Biblio

Security is often treated as secondary or a non- functional feature of software which influences the approach of vendors and developers when describing their products often in terms of what it can do (Use Cases) or offer customers. However, tides are beginning to change as more experienced customers are beginning to demand for more secure and reliable software giving priority to confidentiality, integrity and privacy while using these applications. This paper presents the MOTH (Modeling Threats with Hybrid Techniques) framework designed to help organizations secure their software assets from attackers in order to prevent any instance of SQL Injection Attacks (SQLIAs). By focusing on the attack vectors and vulnerabilities exploited by the attackers and brainstorming over possible attacks, developers and security experts can better strategize and specify security requirements required to create secure software impervious to SQLIAs. A live web application was considered in this research work as a case study and results obtained from the hybrid models extensively exposes the vulnerabilities deep within the application and proposed resolution plans for blocking those security holes exploited by SQLIAs.
 

Malicious emails pose substantial threats to businesses. Whether it is a malware attachment or a URL leading to malware, exploitation or phishing, attackers have been employing emails as an effective way to gain a foothold inside organizations of all kinds. To combat email threats, especially targeted attacks, traditional signature- and rule-based email filtering as well as advanced sandboxing technology both have their own weaknesses. In this paper, we propose a predictive analysis approach that learns the differences between legit and malicious emails through static analysis, creates a machine learning model and makes detection and prediction on unseen emails effectively and efficiently. By comparing three different machine learning algorithms, our preliminary evaluation reveals that a Random Forests model performs the best.

Reversible circuits are vulnerable to intellectual property and integrated circuit piracy. To show these vulnerabilities, a detailed understanding on how to identify the function embedded in a reversible circuit is crucial. To obtain the embedded function, one needs to know the synthesis approach used to generate the reversible circuit in the first place. We present a machine learning based scheme to identify the synthesis approach using telltale signs in the design.

In the present time, there has been a huge increase in large data repositories by corporations, governments, and healthcare organizations. These repositories provide opportunities to design/improve decision-making systems by mining trends and patterns from the data set (that can provide credible information) to improve customer service (e.g., in healthcare). As a result, while data sharing is essential, it is an obligation to maintaining the privacy of the data donors as data custodians have legal and ethical responsibilities to secure confidentiality. This research proposes a 2-layer privacy preserving (2-LPP) data sanitization algorithm that satisfies ε-differential privacy for publishing sanitized data. The proposed algorithm also reduces the re-identification risk of the sanitized data. The proposed algorithm has been implemented, and tested with two different data sets. Compared to other existing works, the results obtained from the proposed algorithm show promising performance.

Learning analytics open up a complex landscape of privacy and policy issues, which, in turn, influence how learning analytics systems and practices are designed. Research and development is governed by regulations for data storage and management, and by research ethics. Consequently, when moving solutions out the research labs implementers meet constraints defined in national laws and justified in privacy frameworks. This paper explores how the OECD, APEC and EU privacy frameworks seek to regulate data privacy, with significant implications for the discourse of learning, and ultimately, an impact on the design of tools, architectures and practices that now are on the drawing board. A detailed list of requirements for learning analytics systems is developed, based on the new legal requirements defined in the European General Data Protection Regulation, which from 2018 will be enforced as European law. The paper also gives an initial account of how the privacy discourse in Europe, Japan, South-Korea and China is developing and reflects upon the possible impact of the different privacy frameworks on the design of LA privacy solutions in these countries. This research contributes to knowledge of how concerns about privacy and data protection related to educational data can drive a discourse on new approaches to privacy engineering based on the principles of Privacy by Design. For the LAK community, this study represents the first attempt to conceptualise the issues of privacy and learning analytics in a cross-cultural context. The paper concludes with a plan to follow up this research on privacy policies and learning analytics systems development with a new international study.

Insider threat is a significant security risk for information system, and detection of insider threat is a major concern for information system organizers. Recently existing work mainly focused on the single pattern analysis of user single-domain behavior, which were not suitable for user behavior pattern analysis in multi-domain scenarios. However, the fusion of multi-domain irrelevant features may hide the existence of anomalies. Previous feature learning methods have relatively a large proportion of information loss in feature extraction. Therefore, this paper proposes a hybrid model based on the deep belief network (DBN) to detect insider threat. First, an unsupervised DBN is used to extract hidden features from the multi-domain feature extracted by the audit logs. Secondly, a One-Class SVM (OCSVM) is trained from the features learned by the DBN. The experimental results on the CERT dataset demonstrate that the DBN can be used to identify the insider threat events and it provides a new idea to feature processing for the insider threat detection.

A problem in managing the ever growing computer networks nowadays is the analysis of events detected by intrusion detection systems and the classification whether an event was correctly detected or not. When a false positive is detected by the user, changes to the configuration must be made and evaluated before they can be adopted to productive use. This paper describes an approach for a visual analysis framework that integrates the monitoring and analysis of events and the resulting changes on the configuration of detection systems after finding false alarms, together with a preliminary simulation and evaluation of the changes.

To combat blind in-window attacks against TCP, changes proposed in RFC 5961 have been implemented by Linux since late 2012. While successfully eliminating the old vulnerabilities, the new TCP implementation was reported in August 2016 to have introduced a subtle yet serious security flaw. Assigned CVE-2016-5696, the flaw exploits the challenge ACK rate limiting feature that could allow an off-path attacker to infer the presence/absence of a TCP connection between two arbitrary hosts, terminate such a connection, and even inject malicious payload. In this work, we perform a comprehensive measurement of the impact of the new vulnerability. This includes (1) tracking the vulnerable Internet servers, (2) monitoring the patch behavior over time, (3) picturing the overall security status of TCP stacks at scale. Towards this goal, we design a scalable measurement methodology to scan the Alexa top 1 million websites for almost 6 months. We also present how notifications impact the patching behavior, and compare the result with the Heartbleed and the Debian PRNG vulnerability. The measurement represents a valuable data point in understanding how Internet servers react to serious security flaws in the operating system kernel.

As we know, we are already facing IoT threat and under IoT attacks. However, there are only a few discussions on, how to analyze this kind of cyber threat and malwares. In this paper, we propose IoT sandbox which can support different type of CPU architecture. It can be used to analyze IoT malwares, collect network packets, identify spread method and record malwares behaviors. To make sure our IoT sandbox can be functional, we implement it and use the Zollard botnet for experiment. According to our experimental data, we found that at least 71,148 IP have been compromised. Some of them are IoT devices (DVR, Web Camera, Router WiFi Disk, Set-top box) and others are ICS devices (Heat pump and ICS data acquisition server). Based on our IoT sandbox technology, we can discover an IoT malware in an early stage. This could help IT manager or security experts to analysis and determine IDS rules. We hope this research can prevent IoT threat and enhance IoT Security in the near future.

Cloud computing is significantly reshaping the computing industry built around core concepts such as virtualization, processing power, connectivity and elasticity to store and share IT resources via a broad network. It has emerged as the key technology that unleashes the potency of Big Data, Internet of Things, Mobile and Web Applications, and other related technologies; but it also comes with its challenges - such as governance, security, and privacy. This paper is focused on the security and privacy challenges of cloud computing with specific reference to user authentication and access management for cloud SaaS applications. The suggested model uses a framework that harnesses the stateless and secure nature of JWT for client authentication and session management. Furthermore, authorized access to protected cloud SaaS resources have been efficiently managed. Accordingly, a Policy Match Gate (PMG) component and a Policy Activity Monitor (PAM) component have been introduced. In addition, other subcomponents such as a Policy Validation Unit (PVU) and a Policy Proxy DB (PPDB) have also been established for optimized service delivery. A theoretical analysis of the proposed model portrays a system that is secure, lightweight and highly scalable for improved cloud resource security and management.

Due to the globalization of Integrated Circuit (IC) design in the semiconductor industry and the outsourcing of chip manufacturing, third Party Intellectual Properties (3PIPs) become vulnerable to IP piracy, reverse engineering, counterfeit IC, and hardware trojans. A designer has to employ a strong technique to thwart such attacks, e.g. using Strong Logic Locking method [1]. But, such technique cannot be used to protect some circuits since the inserted key-gates rely on the topology of the circuit. Also, it requires higher power, delay, and area overheads compared to other techniques. In this paper, we present the use of spintronic devices to help protect ICs with less performance overhead. We then evaluate the proposed design based on security metric and performance overhead. One of the best spintronic device candidates is the All Spin Logic due to its unique properties: small area, no spin-charge signal conversion, and its compatibility with conventional CMOS technology.

This paper introduces a new efficient algorithm for computing Grobner-bases named M4GB. Like Faugere's algorithm F4 it is an extension of Buchberger's algorithm that describes: how to store already computed (tail-)reduced multiples of basis polynomials to prevent redundant work in the reduction step; and how to exploit efficient linear algebra for the reduction step. In comparison to F4 it removes further redundant work in the processing of reducible monomials. Furthermore, instead of translating the reduction of many critical pairs into the row reduction of some large matrix, our algorithm is described more natively and is efficient while processing critical pairs one by one. This feature implies that typically M4GB has to process fewer critical pairs than F4, and reduces the time and data complexity 'staircase' related to the increasing degree of regularity for a sequence of problems one observes for F4. To achieve high efficiency, M4GB has been designed specifically to operate only on tail-reduced polynomials, i.e., polynomials of which all terms except the leading term are non-reducible. This allows it to perform full-reduction directly in the computation of a term polynomial multiplication, where all computations are done over coefficient vectors over the non-reducible monomials. We have implemented a version of our new algorithm tailored for dense overdefined polynomial systems as a proof of concept and made our source code publicly available. We have made a comparison of our implementation against the implementations of FGBlib, Magma and OpenF4 on various dense Fukuoka MQ challenge problems that we were able to compute in reasonable time and memory. We observed that M4GB uses the least total CPU time and the least memory of all these implementations for those MQ problems, often by a significant factor. In the Fukuoka MQ challenges, the starting challenges of Type V and Type VI have 16 equations which was chosen based on an extrapolated computational runtime of more than a month using Magma. M4GB allowed us to set new records for these Fukuoka MQ challenges breaking Type V (F28) up to 18 equations and Type VI (F31) up to 19 equations, each can be computed within up to 11 days on our dual Xeon system.

This paper presents a method to extract important byte sequences in malware samples by application of convolutional neural network (CNN) to images converted from binary data. This method, by combining a technique called the attention mechanism into CNN, enables calculation of an "attention map," which shows regions having higher importance for classification in the image. The extracted region with higher importance can provide useful information for human analysts who investigate the functionalities of unknown malware samples. Results of our evaluation experiment using malware dataset show that the proposed method provides higher classification accuracy than a conventional method. Furthermore, analysis of malware samples based on the calculated attention map confirmed that the extracted sequences provide useful information for manual analysis.

There are currently few methods that can be applied to malware classification problems which don't require domain knowledge to apply. In this work, we develop our new SHWeL feature vector representation, by extending the recently proposed Lempel-Ziv Jaccard Distance. These SHWeL vectors improve upon LZJD's accuracy, outperform byte n-grams, and allow us to build efficient algorithms for both training (a weakness of byte n-grams) and inference (a weakness of LZJD). Furthermore, our new SHWeL method also allows us to directly tackle the class imbalance problem, which is common for malware-related tasks. Compared to existing methods like SMOTE, SHWeL provides significantly improved accuracy while reducing algorithmic complexity to O(N). Because our approach is developed without the use of domain knowledge, it can be easily re-applied to any new domain where there is a need to classify byte sequences.

The application of mobile Wireless Sensor Networks (WSNs) with a big amount of participants poses many challenges. For instance, high transmission loss rates which are caused i.a. by collisions might occur. Additionally, WSNs frequently operate under harsh conditions, where a high probability of link or node failures is inherently given. This leads to reliable data maintenance being a key issue. Existing approaches which were developed to keep data dependably in WSNs often either perform well in highly dynamic or in completely static scenarios, or require complex calculations. Herein, we present the Network Coding based Multicast Growth Codes (MCGC), which represent a solution for reliable data maintenance in large-scale WSNs. MCGC are able to tolerate high fault rates and reconstruct more originally collected data in a shorter period of time than compared existing approaches. Simulation results show performance improvements of up to 75% in comparison to Growth Codes (GC). These results are achieved independently of the systems' dynamics and despite of high fault probabilities.

We report on our research on proving the security of multi-party cryptographic protocols using the EASYCRYPT proof assistant. We work in the computational model using the sequence of games approach, and define honest-butcurious (semi-honest) security using a variation of the real/ideal paradigm in which, for each protocol party, an adversary chooses protocol inputs in an attempt to distinguish the party's real and ideal games. Our proofs are information-theoretic, instead of being based on complexity theory and computational assumptions. We employ oracles (e.g., random oracles for hashing) whose encapsulated states depend on dynamically-made, nonprogrammable random choices. By limiting an adversary's oracle use, one may obtain concrete upper bounds on the distances between a party's real and ideal games that are expressed in terms of game parameters. Furthermore, our proofs work for adaptive adversaries, ones that, when choosing the value of a protocol input, may condition this choice on their current protocol view and oracle knowledge. We provide an analysis in EASYCRYPT of a three party private count retrieval protocol. We emphasize the lessons learned from completing this proof.

Driven by CA compromises and the risk of man-in-the-middle attacks, new security features have been added to TLS, HTTPS, and the web PKI over the past five years. These include Certificate Transparency (CT), for making the CA system auditable; HSTS and HPKP headers, to harden the HTTPS posture of a domain; the DNS-based extensions CAA and TLSA, for control over certificate issuance and pinning; and SCSV, for protocol downgrade protection. This paper presents the first large scale investigation of these improvements to the HTTPS ecosystem, explicitly accounting for their combined usage. In addition to collecting passive measurements at the Internet uplinks of large University networks on three continents, we perform the largest domain-based active Internet scan to date, covering 193M domains. Furthermore, we track the long-term deployment history of new TLS security features by leveraging passive observations dating back to 2012. We find that while deployment of new security features has picked up in general, only SCSV (49M domains) and CT (7M domains) have gained enough momentum to improve the overall security of HTTPS. Features with higher complexity, such as HPKP, are deployed scarcely and often incorrectly. Our empirical findings are placed in the context of risk, deployment effort, and benefit of these new technologies, and actionable steps for improvement are proposed. We cross-correlate use of features and find some techniques with significant correlation in deployment. We support reproducible research and publicly release data and code.

Two emerging architectural paradigms, i.e., Software Defined Networking (SDN) and Network Function Virtualization (NFV), enable the deployment and management of Service Function Chains (SFCs). A SFC is an ordered sequence of abstract Service Functions (SFs), e.g., firewalls, VPN-gateways, traffic monitors, that packets have to traverse in the route from source to destination. While this appealing solution offers significant advantages in terms of flexibility, it also introduces new challenges such as the correct configuration and ordering of SFs in the chain to satisfy overall security requirements. This paper presents a formal model conceived to enable the verification of correct policy enforcements in SFCs. Software tools based on the model can then be designed to cope with unwanted network behaviors (e.g., security flaws) deriving from incorrect interactions of SFs of the same SFC. 

Insider threats can cause immense damage to organizations of different types, including government, corporate, and non-profit organizations. Being an insider, however, does not necessarily equate to being a threat. Effectively identifying valid threats, and assessing the type of threat an insider presents, remain difficult challenges. In this work, we propose a novel breakdown of eight insider threat types, identified by using three insider traits: predictability, susceptibility, and awareness. In addition to presenting this framework for insider threat types, we implement a computational model to demonstrate the viability of our framework with synthetic scenarios devised after reviewing real world insider threat case studies. The results yield useful insights into how further investigation might proceed to reveal how best to gauge predictability, susceptibility, and awareness, and precisely how they relate to the eight insider types.

Conventional intrusion detection systems for smart grid communications rely heavily on static based attack detection techniques. In essence, signatures created from historical data are compared to incoming network traffic to identify abnormalities. In the case of attacks where no historical data exists, static based approaches become ineffective thus relinquishing system resilience and stability. Moving target defense (MTD) has shown to be effective in discouraging attackers by introducing system entropy to increase exploit costs. Increase in exploit cost leads to a decrease in profitability for an attacker. In this paper, a Moving Target Defense Intrusion Detection System (MTDIDS) is proposed for smart grid IPv6 based advanced metering infrastructure. The advantage of MTDIDS is the ability to detect anomalies across moving targets by means of planar keys thereupon increasing detection rate. Evaluation of MTDIDS was carried out in a smart grid advanced metering infrastructure simulated in MATLAB.

The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc. Existing approaches rely on approximate graph-matching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task. To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding, i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions. We implement a prototype called Gemini. Our extensive evaluation shows that Gemini outperforms the state-of-the-art approaches by large margins with respect to similarity detection accuracy. Further, Gemini can speed up prior art's embedding generation time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours. Our real world case studies demonstrate that Gemini can identify significantly more vulnerable firmware images than the state-of-the-art, i.e., Genius. Our research showcases a successful application of deep learning on computer security problems.

Establishing trust relationships between routing nodes represents a vital security requirement to establish reliable routing processes that exclude infected or selfish nodes. In this paper, we propose a new security scheme for the Internet of things and mainly for the RPL (Routing Protocol for Low-power and Lossy Networks) called: Metric-based RPL Trustworthiness Scheme (MRTS). The primary aim is to enhance RPL security and deal with the trust inference problem. MRTS addresses trust issue during the construction and maintenance of routing paths from each node to the BR (Border Router). To handle this issue, we extend DIO (DODAG Information Object) message by introducing a new trust-based metric ERNT (Extended RPL Node Trustworthiness) and a new Objective Function TOF (Trust Objective Function). In fact, ERNT represents the trust values for each node within the network, and TOF demonstrates how ERNT is mapped to path cost. In MRTS all nodes collaborate to calculate ERNT by taking into account nodes' behavior including selfishness, energy, and honesty components. We implemented our scheme by extending the distributed Bellman-Ford algorithm. Evaluation results demonstrated that the new scheme improves the security of RPL.

For the increasing use of internet, it is equally important to protect the intellectual property. And for the protection of copyright, a blind digital watermark algorithm with SVD and OSELM in the IWT domain has been proposed. During the embedding process, SVD has been applied to the coefficient blocks to get the singular values in the IWT domain. Singular values are modulated to embed the watermark in the host image. Online sequential extreme learning machine is trained to learn the relationship between the original coefficient and the corresponding watermarked version. During the extraction process, this trained OSELM is used to extract the embedded watermark logo blindly as no original host image is required during this process. The watermarked image is altered using various attacks like blurring, noise, sharpening, rotation and cropping. The experimental results show that the proposed watermarking scheme is robust against various attacks. The extracted watermark has very much similarity with the original watermark and works good to prove the ownership.

The article issue is the enterprise information protection within the internet of things concept. The aim of research is to develop arrangements set to ensure secure enterprise IPv6 network operating. The object of research is the enterprise IPv6 network. The subject of research is modern switching equipment as a tool to ensure network protection. The research task is to prioritize functioning of switches in production and corporation enterprise networks, to develop a network host protection algorithm, to test the developed algorithm on the Cisco Packet Tracer 7 software emulator. The result of research is the proposed approach to IPv6-network security based on analysis of modern switches functionality, developed and tested enterprise network host protection algorithm under IPv6-protocol with an automated network SLAAC-configuration control, a set of arrangements for resisting default enterprise gateway attacks, using ACL, VLAN, SEND, RA Guard security technology, which allows creating sufficiently high level of networks security.

Existing access control mechanisms are based on the concept of identity enrolment and recognition and assume that recognized identity is a synonym to ethical actions, yet statistics over the years show that the most severe security breaches are the results of trusted, identified, and legitimate users who turned into malicious insiders. Insider threat damages vary from intellectual property loss and fraud to information technology sabotage. As insider threat incidents evolve, there exist demands for a nonidentity-based authentication measure that rejects access to authorized individuals who have mal-intents of access. In this paper, we study the possibility of using the user's intention as an access control measure using the involuntary electroencephalogram reactions toward visual stimuli. We propose intent-based access control (IBAC) that detects the intentions of access based on the existence of knowledge about an intention. IBAC takes advantage of the robustness of the concealed information test to assess access risk. We use the intent and intent motivation level to compute the access risk. Based on the calculated risk and risk accepted threshold, the system makes the decision whether to grant or deny access requests. We assessed the model using experiments on 30 participants that proved the robustness of the proposed solution.