attack from the mobile ad hoc networks and also remove the malicious nodes in order to save the resources of mobile nodes. For the implementation point of view Qualnet simulator 6.1 and Mamdani fuzzy inference system are used to analyze the results. Simulation results show that our system is more capable to detect the dropping attacks with high positive rate and low false positive.

A small battery driven bio-patch, attached to the human body and monitoring various vital signals such as temperature, humidity, heart activity, muscle and brain activity, is an example of a highly resource constrained system, that has the demanding task to assess correctly the state of the monitored subject (healthy, normal, weak, ill, improving, worsening, etc.), and its own capabilities (attached to subject, working sensors, sufficient energy supply, etc.). These systems and many other systems would benefit from a sense of itself and its environment to improve robustness and sensibility of its behavior. Although we can get inspiration from fields like neuroscience, robotics, AI, and control theory, the tight resource and energy constraints imply that we have to understand accurately what technique leads to a particular feature of awareness, how it contributes to improved behavior, and how it can be implemented cost-efficiently in hardware or software. We review the concepts of environment- and self-models, semantic interpretation, semantic attribution, history, goals and expectations, prediction, and self-inspection, how they contribute to awareness and self-awareness, and how they contribute to improved robustness and sensibility of behavior.

Today's more reliable communication technology, together with the availability of higher computational power, have paved the way for introduction of more advanced automation systems based on distributed intelligence and multi-agent technology. However, abundance of data, while making these systems more powerful, can at the same time act as their biggest vulnerability. In a web of interconnected devices and components functioning within an automation framework, potential impact of malfunction in a single device, either through internal failure or external damage/intrusion, may lead to detrimental side-effects spread across the whole underlying system. The potentially large number of devices, along with their inherent interrelations and interdependencies, may hinder the ability of human operators to interpret events, identify their scope of impact and take remedial actions if necessary. Through utilization of the concepts of graph-theoretic fuzzy cognitive maps (FCM) and expert systems, this paper puts forth a solution that is able to reveal weak links and vulnerabilities of an automation system, should it become exposed to partial internal failure or external damage. A case study has been performed on the IEEE 34-bus test distribution system to show the efficiency of the proposed scheme.

Computing systems and networks become increasingly large and complex with a variety of compromises and vulnerabilities. The network security and privacy are of great concern today, where self-defense against different kinds of attacks in an autonomous and holistic manner is a challenging topic. To address this problem, we developed an innovative technology called Bionic Autonomic Nervous System (BANS). The BANS is analogous to biological nervous system, which consists of basic modules like cyber axon, cyber neuron, peripheral nerve and central nerve. We also presented an innovative self-defense mechanism which utilizes the Fuzzy Logic, Neural Networks, and Entropy Awareness, etc. Equipped with the BANS, computer and network systems can intelligently self-defend against both known and unknown compromises/attacks including denial of services (DoS), spyware, malware, and virus. BANS also enabled multiple computers to collaboratively fight against some distributed intelligent attacks like DDoS. We have implemented the BANS in practice. Some case studies and experimental results exhibited the effectiveness and efficiency of the BANS and the self-defense mechanism.