Biblio

More and more organizations and individuals start to pay attention to real-time threat intelligence to protect themselves from the complicated, organized, persistent and weaponized cyber attacks. However, most users worry about the trustworthiness of threat intelligence provided by TISPs (Threat Intelligence Sharing Platforms). The trust evaluation mechanism has become a hot topic in applications of TISPs. However, most current TISPs do not present any practical solution for trust evaluation of threat intelligence itself. In this paper, we propose a graph mining-based trust evaluation mechanism with multidimensional features for large-scale heterogeneous threat intelligence. This mechanism provides a feasible scheme and achieves the task of trust evaluation for TISP, through the integration of a trust-aware intelligence architecture model, a graph mining-based intelligence feature extraction method, and an automatic and interpretable trust evaluation algorithm. We implement this trust evaluation mechanism in a practical TISP (called GTTI), and evaluate the performance of our system on a real-world dataset from three popular cyber threat intelligence sharing platforms. Experimental results show that our mechanism can achieve 92.83% precision and 93.84% recall in trust evaluation. To the best of our knowledge, this work is the first to evaluate the trust level of heterogeneous threat intelligence automatically from the perspective of graph mining with multidimensional features including source, content, time, and feedback. Our work is beneficial to provide assistance on intelligence quality for the decision-making of human analysts, build a trust-aware threat intelligence sharing platform, and enhance the availability of heterogeneous threat intelligence to protect organizations against cyberspace attacks effectively.

Nowadays, the domain of Information System (IS) security is closely related to that of Risk Management (RM). As an immediate consequence, talking about and tackling the security of IS imply the implementation of a set of mechanisms that aim to reduce or eliminate the risk of IS degradations. Also, the high cadence of IS evolution requires careful consideration of corresponding measures to prevent or mitigate security risks that may cause the degradation of these systems. From this perspective, an access control service is subjected to a number of rules established to ensure the integrity and confidentiality of the handled data. During their lifecycle, the use or manipulation of Access Control Policies (ACP) is accompanied with several defects that are made intentionally or not. For many years, these defects have been the subject of numerous studies either for their detection or for the analysis of the risks incurred by IS to their recurrence and complexity. In our research works, we focus on the analysis and risk assessment of noncompliance anomalies in concrete instances of access control policies. We complete our analysis by studying and assessing the risks associated with the correlation that may exist between different anomalies. Indeed, taking into account possible correlations can make a significant contribution to the reliability of IS. Identifying correlation links between anomalies in concrete instances of ACP contributes in discovering or detecting new scenarios of alterations and attacks. Therefore, once done, this study mainly contributes in the improvement of our risk assessment model.

We present research on the security of the most popular websites, ranked according to Alexa's top one million list, based on an HTTP response headers analysis. For each of the domains included in the list, we made four different requests: an HTTP/1.1 request to the domain itself and to its "www" subdomain and two more equivalent HTTPS requests. Redirections were always followed. A detailed discussion of the request process and main outcomes is presented, including X.509 certificate issues and comparison of results with equivalent HTTP/2 requests. The body of the responses was discarded, and the HTTP response header fields were stored in a database. We analysed the prevalence of the most important response headers related to web security aspects. In particular, we took into account Strict- Transport-Security, Content-Security-Policy, X-XSS-Protection, X-Frame-Options, Set-Cookie (for session cookies) and X-Content-Type. We also reviewed the contents of response HTTP headers that potentially could reveal unwanted information, like Server (and related headers), Date and Referrer-Policy. This research offers an up-to-date survey of current prevalence of web security policies implemented through HTTP response headers and concludes that most popular sites tend to implement it noticeably more often than less popular ones. Equally, HTTPS sites seem to be far more eager to implement those policies than HTTP only websites. A comparison with previous works show that web security policies based on HTTP response headers are continuously growing, but still far from satisfactory widespread adoption.

Database outsourcing is one of the important utilities in cloud computing in which the Information Proprietor (IP) transfers the database administration to the Cloud Service Provider (CSP) in order to minimize the administration cost and preservation expenses of the database. Inspite of its immense profit, it undergoes few security issues such as privacy of deployed database and provability of search results. In the recent past, few of the studies have been carried out on provability of search results of Outsourced Database (ODB) that affords correctness and completeness of search results. But in the existing schemes, since there is flow of data between the Information Proprietor and the clients frequently, huge communication cost prevails at the Information Proprietor side. To address this challenge, in this paper we propose Efficient Auditing for Outsourced Database with Token Enforced Cloud Storage (EAODBT). The proposed scheme reduces the large communication cost prevailing at the Information Proprietor side and achieves correctness and completeness of search results even if the mischievous CSP knowingly sends a null set. Experimental analysis show that the proposed scheme has totally reduced the huge communication cost prevailing between Information Proprietor and clients, and simultaneously achieves the correctness and completeness of search results.

Nowadays database outsourcing has become business owners' preferred option and they are benefiting from its flexibility, reliability, and low cost. However, because database service providers cannot always be fully trusted and data owners will no longer have a direct control over their own data, how to make the outsourced data secure becomes a hot research topic. From the data integrity protection aspect, the client wants to make sure the data returned is correct, complete, and up-to-date. Previous research work in literature put more efforts on data correctness, while data completeness is still a challenging problem to solve. There are some existing works that tried to protect the completeness of data. Unfortunately, these solutions were considered not fully solving the problem because of their high communication or computation overhead. The implementations and limitations of existing works will be further discussed in this paper. From the data confidentiality protection aspect, order-preserving encryption (OPE) is a widely used encryption scheme in protecting data confidentiality. It allows the client to perform range queries and some other operations such as GROUP BY and ORDER BY over the OPE encrypted data. Therefore, it is worthy to develop a solution that allows user to verify the query completeness for an OPE encrypted database so that both data confidentiality and completeness are both protected. Inspired by this motivation, we propose a new data completeness protecting scheme by inserting fake tuples into databases. Both the real and fake tuples are OPE encrypted and thus the cloud server cannot distinguish among them. While our new scheme is much more efficient than all existing approaches, the level of security protection remains the same.

As cloud storage and computing gains popularity, data entrusted to the cloud has the potential to be exposed to more people and thus more vulnerable to attacks. It is important to develop mechanisms to protect data privacy and integrity so that clients can safely outsource their data to the cloud. We present a method for ensuring data completeness which is one facet of the data integrity problem. Our approach converts a standard database to a Completeness Protected Database (CPDB) by inserting some semantic fake data before outsourcing it to the cloud. These fake data are initially produced using our generating function which uses Order Preserving Encryption, which allows the user to be able to regenerate these fake data and match them to fake data returned from a range query to check for completeness. The CPDB is innovative in the following ways: (1) fake data is deterministically generated but is semantically indistinguishable from other existing data; (2) since fake data is generated by deterministic functions, data owners do not need to locally store the fake data that have been inserted, instead they can re-generate fake data using the functions; (3) no costly data encryption/signature is used in our scheme compared to previous work which encrypt/sign the entire database.

Several cloud providers have started to offer specific data management services by responding to the new trend called the Internet of Things (IoT). In recent years, we have already seen that cloud computing has managed to serve IoT needs for data retrieval, processing and visualization transparent for the user side. IoT-Cloud systems for smart cities and smart regions can be very complex, therefore their design and analysis should be supported by means of simulation. Nevertheless, the models used in simulation environments should be as close as to the real world utilization to provide reliable results. To facilitate such simulations, in earlier works we proposed an IoT trace archiving service called SUMMON that can be used to gather real world datasets, and to reuse them for simulation experiments. In this paper we provide an extension to SUMMON with an automated web crawling service that gathers IoT and sensor data from publicly available websites. We introduce the architecture and operation of this approach, and exemplify it utilization with three use cases. The provided archiving solution can be used by simulators to perform realistic evaluations.

In order to standardize and describe vulnerability information in detail as far as possible and realize knowledge sharing, reuse and extension at the semantic level, a vulnerability ontology is constructed based on the information security public databases such as CVE, CWE and CAPEC and industry public standards like CVSS. By analyzing the relationship between vulnerability class and weakness class, inference rules are defined to realize knowledge inference from vulnerability instance to its consequence and from one vulnerability instance to another vulnerability instance. The experimental results show that this model can analyze the causal and congeneric relationships between vulnerability instances, which is helpful to repair vulnerabilities and predict attacks.

DNS based domain name resolution has been known as one of the most fundamental Internet services. In the meanwhile, DNS cache poisoning attacks also have become a critical threat in the cyber world. In addition to Kaminsky attacks, the falsified data from the compromised authoritative DNS servers also have become the threats nowadays. Several solutions have been proposed in order to prevent DNS cache poisoning attacks in the literature for the former case such as DNSSEC (DNS Security Extensions), however no effective solutions have been proposed for the later case. Moreover, due to the performance issue and significant workload increase on DNS cache servers, DNSSEC has not been deployed widely yet. In this work, we propose an advanced detection method against DNS cache poisoning attacks using machine learning techniques. In the proposed method, in addition to the basic 5-tuple information of a DNS packet, we intend to add a lot of special features extracted based on the standard DNS protocols as well as the heuristic aspects such as “time related features”, “GeoIP related features” and “trigger of cached DNS data”, etc., in order to identify the DNS response packets used for cache poisoning attacks especially those from compromised authoritative DNS servers. In this paper, as a work in progress, we describe the basic idea and concept of our proposed method as well as the intended network topology of the experimental environment while the prototype implementation, training data preparation and model creation as well as the evaluations will belong to the future work.

The "aging" phenomenon occurs after the long-term running of software, with the fault rate rising and running efficiency dropping. As there is no corresponding testing type for this phenomenon among conventional software tests, "software runtime accumulative testing" is proposed. Through analyzing several examples of software aging causing serious accidents, software is placed in the system environment required for running and the occurrence mechanism of software aging is analyzed. In addition, corresponding testing contents and recommended testing methods are designed with regard to all factors causing software aging, and the testing process and key points of testing requirement analysis for carrying out runtime accumulative testing are summarized, thereby providing a method and guidance for carrying out "software runtime accumulative testing" in software engineering.

Anti-virus software (AVS) tools are used to detect Malware in a system. However, software-based AVS are vulnerable to attacks. A malicious entity can exploit these vulnerabilities to subvert the AVS. Recently, hardware components such as Hardware Performance Counters (HPC) have been used for Malware detection. In this paper, we propose PREEMPT, a zero overhead, high-accuracy and low-latency technique to detect Malware by re-purposing the embedded trace buffer (ETB), a debug hardware component available in most modern processors. The ETB is used for post-silicon validation and debug and allows us to control and monitor the internal activities of a chip, beyond what is provided by the Input/Output pins. PREEMPT combines these hardware-level observations with machine learning-based classifiers to preempt Malware before it can cause damage. There are many benefits of re-using the ETB for Malware detection. It is difficult to hack into hardware compared to software, and hence, PREEMPT is more robust against attacks than AVS. PREEMPT does not incur performance penalties. Finally, PREEMPT has a high True Positive value of 94% and maintains a low False Positive value of 2%.

Today, intrusion detection and prevention systems (IDS / IPS) are a necessary element of protection against network attacks. The main goal of such systems is to identify an unauthorized access to the network and take appropriate countermeasures: alarming security officers about intrusion, reconfiguration of firewall to block further acts of the attacker, protection against cyberattacks and malware. For traditional computer networks there are a large number of sufficiently effective approaches for protection against malicious activity, however, for the rapidly developing dynamic adhoc networks (Internet of Things - IoT, MANET, WSN, etc.) the task of creating a universal protection means is quite acute. In this paper, we review various methods for detecting polymorphic intrusion activity (polymorphic viral code and sequences of operations), present a comparative analysis, and implement the suggested technology for detecting polymorphic chains of operations using bioinformatics for IoT. The proposed approach has been tested with different lengths of operation sequences and different k-measures, as a result of which the optimal parameters of the proposed method have been determined.

Malware authors are known to reuse existing code, this development process results in software evolution and a sequence of versions of a malware family containing functions that show a divergence from the initial version. This paper proposes the term evolved similarity to account for this gradual divergence of similarity across the version history of a malware family. While existing techniques are able to match functions in different versions of malware, these techniques work best when the version changes are relatively small. This paper introduces the concept of evolved similarity and presents automated Evolved Similarity Techniques (EST). EST differs from existing malware function similarity techniques by focusing on the identification of significantly modified functions in adjacent malware versions and may also be used to identify function similarity in malware samples that differ by several versions. The challenge in identifying evolved malware function pairs lies in identifying features that are relatively invariant across evolved code. The research in this paper makes use of the function call graph to establish these features and then demonstrates the use of these techniques using Zeus malware.

"Good Governance" - may it be corporate or governmental, is a badly needed focus area in the world today where the companies and governments are struggling to survive the political and economical turmoil around the globe. All governments around the world have a tendency of expanding the size of their government, but eventually they would be forced to think reducing the size by incorporating information technology as a way to provide services to the citizens effectively and efficiently. Hence our attempt is to offer a complete solution from birth of a citizen till death encompassing all the necessary services related to the well being of a person living in a society. Our research and analysis would explore the pros and cons of using IT as a solution to our problems and ways to implement them for a best outcome in e-Governance occasionally comparing with the present scenario when relevant.

Collective self-adaptive systems (CSAS) are distributed and interconnected systems composed of multiple agents that can perform complex tasks such as environmental data collection, search and rescue operations, and discovery of natural resources. By providing individual agents with learning capabilities, CSAS can cope with challenges related to distributed sensing and decision-making and operate in uncertain environments. This unique characteristic of CSAS enables the collective to exhibit robust behaviour while achieving system-wide and agent-specific goals. Although learning has been explored in many CSAS applications, selecting suitable learning models and techniques remains a significant challenge that is heavily influenced by expert knowledge. We address this gap by performing a multifaceted analysis of existing CSAS with learning capabilities reported in the literature. Based on this analysis, we introduce a 3D framework that illustrates the learning aspects of CSAS considering the dimensions of autonomy, knowledge access, and behaviour, and facilitates the selection of learning techniques and models. Finally, using example applications from this analysis, we derive open challenges and highlight the need for research on collaborative, resilient and privacy-aware mechanisms for CSAS.

The battlefield environment differs from the natural environment in terms of irregular communications and the possibility of destroying communication and medical units by enemy forces. Information that can be collected in a war environment by soldiers is important information and must reach top-level commanders in time for timely decisions making. Also, ambulance staff in the battlefield need to enter the data of injured soldiers after the first aid, so that the information is available for the field hospital staff to prepare the needs for incoming injured soldiers.In this research, we propose two transaction techniques to handle these issues and use different concurrency control protocols, depending on the nature of the transaction and not a one concurrency control protocol for all types of transactions. Message transaction technique is used to collect valuable data from the battlefield by soldiers and allows top-level commanders to view it according to their permissions by logging into the system, to help them make timely decisions. In addition, use the capabilities of DBMS tools to organize data and generate reports, as well as for future analysis. Medical service unit transactional workflow technique is used to provides medical information to the medical authorities about the injured soldiers and their status, which helps them to prepare the required needs before the wounded soldiers arrive at the hospitals. Both techniques handle the disconnection problem during transaction processing.In our approach, the transaction consists of four phases, reading, editing, validation, and writing phases, and its processing is based on the optimistic concurrency control protocol, and the rules of actionability that describe how a transaction behaves if a value-change is occurred on one or more of its attributes during its processing time by other transactions.

The US Federal Communications Commission (FCC) has recently mandated the database-driven dynamic spectrum access where unlicensed secondary users search for idle bands and use them opportunistically. The database-driven dynamic spectrum access approach is regarded for minimizing any harmful interference to licensed primary users caused by RF channel sensing uncertainties. However, when several secondary users (or several malicious users) query the RF spectrum database at the same time, spectrum server could experience denial of service (DoS) attack. In this paper, we investigate the Aggregated-Query-as-a-Secure-Service (AQaaSS) for querying RF spectrum database by secondary users for opportunistic wireless communications where selected number of secondary users aka grid leaders, query the database on behalf of all other secondary users, aka grid followers and relay the idle channel information to grid followers. Furthermore, the grid leaders are selected based on their both reputation or trust level and location in the network for the integrity of the information that grid followers receive. Grid followers also use the weighted majority voting to filter out comprised information about the idle channels. The performance of the proposed approach is evaluated using numerical results. The proposed approach gives lower latency (or same latency) to the secondary users and lower load (or same load) to the RF spectrum database server when more number of secondary users (or less number of secondary users) query than that of the server capacity.

As the technology is growing rapidly, the development of systems and software are becoming more complex. For this reason, the security of software and web applications become more vulnerable. In the last two decades, the use of internet application and security hacking activities are on top of the glance. The organizations are having the biggest challenge that how to secure their web applications from the rapidly increasing cyber threats because the organization can't compromise the security of their sensitive information. Vulnerability Assessment and Penetration Testing techniques may help organizations to find security loopholes. The weakness can be the asset for the attacker if the organizations are not aware of this. Vulnerability Assessment and Penetration Testing helps an organization to cover the security loopholes and determine their security arrangements are working as per defined policies or not. To cover the tracks and mitigate the threats it is necessary to install security patches. This paper includes the survey on the current vulnerabilities, determination of those vulnerabilities, the methodology used for determination, tools used to determine the vulnerabilities to secure the organizations from cyber threat.

We propose a new architecture introducing AI to span the control layer and the data layer in SDON. This demonstration shows the cooperation of the AI engines in two layers in dealing with failure disposal.

Physically Unclonable Functions (PUFs) promise to be a critical hardware primitive to provide unique identities to billions of connected devices in Internet of Things (IoTs). In traditional authentication protocols a user presents a set of credentials with an accompanying proof such as password or digital certificate. However, IoTs need more evolved methods as these classical techniques suffer from the pressing problems of password dependency and inability to bind access requests to the “things” from which they originate. Additionally, the protocols need to be lightweight and heterogeneous. Although PUFs seem promising to develop such mechanism, it puts forward an open problem of how to develop such mechanism without needing to store the secret challenge-response pair (CRP) explicitly at the verifier end. In this paper, we develop an authentication and key exchange protocol by combining the ideas of Identity based Encryption (IBE), PUFs and Key-ed Hash Function to show that this combination can help to do away with this requirement. The security of the protocol is proved formally under the Session Key Security and the Universal Composability Framework. A prototype of the protocol has been implemented to realize a secured video surveillance camera using a combination of an Intel Edison board, with a Digilent Nexys-4 FPGA board consisting of an Artix-7 FPGA, together serving as the IoT node. We show, though the stand-alone video camera can be subjected to man-in-the-middle attack via IP-spoofing using standard network penetration tools, the camera augmented with the proposed protocol resists such attacks and it suits aptly in an IoT infrastructure making the protocol deployable for the industry.

Detection of network attacks is the first step to network security. Many different methods for attack detection were proposed in the past. However, descriptions of these methods are often not complete and it is difficult to verify that the actual implementation matches the description. In this demo paper, we propose to use Complex Event Processing (CEP) for developing detection methods based on network flows. By writing the detection methods in an Event Processing Language (EPL), we can address the above-mentioned problems. The SQL-like syntax of most EPLs is easily readable so the detection method is self-documented. Moreover, it is directly executable in the CEP system, which eliminates inconsistencies between documentation and implementation. The demo will show a running example of a multi-stage HTTP brute force attack detection using Esper and its EPL.

In the era of digitization, data security is a vital role in message transmission and all systems that deal with users require stronger encryption techniques that against brute force attack. Honey encryption (HE) algorithm is a user data protection algorithm that can deceive the attackers from unauthorized access to user, database and websites. The main part of conventional HE is distribution transforming encoder (DTE). However, the current DTE process using cumulative distribution function (CDF) has the weakness in message space limitation because CDF cannot solve the probability theory in more than four messages. So, we propose a new method in DTE process using discrete distribution function in order to solve message space limitation problem. In our proposed honeywords generation method, the current weakness of existing honeywords generation method such as storage overhead problem can be solved. In this paper, we also describe the case studies calculation of DTE in order to prove that new DTE process has no message space limitation and mathematical model using discrete distribution function for DTE process facilitates the distribution probability theory.

Software Defined Networking (SDN) provides new functionalities to efficiently manage the network traffic, which can be used to enhance the networking capabilities to support the growing communication demands today. But at the same time, it introduces new attack vectors that can be exploited by attackers. Hence, evaluating and selecting countermeasures to optimize the security of the SDN is of paramount importance. However, one should also take into account the trade-off between security and performance of the SDN. In this paper, we present a security optimization approach for the SDN taking into account the trade-off between security and performance. We evaluate the security of the SDN using graphical security models and metrics, and use queuing models to measure the performance of the SDN. Further, we use Genetic Algorithms, namely NSGA-II, to optimally select the countermeasure with performance and security constraints. Our experimental analysis results show that the proposed approach can efficiently compute the countermeasures that will optimize the security of the SDN while satisfying the performance constraints.

Information security has developed rapidly over the recent years with a key being the emergence of social media. To standardize this discipline, security of an individual becomes an urgent concern. In 2019, it is estimated that there will be over 2.5 billion social media users around the globe. Unfortunately, anonymous identity has become a major concern for the security advisors. Due to the technological advancements, the phishers are able to access the confidential information. To resolve these issues numerous solutions have been proposed, such as biometric identification, facial and audio recognition etc prior access to any highly secure forum on the web. Generating digital signatures is the recent trend being incorporated in the field of digital security. We have designed an algorithm that after generating 68 point facial landmark, converts the image to a highly compressed and secure digital signature. The proposed algorithm generates a unique signature for an individual which when stored in the user account information database will limit the creation of fake or multiple accounts. At the same time the algorithm reduces the database storage overhead as it stores the facial identity of an individual in the form of a compressed textual signature rather than the traditional method where the image file was being stored, occupying lesser amount of space and making it more efficient in terms of searching, fetching and manipulation. A unique new analysis of the features produced at intermediate layers has been applied. Here, we opt to use the normal and two opposites' angular measures of the triangle as the invariance. It simply acts as the real-time optimized encryption procedure to achieve the reliable security goals explained in detail in the later sections.

In this paper, we implemented a mobile augmented reality system based on cloud computing. This system uses a mobile device with a camera to capture images of book spines and sends processed features to the cloud. In the cloud, the features are compared with the database and the information of the best matched book would be sent back to the mobile device. The information will then be rendered on the display via augmented reality. In order to reduce the transmission cost, the mobile device is used to perform most of the image processing tasks, such as the preprocessing, resizing, corner detection, and augmented reality rendering. On the other hand, the cloud is used to realize routine but large quantity feature comparisons. Using the cloud as the database also makes the future extension much more easily. For our prototype system, we use an Android smart phone as our mobile device, and Chunghwa Telecoms hicloud as the cloud.