Biblio

We propose using memristor-based TCAMs (Ternary Content Addressable Memory) to accelerate Regular Expression (RegEx) matching. RegEx matching is a key function in network security, where deep packet inspection finds and filters out malicious actors. However, RegEx matching latency and power can be incredibly high and current proposals are challenged to perform wire-speed matching for large scale rulesets. Our approach dramatically decreases RegEx matching operating power, provides high throughput, and the use of mTCAMs enables novel compression techniques to expand ruleset sizes and allows future exploitation of the multi-state (analog) capabilities of memristors. We fabricated and demonstrated nanoscale memristor TCAM cells. SPICE simulations investigate mTCAM performance at scale and a mTCAM power model at 22nm demonstrates 0.2 fJ/bit/search energy for a 36x400 mTCAM. We further propose a tiled architecture which implements a Snort ruleset and assess the application performance. Compared to a state-of-the-art FPGA approach (2 Gbps,\textbackslashtextasciitilde1W), we show x4 throughput (8 Gbps) at 60% the power (0.62W) before applying standard TCAM power-saving techniques. Our performance comparison improves further when striding (searching multiple characters) is considered, resulting in 47.2 Gbps at 1.3W for our approach compared to 3.9 Gbps at 630mW for the strided FPGA NFA, demonstrating a promising path to wire-speed RegEx matching on large scale rulesets.

Software insecurity is being identified as one of the leading causes of security breaches. In this paper, we revisited one of the strategies in solving software insecurity, which is the use of software quality metrics. We utilized a multilayer deep feedforward network in examining whether there is a combination of metrics that can predict the appearance of security-related bugs. We also applied the traditional machine learning algorithms such as decision tree, random forest, naïve bayes, and support vector machines and compared the results with that of the Deep Learning technique. The results have successfully demonstrated that it was possible to develop an effective predictive model to forecast software insecurity based on the software metrics and using Deep Learning. All the models generated have shown an accuracy of more than sixty percent with Deep Learning leading the list. This finding proved that utilizing Deep Learning methods and a combination of software metrics can be tapped to create a better forecasting model thereby aiding software developers in predicting security bugs.

Trustworthiness is a paramount concern for users and customers in the selection of a software solution, specially in the context of complex and dynamic environments, such as Cloud and IoT. However, assessing and benchmarking trustworthiness (worthiness of software for being trusted) is a challenging task, mainly due to the variety of application scenarios (e.g., businesscritical, safety-critical), the large number of determinative quality attributes (e.g., security, performance), and last, but foremost, due to the subjective notion of trust and trustworthiness. In this paper, we present trustworthiness as a measurable notion in relative terms based on security attributes and propose an approach for the assessment and benchmarking of software. The main goal is to build a trustworthiness assessment model based on software metrics (e.g., Cyclomatic Complexity, CountLine, CBO) that can be used as indicators of software security. To demonstrate the proposed approach, we assessed and ranked several files and functions of the Mozilla Firefox project based on their trustworthiness score and conducted a survey among several software security experts in order to validate the obtained rank. Results show that our approach is able to provide a sound ranking of the benchmarked software.

Nowadays, honeypots are a key tool to attract attackers and study their activity. They help us in the tasks of evaluating attacker's behaviour, discovering new types of attacks, and collecting information and statistics associated with them. However, the gathered data cannot be directly interpreted, but must be analyzed to obtain useful information. In this paper, we present a SSH honeypot-based system designed to simulate a vulnerable server. Thus, we propose an approach for the classification of metrics from the data collected by the honeypot along 19 months.

Given the centralized architecture of cloud computing, there is a genuine concern about its ability to adequately cope with the demands of connecting devices which are sharply increasing in number and capacity. This has led to the emergence of edge computing technologies, including but not limited to mobile edge-clouds. As a branch of Peer-to-Peer (P2P) networks, mobile edge-clouds inherits disturbing security concerns which have not been adequately addressed in previous methods. P2P security systems have featured many trust-based methods owing to their suitability and cost advantage, but these approaches still lack in a number of ways. They mostly focus on protecting client nodes from malicious service providers, but downplay the security of service provider nodes, thereby creating potential loopholes for bandwidth attack. Similarly, trust bootstrapping is often via default scores, or based on heuristics that does not reflect the identity of a newcomer. This work has patched these inherent loopholes and improved fairness among participating peers. The use cases of mobile edge-clouds have been particularly considered and a scalable reputation based security mechanism was derived to suit them. BitTorrent protocol was modified to form a suitable test bed, using Peersim simulator. The proposed method was compared to some related methods in the literature through detailed simulations. Results show that the new method can foster trust and significantly improve network security, in comparison to previous similar systems.

Security1 design of mobile apps is very important, and it is also important that researchers consider and disseminate the continually changing requirements. For mobile application i.e. a software program that runs on a mobile phone, its design, development and management need to consider security impact. In particular, because of mobile app is running on online devices, cyber security defense is required. In this chapter, mobile app security is discussed from the initial planning and design stage to its maintenance after its launch.

A framework of Software-Defined Networking (SDN) provides a centralized and integrated method to manage and control modern optical networks. Unfortunately, the centralized and programmable structure of SDN introduces several new security threats, which may allow an adversary to take over the entire operation of the network. In this paper, we investigate the potential security threats of SDN over optical networks and propose a mutual authentication and a fine-grained access control mechanism, which are essential to avoid an unauthorized access to the network. The proposed schemes are based only on cryptographic hash functions and do not require an installation of the complicated cryptographic library such as SSL. Unlike conventional authentication and access control schemes, the proposed schemes are flexible, compact and, in addition, are resistant to quantum computer attacks, which may become critical in the near future.

Software verification has been well applied in safety critical areas and has shown the ability to provide better quality assurance for modern software. However, as lines of code and complexity of software systems increase, the scalability of verification becomes a challenge. In this paper, we present an automatic software verification framework TSV to address the scalability issues: (i) the extended structural abstraction and property-guided program slicing to solve large-scale program verification problem, saving time and memory without losing accuracy; (ii) automatically select different verification methods according to the program and property context to improve the verification efficiency. For evaluation, we compare TSV's different configurations with existing C program verifiers based on open benchmarks. We found that TSV with auto-selection performs better than with bounded model checking only or with extended structural abstraction only. Compared to existing tools such as CMBC and CPAChecker, it acquires 10%-20% improvement of accuracy and 50%-90% improvement of memory consumption.

Many test case prioritization criteria have been proposed for speeding up fault detection. Among them, similarity-based approaches give priority to the test cases that are the most dissimilar from those already selected. However, the proposed criteria do not scale up to handle the many thousands or even some millions test suite sizes of modern industrial systems and simple heuristics are used instead. We introduce the FAST family of test case prioritization techniques that radically changes this landscape by borrowing algorithms commonly exploited in the big data domain to find similar items. FAST techniques provide scalable similarity-based test case prioritization in both white-box and black-box fashion. The results from experimentation on real world C and Java subjects show that the fastest members of the family outperform other black-box approaches in efficiency with no significant impact on effectiveness, and also outperform white-box approaches, including greedy ones, if preparation time is not counted. A simulation study of scalability shows that one FAST technique can prioritize a million test cases in less than 20 minutes.

We present the design and implementation of p4v, a practical tool for verifying data planes described using the P4 programming language. The design of p4v is based on classic verification techniques but adds several key innovations including a novel mechanism for incorporating assumptions about the control plane and domain-specific optimizations which are needed to scale to large programs. We present case studies showing that p4v verifies important properties and finds bugs in real-world programs. We conduct experiments to quantify the scalability of p4v on a wide range of additional examples. We show that with just a few hundred lines of control-plane annotations, p4v is able to verify critical safety properties for switch.p4, a program that implements the functionality of on a modern data center switch, in under three minutes.

Smart grid technology is the core technology for the next-generation power grid system with enhanced energy efficiency through decision-making communication between suppliers and consumers enabled by integrating the IoT into the existing grid. This open architecture allowing bilateral information exchange makes it vulnerable to various types of cyberattack. APT attacks, one of the most common cyberattacks, are highly tricky and sophisticated attacks that can circumvent the existing detection technology and attack the targeted system after a certain latent period after intrusion. This paper proposes an ontology-based attack detection system capable of early detection of and response to APT attacks by analyzing their attacking patterns.

In this paper, we propose a new randomized response algorithm that can achieve differential-privacy and utility guarantees for consumer's behaviors, and process a batch of data at each time. Firstly, differing from traditional differential private approach-es, we add randomized response noise into the behavior signa-tures matrix to achieve an acceptable utility-privacy tradeoff. Secondly, a behavior signature modeling method based on sparse coding is proposed. After some lightweight trainings us-ing the energy consumption data, the dictionary will be associat-ed with the behavior characteristics of the electric appliances. At last, through the experimental results verification, we find that our Algorithm can preserve consumer's privacy without comprising utility.

The advent of smart grids offers us the opportunity to better manage the electricity grids. One of the most interesting challenges in the modern grids is the consumer demand management. Indeed, the development in Information and Communication Technologies (ICTs) encourages the development of demand-side management systems. In this paper, we propose a distributed energy demand scheduling approach that uses minimal interactions between consumers to optimize the energy demand. We formulate the consumption scheduling as a constrained optimization problem and use game theory to solve this problem. On one hand, the proposed approach aims to reduce the total energy cost of a building's consumers. This imposes the cooperation between all the consumers to achieve the collective goal. On the other hand, the privacy of each user must be protected, which means that our distributed approach must operate with a minimal information exchange. The performance evaluation shows that the proposed approach reduces the total energy cost, each consumer's individual cost, as well as the peak to average ratio.

Modern malware applies a rich arsenal of evasion techniques to render dynamic analysis ineffective. In turn, dynamic analysis tools take great pains to hide themselves from malware; typically this entails trying to be as faithful as possible to the behavior of a real run. We present a novel approach to malware analysis that turns this idea on its head, using an extreme abstraction of the operating system that intentionally strays from real behavior. The key insight is that the presence of malicious behavior is sufficient evidence of malicious intent, even if the path taken is not one that could occur during a real run of the sample. By exploring multiple paths in a system that only approximates the behavior of a real system, we can discover behavior that would often be hard to elicit otherwise. We aggregate features from multiple paths and use a funnel-like configuration of machine learning classifiers to achieve high accuracy without incurring too much of a performance penalty. We describe our system, TAMALES (The Abstract Malware Analysis LEarning System), in detail and present machine learning results using a 330K sample set showing an FPR (False Positive Rate) of 0.10% with a TPR (True Positive Rate) of 99.11%, demonstrating that extreme abstraction can be extraordinarily effective in providing data that allows a classifier to accurately detect malware.

Nowadays, the digitization of the world is under a serious threat due to the emergence of various new and complex malware every day. Due to this, the traditional signature-based methods for detection of malware effectively become an obsolete method. The efficiency of the machine learning techniques in context to the detection of malwares has been proved by state-of-the-art research works. In this paper, we have proposed a framework to detect and classify different files (e.g., exe, pdf, php, etc.) as benign and malicious using two level classifier namely, Macro (for detection of malware) and Micro (for classification of malware files as a Trojan, Spyware, Ad-ware, etc.). Our solution uses Cuckoo Sandbox for generating static and dynamic analysis report by executing the sample files in the virtual environment. In addition, a novel feature extraction module has been developed which functions based on static, behavioral and network analysis using the reports generated by the Cuckoo Sandbox. Weka Framework is used to develop machine learning models by using training datasets. The experimental results using the proposed framework shows high detection rate and high classification rate using different machine learning algorithms

The ever-increasing number of malware samples demands for automated tools that aid the analysts in the reverse engineering of complex malicious binaries. Frequently, malware communicates over an encrypted channel with external network resources under the control of malicious actors, such as Command and Control servers that control the botnet of infected machines. Hence, a key aspect in malware analysis is uncovering and understanding the semantics of network communications. In this paper we present SysTaint, a semi-automated tool that runs malware samples in a controlled environment and analyzes their execution to support the analyst in identifying the functions involved in the communication and the exchanged data. Our evaluation on four banking Trojan samples from different families shows that SysTaint is able to handle and inspect encrypted network communications, obtaining useful information on the data being sent and received, on how each sample processes this data, and on the inner portions of code that deal with the data processing.

Virtualized infrastructure is a key capability of modern enterprise data centers and cloud computing, enabling a more agile and dynamic IT infrastructure with fast IT provisioning, simplified, automated management, and flexible resource allocation to handle a broad set of workloads. However, at the same time, virtualization introduces new challenges, since securing virtual servers is more difficult than physical machines. HyTrust Inc. has developed an innovative security solution, called HyTrust Cloud Control (HTCC), to mitigate risks associated with virtualization and cloud technologies. HTCC is a virtual appliance deployed as a transparent proxy in front of a VMware-based virtualized environment. Since HTCC serves as a gateway to a customer virtualized environment, it is important to carefully assess its performance and scalability as well as provide its accurate resource sizing. In this work, we introduce a novel approach for accomplishing this goal. First, we describe a special framework, based on a nested virtualization technique, which enables the creation and deployment of a large scale virtualized environment (with 30,000 VMs) using a limited number of physical servers (4 servers in our experiments). Second, we introduce a design and implementation of a novel, extensible benchmark, called HT-vmbench, that allows to mimic the session-based activities of different system administrators and users in virtualized environments. The benchmark is implemented using VMware Web Service SDK. By executing HT-vmbench in the emulated large-scale virtualized environments, we can support an efficient performance assessment of management and security solutions (such as HTCC), their overhead, and provide capacity planning rules and resource sizing recommendations.

In this letter, we proposed a novel scheme for the realization of scalable and flexible semi-quantum secret sharing between a boss and multiple dynamic agent groups. In our scheme, the boss Alice can not only distribute her secret messages to multiple users, but also can dynamically adjust the number of users and user groups based on the actual situation. Furthermore, security analysis demonstrates that our protocol is secure against both external attack and participant attack. Compared with previous schemes, our protocol is more flexible and practical. In addition, since our protocol involving only single qubit measurement that greatly weakens the hardware requirements of each user.

Motivated by networked systems in which the functionality of the network depends on vertices in the network being within a bounded distance T of each other, we study the length-bounded multicut problem: given a set of pairs, find a minimum-size set of edges whose removal ensures the distance between each pair exceeds T . We introduce the first algorithms for this problem capable of scaling to massive networks with billions of edges and nodes: three highly scalable algorithms with worst-case performance ratios. Furthermore, one of our algorithms is fully dynamic, capable of updating its solution upon incremental vertex / edge additions or removals from the network while maintaining its performance ratio. Finally, we show that unless NP ⊆ BPP, there is no polynomial-time, approximation algorithm with performance ratio better than Omega (T), which matches the ratio of our dynamic algorithm up to a constant factor.

Many real-world IoT solutions have to be implemented in a federated environment, which are environments where many different administrative organizations are involved in different parts of the solution. Smarter Cities, Federated Governance, International Trade and Military Coalition Operations are examples of federated environments. As end devices become more capable and intelligent, learning from their environment, and adapting on their own, they expose new types of security vulnerabilities and present an increased attack surface. A distributed AI approach can help mitigate many of the security problems that one may encounter in such federated environments. In this paper, we outline some of the scenarios in which we need to rethink security issues as devices become more intelligent, and discuss how distributed AI techniques can be used to reduce the security exposures in such environments.

Lately, we are facing the Malware crisis due to various types of malware or malicious programs or scripts available in the huge virtual world - the Internet. But, what is malware? Malware can be a malicious software or a program or a script which can be harmful to the user's computer. These malicious programs can perform a variety of functions, including stealing, encrypting or deleting sensitive data, altering or hijacking core computing functions and monitoring users' computer activity without their permission. There are various entry points for these programs and scripts in the user environment, but only one way to remove them is to find them and kick them out of the system which isn't an easy job as these small piece of script or code can be anywhere in the user system. This paper involves the understanding of different types of malware and how we will use Machine Learning to detect these malwares.

The latent behavior of an information system that can exhibit extreme events, such as system faults or cyber-attacks, is complex. Recently, the invariant network has shown to be a powerful way of characterizing complex system behaviors. Structures and evolutions of the invariance network, in particular, the vanishing correlations, can shed light on identifying causal anomalies and performing system diagnosis. However, due to the dynamic and complex nature of real-world information systems, learning a reliable invariant network in a new environment often requires continuous collecting and analyzing the system surveillance data for several weeks or even months. Although the invariant networks learned from old environments have some common entities and entity relationships, these networks cannot be directly borrowed for the new environment due to the domain variety problem. To avoid the prohibitive time and resource consuming network building process, we propose TINET, a knowledge transfer based model for accelerating invariant network construction. In particular, we first propose an entity estimation model to estimate the probability of each source domain entity that can be included in the final invariant network of the target domain. Then, we propose a dependency construction model for constructing the unbiased dependency relationships by solving a two-constraint optimization problem. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness and efficiency of TINET. We also apply TINET to a real enterprise security system for intrusion detection. TINET achieves superior detection performance at least 20 days lead-lag time in advance with more than 75% accuracy.

Community detection in complex networks is a fundamental problem that attracts much attention across various disciplines. Previous studies have been mostly focusing on external connections between nodes (i.e., topology structure) in the network whereas largely ignoring internal intricacies (i.e., local behavior) of each node. A pair of nodes without any interaction can still share similar internal behaviors. For example, in an enterprise information network, compromised computers controlled by the same intruder often demonstrate similar abnormal behaviors even if they do not connect with each other. In this paper, we study the problem of community detection in enterprise information networks, where large-scale internal events and external events coexist on each host. The discovered host communities, capturing behavioral affinity, can benefit many comparative analysis tasks such as host anomaly assessment. In particular, we propose a novel community detection framework to identify behavior-based host communities in enterprise information networks, purely based on large-scale heterogeneous event data. We continue proposing an efficient method for assessing host's anomaly level by leveraging the detected host communities. Experimental results on enterprise networks demonstrate the effectiveness of our model.

Network attacks, especially DoS and DDoS attacks, are a significant threat for all providers of services or infrastructure. The biggest attacks can paralyze even large-scale infrastructures of worldwide companies. Attack mitigation is a complex issue studied by many researchers and security companies. While several approaches were proposed, there is still space for improvement. This paper proposes to augment existing mitigation heuristic with knowledge of reputation score of network entities. The aim is to find a way to mitigate malicious traffic present in DDoS amplification attacks with minimal disruption to communication of legitimate traffic.

For the security of mobile ad-hoc networks (MANETs), a group of wireless mobile nodes needs to cooperate by forwarding packets, to implement an intrusion detection system (IDS). Some of the current IDS implementations in a clustered MANET have designed mobile nodes to wait until the cluster head is elected before scanning the network and thus nodes may be, unfortunately, exposed to several control packet attacks by which nodes identify falsified routes to reach other nodes. In order to detect control packet attacks such as route falsification, we design a route cache sharing mechanism for a non-clustered network where all one-hop routing data are collected by each node for a cooperative host-based detection. The cooperative host-based detection system uses a Support Vector Machine classifier and achieves a detection rate of around 95%. By successfully detecting the route falsification attacks, nodes are given the capability to avoid other attacks such as black-hole and gray-hole, which are in many cases a result of a successful route falsification attack.