Biblio

With the rapid increase in the use of mobile devices in people's daily lives, mobile data traffic is exploding in recent years. In the edge computing environment where edge servers are deployed around mobile users, caching popular data on edge servers can ensure mobile users' fast access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data cache problem with a focus on the reduction of network delay and the improvement of mobile devices' energy efficiency. In this paper, we attack the data caching problem in the edge computing environment from the service providers' perspective, who would like to maximize their venues of caching their data. This problem is complicated because data caching produces benefits at a cost and there usually is a trade-off in-between. In this paper, we formulate the data caching problem as an integer programming problem, and maximizes the revenue of the service provider while satisfying a constraint for data access latency. Extensive experiments are conducted on a real-world dataset that contains the locations of edge servers and mobile users, and the results reveal that our approach significantly outperform the baseline approaches.

The realization principle of zero-time self-healing network communication technology is introduced. According to the characteristics of ship monitoring, an integrated ship monitoring network is designed, which integrates the information of ship monitoring equipment. By setting up a network performance test environment, the information delay of self-healing network switch is tested, and the technical characteristics of "no packet loss" are verified. Zero-time self-healing network communication technology is an innovative technology in the design of ship monitoring network. It will greatly reduce the laying of network cables, reduce the workload of information upgrade and transformation of ships, and has the characteristics of continuous maintenance of the network. It has a wide application prospect.

This paper begins with an introduction to security metrics, describing the need for security metrics, followed by a discussion of the nature of security metrics, including the challenges found with some security metrics used in the past. The paper then discusses what makes a good security metric and proposes a rigorous step-by-step method that can be applied to design good security metrics, and to test existing security metrics to see if they are good metrics. Application examples are included to illustrate the method.

Security patches in open source software (OSS) not only provide security fixes to identified vulnerabilities, but also make the vulnerable code public to the attackers. Therefore, armored attackers may misuse this information to launch N-day attacks on unpatched OSS versions. The best practice for preventing this type of N-day attacks is to keep upgrading the software to the latest version in no time. However, due to the concerns on reputation and easy software development management, software vendors may choose to secretly patch their vulnerabilities in a new version without reporting them to CVE or even providing any explicit description in their change logs. When those secretly patched vulnerabilities are being identified by armored attackers, they can be turned into powerful "0-day" attacks, which can be exploited to compromise not only unpatched version of the same software, but also similar types of OSS (e.g., SSL libraries) that may contain the same vulnerability due to code clone or similar design/implementation logic. Therefore, it is critical to identify secret security patches and downgrade the risk of those "0-day" attacks to at least "n-day" attacks. In this paper, we develop a defense system and implement a toolset to automatically identify secret security patches in open source software. To distinguish security patches from other patches, we first build a security patch database that contains more than 4700 security patches mapping to the records in CVE list. Next, we identify a set of features to help distinguish security patches from non-security ones using machine learning approaches. Finally, we use code clone identification mechanisms to discover similar patches or vulnerabilities in similar types of OSS. The experimental results show our approach can achieve good detection performance. A case study on OpenSSL, LibreSSL, and BoringSSL discovers 12 secret security patches.

Android operating systems have become a prime target for cyber attackers due to security vulnerabilities in the underlying operating system and application design. Recently, anomaly detection techniques are widely studied for security vulnerabilities detection and classification. However, the ability of the attackers to create new variants of existing malware using various masking techniques makes it harder to deploy these techniques effectively. In this research, we present a robust and effective vulnerabilities detection approach based on anomaly detection in a system calls of benign and malicious Android application. The anomaly in our study is type, frequency, and sequence of system calls that represent a vulnerability. Our system monitors the processes of benign and malicious application and detects security vulnerabilities based on the combination of parameters and metrics, i.e., type, frequency and sequence of system calls to classify the process behavior as benign or malign. The detection algorithm detects the anomaly based on the defined scoring function f and threshold ρ. The system refines the detection process by applying machine learning techniques to find a combination of system call metrics and explore the relationship between security bugs and the pattern of system calls detected. The experiment results show the detection rate of the proposed algorithm based on precision, recall, and f-score for different machine learning algorithms.

The timely exchange of information on new threats and vulnerabilities has become a cornerstone of effective cyber defence in recent years. Especially national authorities increasingly assume their role as information brokers through national cyber security centres and distribute warnings on new attack vectors and vital recommendations on how to mitigate them. Although many of these initiatives are effective to some degree, they also suffer from severe limitations. Many steps in the exchange process require extensive human involvement to manually review, vet, enrich, analyse and distribute security information. Some countries have therefore started to adopt distributed cyber security sensor networks to enable the automatic collection, analysis and preparation of security data and thus effectively overcome limiting scalability factors. The basic idea of IoC-centric cyber security sensor networks is that the national authorities distribute Indicators of Compromise (IoCs) to organizations and receive sightings in return. This effectively helps them to estimate the spreading of malware, anticipate further trends of spreading and derive vital findings for decision makers. While this application case seems quite simple, there are some tough questions to be answered in advance, which steer the further design decisions: How much can the monitored organization be trusted to be a partner in the search for malware? How much control of the scanning process should be delegated to the organization? What is the right level of search depth? How to deal with confidential indicators? What can be derived from encrypted traffic? How are new indicators distributed, prioritized, and scan targets selected in a scalable manner? What is a good strategy to re-schedule scans to derive meaningful data on trends, such as rate of spreading? This paper suggests a blueprint for a sensor network and raises related questions, outlines design principles, and discusses lessons learned from small-scale pilots.

This research explored how cyber security risks are managed across UK Critical National Infrastructure (CNI) sectors following implementation of the 2018 Networks and Information Security (NIS) legislation. Being in its infancy, there has been limited study into the effectiveness of this national framework for cyber risk management. The analysis of data gathered through interviews with key stakeholders against the NIS objectives indicated a collaborative implementation approach to improve cyber-risk management capabilities in CNI sectors. However, more work is required to bridge the gaps in the NIS framework to ensure holistic security across cyber spaces as well as non-cyber elements: cyber-physical security, cross-sector CNI service security measures, outcome-based regulatory assessments and risks due to connected smart technology implementations alongside legacy systems. This paper proposes ten key recommendations to counter the danger of not meeting the NIS key strategic objectives. In particular, it recommends that the approach to NIS implementation needs further alignment with its objectives, such as bringing a step-change in the cyber-security risk management capabilities of the CNI sectors.

The presence of artificial agents in human social networks is growing. From chatbots to robots, human experience in the developed world is moving towards a socio-technical system in which agents can be technological or biological, with increasingly blurred distinctions between. Given that emotion is a key element of human interaction, enabling artificial agents with the ability to reason about affect is a key stepping stone towards a future in which technological agents and humans can work together. This paper presents work on building intelligent computational agents that integrate both emotion and cognition. These agents are grounded in the well-established social-psychological Bayesian Affect Control Theory (BayesAct). The core idea of BayesAct is that humans are motivated in their social interactions by affective alignment: they strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and general world views as constructed through culturally shared symbols. This affective alignment creates cohesive bonds between group members, and is instrumental for collaborations to solidify as relational group commitments. BayesAct agents are motivated in their social interactions by a combination of affective alignment and decision theoretic reasoning, trading the two off as a function of the uncertainty or unpredictability of the situation. This paper provides a high-level view of dual process theories and advances BayesAct as a plausible, computationally tractable model based in social-psychological and sociological theory.

As many organizations deploy their chatbots on social network applications to interact with their customers, a person may switch among different chatbots for different services. To reduce the switching cost, this study proposed the Federated Chat Service Framework. The framework maintains user profiles and historical behaviors. Instead of deploying chatbots, organizations follow the rules of the framework to provide chat services. Therefore, the framework can organize service requests with context information and responses to emulate the conversations between users and chat services. Consequently, the study can hopefully contribute to reducing the cost for a user to communicate with different chatbots.

Modern advances in sensor, computing, and communication technologies enable various smart grid applications. The heavy dependence on communication technology has highlighted the vulnerability of the electricity grid to false data injection (FDI) attacks that can bypass bad data detection mechanisms. Existing mitigation in the power system either focus on redundant measurements or protect a set of basic measurements. These methods make specific assumptions about FDI attacks, which are often restrictive and inadequate to deal with modern cyber threats. In the proposed approach, a deep learning based framework is used to detect injected data measurement. Our time-series anomaly detector adopts a Convolutional Neural Network (CNN) and a Long Short Term Memory (LSTM) network. To effectively estimate system variables, our approach observes both data measurements and network level features to jointly learn system states. The proposed system is tested on IEEE 39-bus system. Experimental analysis shows that the deep learning algorithm can identify anomalies which cannot be detected by traditional state estimation bad data detection.

This article shows the possibility of detection of the hidden information in images. This is the approach to steganalysis than the basic data about the image and the information about the hiding method of the information are unknown. The architecture of the convolutional neural network makes it possible to detect small changes in the image with high probability.

While the number of mobile applications are rapidly growing, these applications are often coming with numerous security flaws due to the lack of appropriate coding practices. Security issues must be addressed earlier in the development lifecycle rather than fixing them after the attacks because the damage might already be extensive. Early elimination of possible security vulnerabilities will help us increase the security of our software and mitigate or reduce the potential damages through data losses or service disruptions caused by malicious attacks. However, many software developers lack necessary security knowledge and skills required at the development stage, and Secure Mobile Software Development (SMSD) is not yet well represented in academia and industry. In this paper, we present a static analysis-based security analysis approach through design and implementation of a plugin for Android Development Studio, namely DroidPatrol. The proposed plugins can support developers by providing list of potential vulnerabilities early.

With the rapid growth in online services, hacking (alternatively attacking) on online database applications has become a grave concern now. Attacks on online database application are being frequently reported. Among these attacks, the SQL injection attack is at the top of the list. The hackers alter the SQL query sent by the user and inject malicious code therein. Hence, they access the database and manipulate the data. It is reported in the literature that the traditional SQL injection detection algorithms fail to prevent this type of attack. In this paper, we propose a machine learning based heuristic algorithm to prevent the SQL injection attack. We use a dataset of 616 SQL statements to train and test 23 different machine learning classifiers. Among these classifiers, we select the best five classifiers based on their detection accuracy and develop a Graphical User Interface (GUI) application based on these five classifiers. We test our proposed algorithm and the results show that our algorithm is able to detect the SQL injection attack with a high accuracy (93.8%).

SQL injection attacks are a kind of the greatest security risks on Web applications. Much research has been done to detect SQL injection attacks by rule matching and syntax tree. However, due to the complexity and variety of SQL injection vulnerabilities, these approaches fail to detect unknown and variable SQL injection attacks. In this paper, we propose a model, ATTAR, to detect SQL injection attacks using grammar pattern recognition and access behavior mining. The most important idea of our model is to extract and analyze features of SQL injection attacks in Web access logs. To achieve this goal, we first extract and customize Web access log fields from Web applications. Then we design a grammar pattern recognizer and an access behavior miner to obtain the grammatical and behavioral features of SQL injection attacks, respectively. Finally, based on two feature sets, machine learning algorithms, e.g., Naive Bayesian, SVM, ID3, Random Forest, and K-means, are used to train and detect our model. We evaluated our model on these two feature sets, and the results show that the proposed model can effectively detect SQL injection attacks with lower false negative rate and false positive rate. In addition, comparing the accuracy of our model based on different algorithms, ID3 and Random Forest have a better ability to detect various kinds of SQL injection attacks.

Data-driven verification methods utilize execution data together with models for establishing safety requirements. These are often the only tools available for analyzing complex, nonlinear cyber-physical systems, for which purely model-based analysis is currently infeasible. In this chapter, we outline the key concepts and algorithmic approaches for data-driven verification and discuss the guarantees they provide. We introduce some of the software tools that embody these ideas and present several practical case studies demonstrating their application in safety analysis of autonomous vehicles, advanced driver assist systems (ADAS), satellite control, and engine control systems.

Keystroke Dynamics is the study of typing patterns and rhythm for personal identification and traits. Keystrokes may be analysed as fixed text such as passwords or as continuous typed text such as documents. This paper reviews different classification metrics for continuous text, such as the A and R metrics, Canberra, Manhattan and Euclidean and introduces a variant of the Minkowski distance. To test the metrics, we adopted a substantial dataset containing 239 thousand records acquired under real, harsh, and unidealised conditions. We propose a new parameter for the Minkowski metric, and we reinforce another for the A metric, as initially stated by its authors.

Currently, organisations find it difficult to design a Decision Support System (DSS) that can predict various operational risks, such as financial and quality issues, with operational risks responsible for significant economic losses and damage to an organisation's reputation in the market. This paper proposes a new DSS for risk assessment, called the Fuzzy Inference DSS (FIDSS) mechanism, which uses fuzzy inference methods based on an organisation's big data collection. It includes the Emerging Association Patterns (EAP) technique that identifies the important features of each risk event. Then, the Mamdani fuzzy inference technique and several membership functions are evaluated using the firm's data sources. The FIDSS mechanism can enhance an organisation's decision-making processes by quantifying the severity of a risk as low, medium or high. When it automatically predicts a medium or high level, it assists organisations in taking further actions that reduce this severity level.

The use of uninitialized variables is a common issue. It could cause kernel information leak, which defeats the widely deployed security defense, i.e., kernel address space layout randomization (KASLR). Though a recent system called Bochspwn Reloaded reported multiple memory leaks in Windows kernels, how to effectively detect this issue is still largely behind. In this paper, we propose a new technique, i.e., differential replay, that could effectively detect the use of uninitialized variables. Specifically, it records and replays a program's execution in multiple instances. One instance is with the vanilla memory, the other one changes (or poisons) values of variables allocated from the stack and the heap. Then it compares program states to find references to uninitialized variables. The idea is that if a variable is properly initialized, it will overwrite the poisoned value and program states in two running instances should be the same. After detecting the differences, our system leverages the symbolic taint analysis to further identify the location where the variable was allocated. This helps us to identify the root cause and facilitate the development of real exploits. We have implemented a prototype called TimePlayer. After applying it to both Windows 7 and Windows 10 kernels (x86/x64), it successfully identified 34 new issues and another 85 ones that had been patched (some of them were publicly unknown.) Among 34 new issues, 17 of them have been confirmed as zero-day vulnerabilities by Microsoft.

Nowadays, the industrial control systems (ICS) face many challenges, where security is becoming one of the most crucial. This fact is caused by new connected environment, which brings among new possibilities also new vulnerabilities, threats, or possible attacks. The criminal acts in the ICS area increased over the past years exponentially, which caused the loss of billions of dollars. This also caused classical Intrusion Detection Systems and Intrusion Prevention Systems to evolve in order to protect among IT also ICS networks. However, these systems need sufficient data such as traffic logs, protocol information, attack patterns, anomaly behavior marks and many others. To provide such data, the requirements for the test environment are summarized in this paper. Moreover, we also introduce more than twenty common vulnerabilities across the ICS together with information about possible risk, attack vector (point), possible detection methods and communication layer occurrence. Therefore, the paper might be used as a base-ground for building sufficient data generator for machine learning and artificial intelligence algorithms often used in ICS/IDS systems.

Integrating Industrial Control Systems (ICS) with Corporate System (IT) is one of the most important industrial orientations. With recent cybersecurity attacks, the security of integrated ICS systems has become the priority of industrial world. Access control technologies such as firewalls are very important for Integrated ICS (IICS) systems to control communication across different networks to protect valuable resources. However, conventional firewalls are not always fully compatible with Industrial Control Systems. In fact, firewalls can introduce significant latency while ICS systems usually are very demanding in terms of timing requirements. Besides, most of existing firewalls do not support all industrial protocols. This paper proposes a new access control model for integrated ICS systems based on Domain and Type Enforcement (DTE). This new model allows to define and apply enforced access controls with respect of ICS timing requirements. Access controls definition is based on a high level language that can be used by ICS administrators with ease. This paper also proposes an initial generic ruleset based on the ISA95 functional model. This generic ruleset simplifies the deployment of DTE access controls and provides a good introduction to the DTE concepts for administrators.

The autonomic logistic system (ALS), first used by the U.S. military JSF, is a new conceptional system which supports prognostic and health management system of aircrafts, including such as real-time failure monitoring, remaining useful life prediction and maintenance decisions-making. However, the development of ALS faces some challenges. Firstly, current ALS is mainly based on client/server architecture, which is very complex in a large-scale aircraft control center and software is required to be reconfigured for every accessed node, which will increase the cost and decrease the expandability of deployment for large scale aircraft control centers. Secondly, interpretation of telemetry parameters from the aircraft is a tough task considering various real-time flight conditions, including instructions from controllers, work statements of single machines or machine groups, and intrinsic physical meaning of telemetry parameters. It is troublesome to meet the expectation of full representing the relationship between faults and tests without a standard model. Finally, typical diagnostic algorithms based on dependency matrix are inefficient, especially the temporal waste when dealing with thousands of test points and fault modes, for the reason that the time complexity will increase exponentially as dependency matrix expansion. Under this situation, this paper proposed a distributed ALS under complex operating conditions, which has the following contributions 1) introducing a distributed system based on browser/server architecture, which is divided overall system into primary control system and diagnostic and health assessment platform; 2) designing a novel interface for modelling the interpretation rules of telemetry parameters and the relationship between faults and tests in consideration of multiple elements of aircraft conditions; 3) proposing a promoted diagnostic algorithm under parallel computing in order to decrease the computing time complexity. what's more, this paper develops a construction with 3D viewer of aircraft for user to locate fault points and presents repairment instructions for maintenance personnels based on Interactive Electronic Technical Manual, which supports both online and offline. A practice in a certain aircraft demonstrated the efficiency of improved diagnostic algorithm and proposed ALS.

Order-revealing encryption (ORE) is a basic cryptographic primitive for ciphertext comparisons based on the order relationship of plaintexts while maintaining the privacy of them. In the data era we are experiencing, cross-dataset transactions become ubiquitous in practice. However, almost all the previous ORE schemes can only support comparisons on ciphertexts from the same user, which does not meet the requirement for the multi-user environment. In this work, we introduce and design ORE schemes with delegation functionality, which is referred to as delegatable ORE (DORE). The "delegation" here is an authorization that allows for efficient ciphertext comparisons among different users. To the best of our knowledge, it is the first ORE that allows an user to delegate the comparison privilege for his ciphertexts, which also opens the door for future explorations. At the heart of the construction and analysis of DORE is a new building tool proposed in this work, named delegatable equality-revealing encoding (DERE), which might be of independent interest.

This paper proposes a multi-target tracking and identity management method with multiple sensors: a primary sensor with a large detection range to provide the targets' state estimates, and multiple secondary sensors capable of recognizing the targets' identities. Each of the secondary sensors is assigned to a sector of the operation area; a secondary sensor decides which target in its assigned sector to be identified and controls itself to identify the target. We formulate the decision-making process as an optimization problem to minimize the uncertainty of the targets' identities subject to the sensor dynamic constraints. The proposed algorithm is decentralized since the secondary sensors only communicate with the primary sensor for the target information, and need not to synchronize with each other. By integrating the proposed algorithm with the existing multi-target tracking algorithms, we develop a closed-loop multi-target tracking and identity management algorithm. The effectiveness of the proposed algorithm is demonstrated with illustrative numerical examples.

The future fifth-generation (5G) mobile communications system has already become a focus around the world. A large number of late-model services and applications including high definition visual communication, internet of vehicles, multimedia interaction, mobile industry automation, and etc, will be added to 5G network platform in the future. Different application services have different security requirements. However, the current user authentication for services and applications: Extensible Authentication Protocol (EAP) suggested by the 3GPP committee, is only a unitary authentication model, which is unable to meet the diversified security requirements of differentiated services. In this paper, we present a new diversified identity management as well as a flexible and composable three-factor authentication mechanism for different applications in 5G multi-service systems. The proposed scheme can provide four identity authentication methods for different security levels by easily splitting or assembling the proposed three-factor authentication mechanism. Without a design of several different authentication protocols, our proposed scheme can improve the efficiency, service of quality and reduce the complexity of the entire 5G multi-service system. Performance analysis results show that our proposed scheme can ensure the security with ideal efficiency.

Stealth malware, a representative tool of advanced persistent threat (APT) attacks, in particular poses an increased threat to cyber-physical systems (CPS). Due to the use of stealthy and evasive techniques (e.g., zero-day exploits, obfuscation techniques), stealth malwares usually render conventional heavyweight countermeasures (e.g., exploits patching, specialized ant-malware program) inapplicable. Light-weight countermeasures (e.g., containment techniques), on the other hand, can help retard the spread of stealth malwares, but the ensuing side effects might violate the primary safety requirement of CPS. Hence, defenders need to find a balance between the gain and loss of deploying light-weight countermeasures. To address this challenge, we model the persistent anti-malware process as a shortest-path tree interdiction (SPTI) Stackelberg game, and safety requirements of CPS are introduced as constraints in the defender's decision model. Specifically, we first propose a static game (SSPTI), and then extend it to a multi-stage dynamic game (DSPTI) to meet the need of real-time decision making. Both games are modelled as bi-level integer programs, and proved to be NP-hard. We then develop a Benders decomposition algorithm to achieve the Stackelberg Equilibrium of SSPTI. Finally, we design a model predictive control strategy to solve DSPTI approximately by sequentially solving an approximation of SSPTI. The extensive simulation results demonstrate that the proposed dynamic defense strategy can achieve a balance between fail-secure ability and fail-safe ability while retarding the stealth malware propagation in CPS.