Biblio

In this paper, we consider a stochastic model to evaluate the system availability of an intrusion tolerant system (ITS), where the system undergoes the patch management with a periodic vulnerability checking strategy, i.e., a pull-type patch management. Based on the model, this paper discusses the appropriate timing for patch applying. In particular, the paper models the attack behavior of adversary and the system behaviors under reactive defense strategies by a composite stochastic reward net (SRN). Furthermore, we formulate the interval availability by applying the phase-type (PH) approximation to solve the Markov regenerative process (MRGP) models derived from the SRNs. Numerical experiments are conducted to study the sensitivity of the system availability with respect to the number of checking.

In recent years the use of security gateways (SG) located within the electrical grid distribution network has become pervasive. SGs in substations and renewable distributed energy resource aggregators (DERAs) protect power distribution control devices from cyber and cyber-physical attacks. When encrypted communications within a DER network is used, TCP/IP packet inspection is restricted to packet header behavioral analysis which in most cases only allows the SG to perform anomaly detection of blocks of time-series data (event windows). Packet header anomaly detection calculates the probability of the presence of a threat within an event window, but fails in such cases where the unreadable encrypted payload contains the attack content. The SG system log (syslog) is a time-series record of behavioral patterns of network users and processes accessing and transferring data through the SG network interfaces. Threatening behavioral pattern in the syslog are measurable using both anomaly detection and graph theory. In this paper it will be shown that it is possible to efficiently detect the presence of and classify a potential threat within an SG syslog using light-weight anomaly detection and graph theory.

We present Diana, an embodied agent who is aware of her own virtual space and the physical space around her. Using video and depth sensors, Diana attends to the user's gestures, body language, gaze and (soon) facial expressions as well as their words. Diana also gestures and emotes in addition to speaking, and exists in a 3D virtual world that the user can see. This produces symmetric and shared perception, in the sense that Diana can see the user, the user can see Diana, and both can see the virtual world. The result is an embodied agent that begins to develop the conceit that the user is interacting with a peer rather than a program.

Conversational agents assist traditional teaching-learning instruments in proposing new designs for knowledge creation and learning analysis, across organizational environments. Means of building common educative background in both industry and academic fields become of interest for ensuring educational effectiveness and consistency. Such a context requires transferable practices and becomes the basis for the Agile adoption into Higher Education, at both curriculum and operational levels. The current work proposes a model for delivering Agile Scrum training through an assistive web-based conversational service, where analytics are collected to provide an overview on learners' knowledge path. Besides its specific applicability into Software Engineering (SE) industry, the model is to assist the academic SE curriculum. A user-acceptance test has been carried out among 200 undergraduate students and patterns of interaction have been depicted for 2 conversational strategies.

Once an individual employs the use of the Internet for accessing information; carrying out transactions and sharing of data on the Cloud, they are connected to diverse computers on the network. As such, security of such transmitted data is most threatened and then potentially creating privacy risks of users on the federated identity management system in the Cloud. Usually, User's attributes or Personal Identifiable Information (PII) are needed to access Services on the Cloud from different Service Providers (SPs). Sometime these SPs may by themselves violate user's privacy by the reuse of user's attributes offered them for the release of services to the users without their consent and then carrying out activities that may appear malicious and then causing damage to the users. Similarly, it should be noted that sensitive user's attributes (e.g. first name, email, address and the likes) are received in their original form by needed SPs in plaintext. As a result of these problems, user's privacy is being violated. Since these SPs may reuse them or connive with other SPs to expose a user's identity in the cloud environment. This research is motivated to provide a protective and novel approach that shall no longer release original user's attributes to SPs but pseudonyms that shall prevent the SPs from violating user's privacy through connivance to expose the user's identity or other means. The paper introduces a conceptual framework for the proposed user's attributes privacy protection in a federated identity management system for the cloud. On the proposed system, the use of pseudonymous technique also called Privacy Token (PT) is employed. The pseudonymous technique ensures users' original attributes values are not sent directly to the SP but auto generated pseudo attributes values. The PT is composed of: Pseudo Attribute values, Timestamp and SPİD. These composition of the PT makes it difficult for the User's PII to be revealed and further preventing the SPs from being able to keep them or reuse them in the future without the user's consent for any purpose. Another important feature of the PT is its ability to forestall collusion among several collaborating service providers. This is due to the fact that each SP receives pseudo values that have no direct link to the identity of the user. The prototype was implemented with Java programming language and its performance tested on CloudAnalyst simulation.

Given two strings: pattern p of length m and text t of length n. The string matching problem is to find all (or some) occurrences of the pattern p in the text t. We introduce a new simple data structure, called index arrays, and design fast privacy-preserving matching algorithm for string matching. The motivation behind introducing index arrays is determined by the need for pattern matching on distributed cloud-based datasets with semi-trusted cloud providers. It is intended to use encrypted index arrays both to improve performance and protect confidentiality and privacy of user data.

Cyber-Physical Systems are distributed, heterogeneous systems that communicate and exchange data over networks. This creates semantic dependencies between the individual components. In the event of an error, it is difficult to identify the source of an occurring error that is spread due to those underlying dependencies. Tools such as the Information Flow Monitor solve this problem, but require compliance with a protocol. Nodes that do not adhere to this protocol prevent errors from being tracked. In this paper, we present a way to bridge these black-box nodes with a dependency model and to still be able to use them in monitoring tools.

Identifying services constituting traffic from given IP network flows is essential to various applications, such as the management of quality of service (QoS) and the prevention of security issues. Typical methods for achieving this objective include identifications based on IP addresses and port numbers. However, such methods are not sufficiently accurate and require improvement. Deep Packet Inspection (DPI) is one of the most promising methods for improving the accuracy of identification. In addition, many current IP flows are encrypted using Transport Layer Security (TLS). Hence, it is necessary for identification methods to analyze flows encrypted by TLS. For that reason, a service identification method based on DPI and n-gram that focuses only on the non-encrypted parts in the TLS session establishment was proposed. However, there is room for improvement in identification accuracy because this method analyzes all the non-encrypted parts including Random Values without protocol analyses. In this paper, we propose a method for identifying the service from given IP flows based on analysis of Server Name Indication (SNI). The proposed method clusters flow according to the value of SNI and identify services from the occurrences of all clusters. Our evaluations, which involve identifications of services on Google and Yahoo sites, demonstrate that the proposed method can identify services more accurately than the existing method.

Distributed Denial of Service (DDoS) attack is one of the major threats to the network services. In this paper, we propose a DDoS attack detection algorithm based on the probability distributions of source IP addresses and destination IP addresses. According to the behavior of source and destination IP addresses during DDoS attack, the distance between these features is calculated and used.It is calculated with using the Greedy algorithm which eliminates some requirements associated with Kullback-Leibler divergence such as having the same rank of the probability distributions. Then frequency modulation is proposed in the detection phase to reduce false alarm rates and to avoid using static threshold. This algorithm is tested on the real data collected from Boğaziçi University network.

The core operation of all cryptosystems based on Elliptic Curve Cryptography is Elliptic Curve Point Multiplication. Depending on implementation it can be vulnerable to different Side Channel Analysis attacks exploiting information leakage, such as power consumption or execution time. Multiple countermeasures against these attacks have been developed over time, each having different impact on parameters of the cryptosystem. This paper summarizes popular countermeasures for simple and differential power analysis attacks on Elliptic Curve cryptosystems. Presented secure algorithms were implemented in Verilog hardware description language and synthesized to logic gates for power trace generation.

Modern computing devices use classical algorithms such as Rivest Shamir Adleman (RSA) and Elliptic Curve Digital Signature Algorithm (ECDSA) for their security. The securities of these algorithms relied on the problem and difficulty of integer factorization and also calculating the Discrete Logarithm Problems. With the introduction of quantum computers, recent research is focusing on developing alternative algorithms which are supposed to withstand attacks from quantum computers. One of such alternatives is the Hash-based Digital Signature Schemes. Chosen hash-based signature schemes over classical algorithms is because their security is on the hash function used and that they are metaheuristic in nature. This research work presents basic analysis and the background understanding of Stateful Hash-based Signature Schemes, particularly the Lamport One-Time Signature Scheme, Winternitz One-Time Signature Scheme, and the Merkle Signature Scheme. The three schemes selected are stateful, hence has common features and are few-time hash-based signature schemes. The selected Stateful Hash-based Digital Signature Schemes were analyzed based on their respective key generation, signature generation, signature verification, and their security levels. Practical working examples were given for better understanding. With the analyses, Merkle Signature Scheme proves to be the best candidate to be used in the Bitcoin Proof of Work protocol because of its security and its advantage of signing many messages.

Due to practical constraints in preventing phishing through public network or insecure communication channels, simple physical unclonable function (PDF)-based authentication protocol with unrestricted queries and transparent responses is vulnerable to modeling and replay attacks. In this paper, we present a PUF-based authentication method to mitigate the practical limitations in applications where a resource-rich server authenticates a device with no strong restriction imposed on the type of PUF designs or any additional protection on the binary channel used for the authentication. Our scheme uses an active deception protocol to prevent machine learning (ML) attacks on a device. The monolithic system makes collection of challenge response pairs (CRPs) easy for model building during enrollment but prohibitively time consuming upon device deployment. A genuine server can perform a mutual authentication with the device at any time with a combined fresh challenge contributed by both the server and the device. The message exchanged in clear does not expose the authentic CRPs. The false PUF multiplexing is fortified against prediction of waiting time by doubling the time penalty for every unsuccessful authentication.

The problem of fast items retrieval from a fixed collection is often encountered in most computer science areas, from operating system components to databases and user interfaces. We present an approach based on hash tables that focuses on both minimizing the number of comparisons performed during the search and minimizing the total collection size. The standard open-addressing double-hashing approach is improved with a non-linear transformation that can be parametrized in order to ensure a uniform distribution of the data in the hash table. The optimal parameter is determined using a genetic algorithm. The paper results show that near-perfect hashing is faster than binary search, yet uses less memory than perfect hashing, being a good choice for memory-constrained applications where search time is also critical.

Today, network security is a world hot topic in computer security and defense. Intrusions and attacks in network infrastructures lead mostly in huge financial losses, massive sensitive data leaks, thus decreasing efficiency, competitiveness and the quality of productivity of an organization. Network Intrusion Detection System (NIDS) is valuable tool for the defense-in-depth of computer networks. It is widely deployed in network architectures in order to monitor, to detect and eventually respond to any anomalous behavior and misuse which can threat confidentiality, integrity and availability of network resources and services. Thus, the presence of NIDS in an organization plays a vital part in attack mitigation, and it has become an integral part of a secure organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on improved Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). GA is improved through an optimization strategy, namely Fitness Value Hashing (FVH), which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-GASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. In addition, improvement of GA through FVH has saved processing power and execution time. Thereby, our proposed IDS is very much suitable for network anomaly detection.

Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a three-dimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system.

We consider a setup in which the channel from Alice to Bob is less noisy than the channel from Eve to Bob. We show that there exist encoding and decoding which accomplish error correction and authentication simultaneously; that is, Bob is able to correctly decode a message coming from Alice and reject a message coming from Eve with high probability. The system does not require any secret key shared between Alice and Bob, provides information theoretic security, and can safely be composed with other protocols in an arbitrary context.

With the emergence of the Internet of Things (IoT) and the increasing number of resource-constrained interconnected smart devices, there is a noticeable increase in the number of cyber security crimes. In the face of the possible attacks on IoT networks such as network intrusion, denial of service, spoofing and so on, there is a need to develop efficient methods to locate vulnerabilities and mitigate attacks in IoT networks. Without loss of generality, we consider only intrusion-related threats to IoT. A honeypot is a system used to understand the potential dynamic threats and act as a proactive measure to detect any intrusion into the network. It is used as a trap for intruders to control unauthorized access to the network by analyzing malicious traffic. However, a sophisticated attacker can detect the presence of a honeypot and abort the intrusion mission. Therefore it is essential for honeypots to be undetectable. In this paper, we study and analyze possible techniques for SSH and telnet honeypot detection. Moreover, we propose a new methodology for probabilistic estimation of honeypot detection and an automated software implemented this methodology.

Named Data Network (NDN) is an alternative to host-centric networking exemplified by today's Internet. One key feature of NDN is in-network caching that reduces access delay and query overhead by caching popular contents at the source as well as at a few other nodes. Unfortunately, in-network caching suffers various privacy risks by different attacks, one of which is termed timing attack. This is an attack to infer whether a consumer has recently requested certain contents based on the time difference between the delivery time of those contents that are currently cached and those that are not cached. In order to prevent the privacy leakage and resist such kind of attacks, we propose a detection scheme by adopting Long Short-term Memory (LSTM) model. Based on the four input features of LSTM, cache hit ratio, average request interval, request frequency, and types of requested contents, we timely capture more important eigenvalues by dividing a constant time window size into a few small slices in order to detect timing attacks accurately. We have performed extensive simulations to compare our scheme with several other state-of-the-art schemes in classification accuracy, detection ratio, false alarm ratio, and F-measure. It has been shown that our scheme possesses a better performance in all cases studied.

Vehicular Named Data Network (VNDN) uses NDN as an underlying communication paradigm to realize intelligent transportation system applications. Content communication is the essence of NDN, which is primarily carried out through content naming, forwarding, intrinsic content security, and most importantly the in-network caching. In vehicular networks, vehicles on the road communicate with other vehicles and/or infrastructure network elements to provide passengers a reliable, efficient, and infotainment-rich commute experience. Recently, different aspects of NDN have been investigated in vehicular networks and in vehicular social networks (VSN); however, in this paper, we investigate the in-network caching, realized in NDN through the content store (CS) data structure. As the stale contents in CS do not just occupy cache space, but also decrease the overall performance of NDN-driven VANET and VSN applications, therefore the size of CS and the content lifetime in CS are primary issues in VNDN communications. To solve these issues, we propose a simple yet efficient multi-metric CS management mechanism through cache replacement (M2CRP). We consider the content popularity, relevance, freshness, and distance of a node to devise a set of algorithms for selection of the content to be replaced in CS in the case of replacement requirement. Simulation results show that our multi-metric strategy outperforms the existing cache replacement mechanisms in terms of Hit Ratio.

Cloud computing has become an important and popular infrastructure for data storage and sharing. Typically, data owners outsource their massive data to a public cloud that will provide search services to authorized data users. With privacy concerns, the valuable outsourced data cannot be exposed directly, and should be encrypted before outsourcing to the public cloud. In this paper, we focus on k-Nearest Neighbor (k-NN) search over encrypted data. We propose efficient and secure k-NN search schemes based on matrix similarity to achieve efficient and secure query services in public cloud. In our basic scheme, we construct the traces of two diagonal multiplication matrices to denote the Euclidean distance of two data points, and perform secure k-NN search by comparing traces of corresponding similar matrices. In our enhanced scheme, we strengthen the security property by decomposing matrices based on our basic scheme. Security analysis shows that our schemes protect the data privacy and query privacy under attacking with different levels of background knowledge. Experimental evaluations show that both schemes are efficient in terms of computation complexity as well as computational cost.

This article presents results and overview of conducted testing of active optical network devices. The base for the testing is originating in Kali Linux and penetration testing generally. The goal of tests is to either confirm or disprove a vulnerability of devices used in the tested polygon. The first part deals with general overview and topology of testing devices, the next part is dedicated to active and passive exploration and exploits. The last part provides a summary of the results.

To gain strategic insight into defending against the network reconnaissance stage of advanced persistent threats, we recreate the escalating competition between scans and deceptive views on a Software Defined Network (SDN). Our threat model presumes the defense is a deceptive network view unique for each node on the network. It can be configured in terms of the number of honeypots and subnets, as well as how real nodes are distributed across the subnets. It assumes attacks are NMAP ping scans that can be configured in terms of how many IP addresses are scanned and how they are visited. Higher performing defenses detect the scanner quicker while leaking as little information as possible while higher performing attacks are better at evading detection and discovering real nodes. By using Artificial Intelligence in the form of a competitive coevolutionary genetic algorithm, we can analyze the configurations of high performing static defenses and attacks versus their evolving adversary as well as the optimized configuration of the adversary itself. When attacks and defenses both evolve, we can observe that the extent of evolution influences the best configurations.

The objective of this paper is to propose a distributed intrusion detection model based on a multi agent system. Mutli Agent Systems (MAS) are very suitable for intrusion detection systems as they meet the characteristics required by the networks and Big Data issues. The MAS agents cooperate and communicate with each other to ensure the effective detection of network intrusions without the intervention of an expert as used to be in the classical intrusion detection systems relying on signature matching to detect known attacks. The proposed model helped to detect known and unknown attacks within big computer infrastructure by responding to the network requirements in terms of distribution, autonomy, responsiveness and communication. The proposed model is capable of achieving a good and a real time intrusion detection using multi-agents paradigm and Hadoop Distributed File System (HDFS).

This paper describes an approach to detecting malicious code introduced by insiders, which can compromise the data integrity in a program. The approach identifies security spots in a program, which are either malicious code or benign code. Malicious code is detected by reviewing each security spot to determine whether it is malicious or benign. The integrity breach conditions (IBCs) for object-oriented programs are specified to identify security spots in the programs. The IBCs are specified by means of the concepts of coupling within an object or between objects. A prototype tool is developed to validate the approach with a case study.

Wide bandgap power semiconductors are key enablers for increasing the power density of switch-mode power supplies. However, they require new gate drive technologies. This paper examines and characterizes a fabricated gate-driver in a class-E resonant inverter. The gate-driver's total area of 1.2mm2 includes two high-voltage transistors for gate-driving, integrated complementary metal-oxide-semiconductor (CMOS) gate-drivers, high-speed floating level-shifter and reset circuitry. A prototype printed circuit board (PCB) was designed to assess the implications of an electrostatic discharge (ESD) diode, its parasitic capacitance and package bondwire connections. The parasitic capacitance was estimated using its discharge time from an initial voltage and the capacitance is 56.7 pF. Both bondwires and the diode's parasitic capacitance is neglegible. The gate-driver's functional behaviour is validated using a parallel LC resonant tank resembling a self-oscillating gate-drive. Measurements and simulations show the ESD diode clamps the output voltage to a minimum of -2V.