Biblio

Byte-addressable non-volatile memory technology is emerging as an alternative for DRAM for main memory. This new Non-Volatile Main Memory (NVMM) allows programmers to store important data in data structures in memory instead of serializing it to the file system, thereby providing a substantial performance boost. However, modern systems reorder memory operations and utilize volatile caches for better performance, making it difficult to ensure a consistent state in NVMM. Intel recently announced a new set of persistence instructions, clflushopt, clwb, and pcommit. These new instructions make it possible to implement fail-safe code on NVMM, but few workloads have been written or characterized using these new instructions. In this work, we describe how these instructions work and how they can be used to implement write-ahead logging based transactions. We implement several common data structures and kernels and evaluate the performance overhead incurred over traditional non-persistent implementations. In particular, we find that persistence instructions occur in clusters along with expensive fence operations, they have long latency, and they add a significant execution time overhead, on average by 20.3% over code with logging but without fence instructions to order persists. To deal with this overhead and alleviate the performance bottleneck, we propose to speculate past long latency persistency operations using checkpoint-based processing. Our speculative persistence architecture reduces the execution time overheads to only 3.6%.

SQL injection attack (SQLIA) pose a serious security threat to the database driven web applications. This kind of attack gives attackers easily access to the application's underlying database and to the potentially sensitive information these databases contain. A hacker through specifically designed input, can access content of the database that cannot otherwise be able to do so. This is usually done by altering SQL statements that are used within web applications. Due to importance of security of web applications, researchers have studied SQLIA detection and prevention extensively and have developed various methods. In this research, after reviewing the existing research in this field, we present a new hybrid method to reduce the vulnerability of the web applications. Our method is specifically designed to detect and prevent SQLIA. Our proposed method is consists of three phases namely, the database design, implementation, and at the common gateway interface (CGI). Details of our approach along with its pros and cons are discussed in detail.

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.

Traditionally, distributed computing concentrates on computation understood at the level of information exchange and sets aside human and organizational concerns as largely to be handled in an ad hoc manner.  Increasingly, however, distributed applications involve multiple loci of autonomy.  Research in multiagent systems (MAS) addresses autonomy by drawing on concepts and techniques from artificial intelligence.  However, MAS research generally lacks an adequate understanding of modern distributed computing.

In this Blue Sky paper, we envision decentralized multiagent systems as a way to place decentralized intelligence in distributed computing, specifically, by supporting computation at the level of social meanings.  We motivate our proposals for research in the context of the Internet of Things (IoT), which has become a major thrust in distributed computing.  From the IoT's representative applications, we abstract out the major challenges of relevance to decentralized intelligence.  These include the heterogeneity of IoT components; asynchronous and delay-tolerant communication and decoupled enactment; and multiple stakeholders with subtle requirements for governance, incorporating resource usage, cooperation, and privacy.  The IoT yields high-impact problems that require solutions that go beyond traditional ways of thinking.

We conclude with highlights of some possible research directions in decentralized MAS, including programming models; interaction-oriented software engineering; and what we term enlightened governance.

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.

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.

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.

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.

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.

Trustworthy and safe operation of the power grid critical infrastructures relies on secure execution of low-level substation controller devices such as programmable logic controllers (PLCs). Currently, there are very few security protection solutions deployed on these devices to ensure provenance control: to execute controller code on the device that is developed by trusted parties and complies with safety/security policies that are defined by the code developer as well as the power grid operators. Resource-limited PLC controllers have been becoming increasingly popular among not only legitimate system operators, but also malicious adversaries such as the most recent Stuxnet and BlackEnergy malware that caused various damages such as unauthorized infrastructural safety and integrity violations. We present PLCtrust, a domain-specific solution that deploys virtual micro security-perimeters, so-called capsules, and the corresponding device-level runtime power system-safety policy enforcement dynamically. PLCtrust makes use of data taint analysis to monitor and control data flow among the capsules based on data owner-defined policies. PLCtrust provides the operators with a transparent and lightweight solution to address various safety-critical data protection requirements. PLCtrust also provides the legitimate third-party controller code developers with a taint-aware programming interface to develop applications in compliance with the dynamic power system safety/security policies. Our experimental results on real-world settings show that PLCtrust is transparent to the end-users while ensuring the power grid safety maintenance with minimal performance overhead.

Cloud computing emerges as an endowment technological data for the longer term and increasing on one of the standards of utility computing is most likely claimed to symbolize a wholly new paradigm for viewing and getting access to computational assets. As a result of protection problem many purchasers hesitate in relocating their touchy data on the clouds, regardless of gigantic curiosity in cloud-based computing. Security is a tremendous hassle, considering the fact that so much of firms present a alluring goal for intruders and the particular considerations will pursue to lower the advancement of distributed computing if not located. Hence, this recent scan and perception is suitable to honeypot. Distributed Denial of Service (DDoS) is an assault that threats the availability of the cloud services. It's fundamental investigate the most important features of DDoS Defence procedures. This paper provides exact techniques that been carried out to the DDoS attack. These approaches are outlined in these paper and use of applied sciences for special kind of malfunctioning within the cloud.

Distributed Denial-of-Service (DDoS) attacks are increasing in frequency and volume on the Internet, and there is evidence that cyber-criminals are turning to Internet-of-Things (IoT) devices such as cameras and vending machines as easy launchpads for large-scale attacks. This paper quantifies the capability of consumer IoT devices to participate in reflective DDoS attacks. We first show that household devices can be exposed to Internet reflection even if they are secured behind home gateways. We then evaluate eight household devices available on the market today, including lightbulbs, webcams, and printers, and experimentally profile their reflective capability, amplification factor, duration, and intensity rate for TCP, SNMP, and SSDP based attacks. Lastly, we demonstrate reflection attacks in a real-world setting involving three IoT-equipped smart-homes, emphasising the imminent need to address this problem before it becomes widespread.

Ransomware is one of the most increasing malwares used by cyber-criminals in recent days. This type of malware uses cryptographic technology that encrypts a user's important files, folders makes the computer systems unusable, holds the decryption key and asks for the ransom from the victims for recovery. The recent ransomware families are very sophisticated and difficult to analyze & detect using static features only. On the other hand, latest crypto-ransomwares having sandboxing and IDS evading capabilities. So obviously, static or dynamic analysis of the ransomware alone cannot provide better solution. In this paper, we will present a Machine Learning based approach which will use integrated method, a combination of static and dynamic analysis to detect ransomware. The experimental test samples were taken from almost all ransomware families including the most recent ``WannaCry''. The results also suggest that combined analysis can detect ransomware with better accuracy compared to individual analysis approach. Since ransomware samples show some ``run-time'' and ``static code'' features, it also helps for the early detection of new and similar ransomware variants.

System-level development has been dominated by traditional programming languages such as C and C++ for decades. These languages are inherently unsafe regarding memory management. Even experienced developers make mistakes that open up security holes or compromise the safety properties of software. The Rust programming language is targeted at the systems domain and aims to eliminate memory-related programming errors by enforcing a strict memory model at the language and compiler level. Unfortunately, these compile-time guarantees no longer hold when a Rust program is linked against a library written in unsafe C, which is commonly required for functionality where an implementation in Rust is not yet available. In this paper, we present Sandcrust, an easy-to-use sand-boxing solution for isolating code and data of a C library in a separate process. This isolation protects the Rust-based main program from any memory corruption caused by bugs in the unsafe library, which would otherwise invalidate the memory safety guarantees of Rust. Sandcrust is based on the Rust macro system and requires no modification to the compiler or runtime, but only straightforward annotation of functions that call the library's API.

Software Defined Networking (SDN) presents a unique opportunity to manage and orchestrate cloud networks. The educational institutions, like many other industries face a lot of security threats. We have established an SDN enabled Demilitarized Zone (DMZ) — Science DMZ to serve as testbed for securing ASU Internet2 environment. Science DMZ allows researchers to conduct in-depth analysis of security attacks and take necessary countermeasures using SDN based command and control (C&C) center. Demo URL: https : //www.youtube.corn/watchlv = 8yo2lTNV 3r4.

Steganography is the science of hiding information to send secret messages using the carrier object known as stego object. Steganographic technology is based on three principles including security, robustness and capacity. In this paper, we present a digital image hidden by using the compressive sensing technology to increase security of stego image based on human visual system features. The results represent which our proposed method provides higher security in comparison with the other presented methods. Bit Correction Rate between original secret message and extracted message is used to show the accuracy of this method.

Artificial software diversity is an effective way to prevent software vulnerabilities and errors to be exploited in code-reuse attacks. This is achieved by lowering the individual probability of a successful attack to a level that makes the attack unfeasible. Unfortunately, the existing approaches are not applicable to safety-critical real-time systems as they induce unacceptable performance overheads, they violate safety and timing guarantees, or they assume hardware resources which are typically not available in embedded systems. To overcome these problems, we propose a safe diversity approach that preserves the timing properties of real-time processes by controlling its impact on the worst case execution time (WCET). Our main idea is to use block-level diversity with a large, but fixed set of movable instruction sequences, and to use static WCET analysis to identify non-critical areas of code where it can safely be split into more movable instruction sequences.

The Internet of Things (IoT) has bridged our physical world to the cyber world which allows us to achieve our desired lifestyle. However, service security is an essential part to ensure that the designed service is not compromised. In this paper, we proposed a security analysis for IoT services. We focus on the context of detecting malicious operation from an event log of the designed IoT services. We utilized Petri nets with data to model IoT service which is logically correct. Then, we check the trace from an event log by tracking the captured process and data. Finally, we illustrated the approach with a smart home service and showed the effectiveness of our approach.

Classifying users according to their behaviors is a complex problem due to the high-volume of data and the unclear association between distinct data points. Although over the past years behavioral researches has mainly focused on Massive Multiplayer Online Role Playing Games (MMORPG), such as World of Warcraft (WoW), which has predefined player classes, there has been little applied to Open World Sandbox Games (OWSG). Some OWSG do not have player classes or structured linear gameplay mechanics, as freedom is given to the player to freely wander and interact with the virtual world. This research focuses on identifying different play styles that exist within the non-structured gameplay sessions of OWSG. This paper uses the OWSG TUG as a case study and over a period of forty-five days, a database stored selected gameplay events happening on the research server. The study applied k-means clustering to this dataset and evaluated the resulting distinct behavioral profiles to classify player sessions on an open world sandbox game.

Internet eXchange Points (IXPs) play an ever-growing role in Internet inter-connection. To facilitate the exchange of routes amongst their members, IXPs provide Route Server (RS) services to dispatch the routes according to each member's peering policies. Nowadays, to make use of RSes, these policies must be disclosed to the IXP. This poses fundamental questions regarding the privacy guarantees of route-computation on confidential business information. Indeed, as evidenced by interaction with IXP administrators and a survey of network operators, this state of affairs raises privacy concerns among network administrators and even deters some networks from subscribing to RS services. We design Sixpack1, an RS service that leverages Secure Multi-Party Computation (SMPC) to keep peering policies confidential, while extending, the functionalities of today's RSes. As SMPC is notoriously heavy in terms of communication and computation, our design and implementation of Sixpack aims at moving computation outside of the SMPC without compromising the privacy guarantees. We assess the effectiveness and scalability of our system by evaluating a prototype implementation using traces of data from one of the largest IXPs in the world. Our evaluation results indicate that Sixpack can scale to support privacy-preserving route-computation, even at IXPs with many hundreds of member networks.

The past ten years has seen increasing calls to make security research more “scientific”. On the surface, most agree that this is desirable, given universal recognition of “science” as a positive force. However, we find that there is little clarity on what “scientific” means in the context of computer security research, or consensus on what a “Science of Security” should look like. We selectively review work in the history and philosophy of science and more recent work under the label “Science of Security”. We explore what has been done under the theme of relating science and security, put this in context with historical science, and offer observations and insights we hope may motivate further exploration and guidance. Among our findings are that practices on which the rest of science has reached consensus appear little used or recognized in security, and a pattern of methodological errors continues unaddressed.

Web-Based applications are becoming more increasingly technically complex and sophisticated. The very nature of their feature-rich design and their capability to collate, process, and disseminate information over the Internet or from within an intranet makes them a popular target for attack. According to Open Web Application Security Project (OWASP) Top Ten Cheat sheet-2017, SQL Injection Attack is at peak among online attacks. This can be attributed primarily to lack of awareness on software security. Developing effective SQL injection detection approaches has been a challenge in spite of extensive research in this area. In this paper, we propose a signature based SQL injection attack detection framework by integrating fingerprinting method and Pattern Matching to distinguish genuine SQL queries from malicious queries. Our framework monitors SQL queries to the database and compares them against a dataset of signatures from known SQL injection attacks. If the fingerprint method cannot determine the legitimacy of query alone, then the Aho Corasick algorithm is invoked to ascertain whether attack signatures appear in the queries. The initial experimental results of our framework indicate the approach can identify wide variety of SQL injection attacks with negligible impact on performance.