Biblio

Information systems are becoming more and more complex and closely linked. These systems are encountering an enormous amount of nefarious traffic while ensuring real - time connectivity. Therefore, a defense method needs to be in place. One of the commonly used tools for network security is intrusion detection systems (IDS). An IDS tries to identify fraudulent activity using predetermined signatures or pre-established user misbehavior while monitoring incoming traffic. Intrusion detection systems based on signature and behavior cannot detect new attacks and fall when small behavior deviations occur. Many researchers have proposed various approaches to intrusion detection using machine learning techniques as a new and promising tool to remedy this problem. In this paper, the authors present a combination of two machine learning methods, unsupervised clustering followed by a supervised classification framework as a Fast, highly scalable and precise packets classification system. This model's performance is assessed on the new proposed dataset by the Canadian Institute for Cyber security and the University of New Brunswick (CICIDS2017). The overall process was fast, showing high accuracy classification results.

Fingerprinting the malware by its behavioural signature has been an attractive approach for malware detection due to the homogeneity of dynamic execution patterns across different variants of similar families. Although previous researches show reasonably good performance in dynamic detection using machine learning techniques on a large corpus of training set, decisions must be undertaken based upon a scarce number of observable samples in many practical defence scenarios. This paper demonstrates the effectiveness of generative adversarial autoencoder for dynamic malware detection under outbreak situations where in most cases a single sample is available for training the machine learning algorithm to detect similar samples that are in the wild.

Logic locking, and Integrated Circuit (IC) Camouflaging, are techniques that try to hide the design of an IC from a malicious foundry or end-user by introducing ambiguity into the netlist of the circuit. While over the past decade an array of such techniques have been proposed, their security has been constantly challenged by algorithmic attacks. This may in part be due to a lack of formally defined notions of security in the first place, and hence a lack of security guarantees based on long-standing hardness assumptions. In this paper we take a formal approach. We define the problem of circuit locking (cL) as transforming an original circuit to a locked one which is ``unintelligable'' without a secret key (this can model camouflaging and split-manufacturing in addition to logic locking). We define several notions of security for cL under different adversary models. Using long standing results from computational learning theory we show the impossibility of exponentially approximation-resilient locking in the presence of an oracle for large classes of Boolean circuits. We then show how exact-recovery-resiliency and a more relaxed notion of security that we coin ``best-possible'' approximation-resiliency can be provably guaranteed with polynomial overhead. Our theoretical analysis directly results in stronger attacks and defenses which we demonstrate through experimental results on benchmark circuits.

The Named Data Network (NDN) is a promising network paradigm for content distribution based on caching. However, it may put consumer privacy at risk, as the adversary may identify the content, the name and the signature (namely a certificate) through side-channel timing responses from the cache of the routers. The adversary may identify the content name and the consumer node by distinguishing between cached and un- cached contents. In order to mitigate the timing attack, effective countermeasure methods have been proposed by other authors, such as random caching, random freshness, and probabilistic caching. In this work, we have implemented a timing attack scenario to evaluate the efficiency of these countermeasures and to demonstrate how the adversary can be detected. For this goal, a brute force timing attack scenario based on a real topology was developed, which is the first brute force attack model applied in NDN. Results show that the adversary nodes can be effectively distinguished from other legitimate consumers during the attack period. It is also proposed a multi-level mechanism to detect an adversary node. Through this approach, the content distribution performance can be mitigated against the attack.

At the time of more and more devices being connected to the internet, personal and sensitive information is going around the network more than ever. Thus, security and privacy regarding IoT communications, devices, and data are a concern due to the diversity of the devices and protocols used. Since traditional security mechanisms cannot always be adequate due to the heterogeneity and resource limitations of IoT devices, we conclude that there are still several improvements to be made to the 2nd line of defense mechanisms like Intrusion Detection Systems. Using a collection of IP flows, we can monitor the network and identify properties of the data that goes in and out. Since network flows collection have a smaller footprint than packet capturing, it makes it a better choice towards the Internet of Things networks. This paper aims to study IP flow properties of certain network attacks, with the goal of identifying an attack signature only by observing those properties.

Most blockchain-based identity authentication systems focus on using blockchain to establish the public key infrastructure (PKI). It can solve the problem of single point of failure and certificate transparency faced by traditional PKI systems, but there are still some problems such as complex certificate management and complex certificate usage process. In this paper, we propose an identity authentication scheme based on blockchain and identity-based cryptography (IBC). The scheme implements a decentralized private key generator (PKG) by deploying the smart contract in Ethereum blockchain, and uses the IBC signature algorithm and challenge-response protocol during the authentication process. Compared with other blockchain-based identity authentication systems, the scheme not only prevents the single point of failure, but also avoids the complex certificate management, has lower system complexity, and resists impersonation attack, man-in-the-middle attack and replay attack.

With the economic development, the number of cars is increasing, and the traffic accidents and congestion problems that follow will not be underestimated. The concept of the Internet of Vehicles is becoming popular, and demand for intelligent traffic is growing. In this paper, the warning scheme we proposed aims to solve the traffic problems. Using intelligent terminals, it is faster and more convenient to obtain driving behaviors and road condition information. The application of blockchain technology can spread information to other vehicles for sharing without third-party certification. Group signature-based authentication protocol guarantees privacy and security while ensuring identity traceability. In experiments and simulations, the recognition accuracy of driving behavior can reach up to 94.90%. The use of blockchain provides secure, distributed, and autonomous features for the solution. Compared with the traditional signature method, the group signature-based authentication time varies less with the increase of the number of vehicles, and the communication time is more stable.

Signature-based Intrusion Detection Systems (IDS) are a key component in the cybersecurity defense strategy for any network being monitored. In order to improve the efficiency of the intrusion detection system and the corresponding mitigation action, it is important to address the problem of false alarms. In this paper, we present a comparative analysis of two approaches that consider the false alarm minimization and alarm correlation techniques. The output of this analysis provides us the elements to propose a parallelizable strategy designed to achieve better results in terms of precision, recall and alarm load reduction in the prioritization of alarms. We use Prelude SIEM as the event normalizer in order to process security events from heterogeneous sensors and to correlate them. The alarms are verified using the dynamic network context information collected from the vulnerability analysis, and they are prioritized using the HP Arsight priority formula. The results show an important reduction in the volume of alerts, together with a high precision in the identification of false alarms.

Access authentication is a key technology to identify the legitimacy of mobile users when accessing the space-ground integrated networks (SGIN). A hierarchical identity-based signature over lattice (L-HIBS) based mobile access authentication mechanism is proposed to settle the insufficiencies of existing access authentication methods in SGIN such as high computational complexity, large authentication delay and no-resistance to quantum attack. Firstly, the idea of hierarchical identity-based cryptography is introduced according to hierarchical distribution of nodes in SGIN, and a hierarchical access authentication architecture is built. Secondly, a new L-HIBS scheme is constructed based on the Small Integer Solution (SIS) problem to support the hierarchical identity-based cryptography. Thirdly, a mobile access authentication protocol that supports bidirectional authentication and shared session key exchange is designed with the aforementioned L-HIBS scheme. Results of theoretical analysis and simulation experiments suggest that the L-HIBS scheme possesses strong unforgeability of selecting identity and adaptive selection messages under the standard security model, and the authentication protocol has smaller computational overhead and shorter private keys and shorter signature compared to given baseline protocols.

Threat actors are constantly seeking new attack surfaces, with ransomeware being one the most successful attack vectors that have been used for financial gain. This has been achieved through the dispersion of unlimited polymorphic samples of ransomware whilst those responsible evade detection and hide their identity. Nonetheless, every ransomware threat actor adopts some similar style or uses some common patterns in their malicious code writing, which can be significant evidence contributing to their identification. he first step in attempting to identify the source of the attack is to cluster a large number of ransomware samples based on very little or no information about the samples, accordingly, their traits and signatures can be analysed and identified. T herefore, this paper proposes an efficient fuzzy analysis approach to cluster ransomware samples based on the combination of two fuzzy techniques fuzzy hashing and fuzzy c-means (FCM) clustering. Unlike other clustering techniques, FCM can directly utilise similarity scores generated by a fuzzy hashing method and cluster them into similar groups without requiring additional transformational steps to obtain distance among objects for clustering. Thus, it reduces the computational overheads by utilising fuzzy similarity scores obtained at the time of initial triaging of whether the sample is known or unknown ransomware. The performance of the proposed fuzzy method is compared against k-means clustering and the two fuzzy hashing methods SSDEEP and SDHASH which are evaluated based on their FCM clustering results to understand how the similarity score affects the clustering results.

Dynamic Information Flow Tracking (DIFT) has been proposed to detect stealthy and persistent cyber attacks that evade existing defenses such as firewalls and signature-based antivirus systems. A DIFT defense taints and tracks suspicious information flows across the network in order to identify possible attacks, at the cost of additional memory overhead for tracking non-adversarial information flows. In this paper, we present the first analytical model that describes the interaction between DIFT and adversarial information flows, including the probability that the adversary evades detection and the performance overhead of the defense. Our analytical model consists of a multi-stage game, in which each stage represents a system process through which the information flow passes. We characterize the optimal strategies for both the defense and adversary, and derive efficient algorithms for computing the strategies. Our results are evaluated on a realworld attack dataset obtained using the Refinable Attack Investigation (RAIN) framework, enabling us to draw conclusions on the optimal adversary and defense strategies, as well as the effect of valid information flows on the interaction between adversary and defense.

Security is of concern of any computing system. Intruders break into machines to steal private data, important credentials, or credit cards. Causing damage, denying services, spaming, and defrauding are among intruders' goals. Security engineers strive to secure systems against many kinds of attacks. Different security controls are deployed at variety of perimeters to fight attacks. Firewalls, intrusion detection systems, intrusion prevention systems, encryption techniques, spam filters, and anti-adware are among such security controls. As a last line of defense, the Antivirus (AV) is of an important concern to the end-users community. Mainly, the AV achieves security by scanning data against its database of virus signatures. In addition, the AV tries to reach a pleasant balance between security and performance because end-users are not willing to deploy a performance-killing AV. When to scan data is an important design factor an Antivirus has to make. In this study, we test two AV aspects. First, we want to know how aggressive the AV is against kernel-level activities compared with user-level activities. In order to do that, we implemented a kernel-level device driver that reads malware with the present of the AV. Second, because AVs are equipped with on-access scanners that are triggered based on file access, we want to know how the AV is achieving that and how that could affect the overall performance.

Universal serial bus (USB) devices have widespread use in different computing platforms, including IoT gadgets, but this popularity makes them attractive targets for exploits and being used as an attack vector by malicious software. During recent years, several reports [17] ranked USB-based malware among top 10 popular malware. This security flaw can slow down the increasing penetration rate of IoT devices since most of those devices have USB ports. The research community and industry has tried to address USB security problem by implementing authentication protocols to protect users' private information and also scanning USB's storage space for any malicious software using their own repository of malware signatures, or simply disallowing use of USB devices on desktops. The new generation of USB malware does not hide in storage space, which means they are not detectable by conventional anti-malware. BadUSB is a malware recently introduced by security researchers. BadUSB modifies USB firmware and can attack all the systems which the infected USB is plugged in. The only applicable solution against this new generation of malware is whitelisting. However, generating a unique fingerprint for USB devices is challenging. In this paper, we propose an accurate USB feature based fingerprinting approach which helps us to create a list of trusted USBs as device whitelist. Our solution prevents and detects BadUSB and similar attacks by generating fingerprint from trusted USB devices' features and their primary usage. We verified the uniqueness of our generated fingerprints by analyzing real data which is collected from USB drives used by students in academic computer labs over one year. Our results indicate that our feature based whitelisting approach with an accuracy of 98.5% can identify USB whitelist members.

Web-based Git hosting services such as GitHub and GitLab are popular choices to manage and interact with Git repositories. However, they lack an important security feature - the ability to sign Git commits. Users instruct the server to perform repository operations on their behalf and have to trust that the server will execute their requests faithfully. Such trust may be unwarranted though because a malicious or a compromised server may execute the requested actions in an incorrect manner, leading to a different state of the repository than what the user intended. In this paper, we show a range of high-impact attacks that can be executed stealthily when developers use the web UI of a Git hosting service to perform common actions such as editing files or merging branches. We then propose le-git-imate, a defense against these attacks which provides security guarantees comparable and compatible with Git's standard commit signing mechanism. We implement le-git-imate as a Chrome browser extension. le-git-imate does not require changes on the server side and can thus be used immediately. It also preserves current workflows used in Github/GitLab and does not require the user to leave the browser, and it allows anyone to verify that the server's actions faithfully follow the user's requested actions. Moreover, experimental evaluation using the browser extension shows that le-git-imate has comparable performance with Git's standard commit signature mechanism. With our solution in place, users can take advantage of GitHub/GitLab's web-based features without sacrificing security, thus paving the way towards verifiable web-based Git repositories.

In this work, we analyze the feasibility of a physically secure implementation of the quantum-resistant supersingular isogeny Diffie-Hellman (SIDH) protocol. Notably, we analyze the defense against timing attacks, simple power analysis, differential power analysis, and fault attacks. Luckily, the SIDH protocol closely resembles its predecessor, the elliptic curve Diffie-Hellman (ECDH) key exchange. As such, much of the extensive literature in side-channel analysis can also apply to SIDH. In particular, we focus on a hardware implementation that features a true random number generator, ALU, and controller. SIDH is composed of two rounds containing a double-point multiplication to generate a secret kernel point and an isogeny over that kernel to arrive at a new elliptic curve isomorphism. To protect against simple power analysis and timing attacks, we recommend a constant-time implementation with Fermat's little theorem inversion. Differential power analysis targets the power output of the SIDH core over many runs. As such, we recommend scaling the base points by secret scalars so that each iteration has a unique power signature. Further, based on recent oracle attacks on SIDH, we cannot recommend the use of static keys from both parties. The goal of this paper is to analyze the tradeoffs in elliptic curve theory to produce a cryptographically and physically secure implementation of SIDH.

Attacks on web services are major concerns and can expose organizations valuable information resources. Despite there are increasing awareness in secure programming, we still find vulnerabilities in web services. To protect deployed web services, it is important to have defense techniques. Signaturebased Intrusion Detection Systems (IDS) have gained popularity to protect applications against attacks. However, signature IDSs have limited number of attack signatures. In this paper, we propose a Genetic Algorithm (GA)-based attack signature generation approach and show its application for web services. GA algorithm has the capability of generating new member from a set of initial population. We leverage this by generating new attack signatures at SOAP message level to overcome the challenge of limited number of attack signatures. The key contributions include defining chromosomes and fitness functions. The initial results show that the GA-based IDS can generate new signatures and complement the limitation of existing web security testing tools. The approach can generate new attack signatures for injection, privilege escalation, denial of service and information leakage.

Anomaly detection for cyber-security defence hasgarnered much attention in recent years providing an orthogonalapproach to traditional signature-based detection systems.Anomaly detection relies on building probability models ofnormal computer network behaviour and detecting deviationsfrom the model. Most data sets used for cyber-security havea mix of user-driven events and automated network events,which most often appears as polling behaviour. Separating theseautomated events from those caused by human activity is essentialto building good statistical models for anomaly detection. This articlepresents a changepoint detection framework for identifyingautomated network events appearing as periodic subsequences ofevent times. The opening event of each subsequence is interpretedas a human action which then generates an automated, periodicprocess. Difficulties arising from the presence of duplicate andmissing data are addressed. The methodology is demonstrated usingauthentication data from Los Alamos National Laboratory'senterprise computer network.

One of the challenges of malware defense is that the attacker has the advantage over the defender. In many cases, an attack is successful and causes damage before the defender can even begin to prepare a defense. The ability to anticipate attacks and prepare defenses before they occur would be a significant scientific and technological development with practical applications in cybersecurity. In this paper, we present a method to augment machine learning-based malware detection systems by predicting signatures of future malware variants and injecting these variants into the defensive system as a vaccine. Our method uses deep learning to learn patterns of malware evolution from family histories. These evolution patterns are then used to predict future family developments. Our experiments show that a detection system augmented with these future malware signatures is able to detect future malware variants that could not be detected by the detection system alone. In particular, it detected 11 new malware variants without increasing false positives, while providing up to 5 months of lead time between prediction and attack.

The large number of malicious files that are produced daily outpaces the current capacity of malware analysis and detection. For example, Intel Security Labs reported that during the second quarter of 2016, their system found more than 40M of new malware [1]. The damage of malware attacks is also increasingly devastating, as witnessed by the recent Cryptowall malware that has reportedly generated more than \$325M in ransom payments to its perpetrators [2]. In terms of defense, it has been widely accepted that the traditional approach based on byte-string signatures is increasingly ineffective, especially for new malware samples and sophisticated variants of existing ones. New techniques are therefore needed for effective defense against malware. Motivated by this problem, the paper investigates a new defense technique against malware. The technique presented in this paper is utilized for automatic identification of malware packers that are used to obfuscate malware programs. Signatures of malware packers and obfuscators are extracted from the CFGs of malware samples. Unlike conventional byte signatures that can be evaded by simply modifying one or multiple bytes in malware samples, these signatures are more difficult to evade. For example, CFG-based signatures are shown to be resilient against instruction modifications and shuffling, as a single signature is sufficient for detecting mildly different versions of the same malware. Last but not least, the process for extracting CFG-based signatures is also made automatic.

Bitcoin is a decentralized digital currency, widely used for its perceived anonymity property, and has surged in popularity in recent years. Bitcoin publishes the complete transaction history in a public ledger, under pseudonyms of users. This is an alternative way to prevent double-spending attack instead of central authority. Therefore, if pseudonyms of users are attached to their identities in real world, the anonymity of Bitcoin will be a serious vulnerability. It is necessary to enhance anonymity of Bitcoin by a coin mixing service or other modifications in Bitcoin protocol. But in a coin mixing service, the relationship among input and output addresses is not hidden from the mixing service provider. So the mixing server still has the ability to track the transaction records of Bitcoin users. To solve this problem, We present a new coin mixing scheme to ensure that the relationship between input and output addresses of any users is invisible for the mixing server. We make use of a ring signature algorithm to ensure that the mixing server can't distinguish specific transaction from all these addresses. The ring signature ensures that a signature is signed by one of its users in the ring and doesn't leak any information about who signed it. Furthermore, the scheme is fully compatible with existing Bitcoin protocol and easily to scale for large amount of users.

Because of the explosive growth of Android malware and due to the severity of its damages, the detection of Android malware has become an increasing important topic in cybersecurity. Currently, the major defense against Android malware is commercial mobile security products which mainly use signature-based method for detection. However, attackers can easily devise methods, such as obfuscation and repackaging, to evade the detection, which calls for new defensive techniques that are harder to evade. In this paper, resting on the analysis of Application Programming Interface (API) calls extracted from the smali files, we further categorize the API calls which belong to the some method in the smali code into a block. Based on the generated API call blocks, we then explore deep neural networks (i.e., Deep Belief Network (DBN) and Stacked AutoEncoders (SAEs)) for newly unknown Android malware detection. Using a real sample collection from Comodo Cloud Security Center, a comprehensive experimental study is performed to compare various malware detection approaches. The experimental results demonstrate that (1) our proposed feature extraction method (i.e., using API call blocks) outperforms using API calls directly in Android malware detection; (2) DBN works better than SAEs in this application; and (3) the detection performance of deep neural networks is better than shallow learning architectures.

Botnet malware, which infects Internet-connected devices and seizes control for a remote botmaster, is a long-standing threat to Internet-connected users and systems. Botnets are used to conduct DDoS attacks, distributed computing (e.g., mining bitcoins), spread electronic spam and malware, conduct cyberwarfare, conduct click-fraud scams, and steal personal user information. Current approaches to the detection and classification of botnet malware include syntactic, or signature-based, and semantic, or context-based, detection techniques. Both methods have shortcomings and botnets remain a persistent threat. In this paper, we propose a method of botnet detection using Nonparametric Bayesian Methods.

Monitoring network behaviors of mobile applications, controlling their resource access and detecting potentially harmful apps are becoming increasingly important for the security protection within today's organizational, ISP and carriers. For this purpose, apps need to be identified from their communication, based upon their individual traffic signatures (called imprints in our research). Creating imprints for a large number of apps is nontrivial, due to the challenges in comprehensively analyzing their network activities at a large scale, for millions of apps on today's rapidly-growing app marketplaces. Prior research relies on automatic exploration of an app's user interfaces (UIs) to trigger its network activities, which is less likely to scale given the cost of the operation (at least 5 minutes per app) and its effectiveness (limited coverage of an app's behaviors). In this paper, we present Tiger (Traffic Imprint Generator), a novel technique that makes comprehensive app imprint generation possible in a massive scale. At the center of Tiger is a unique instantiated slicing technique, which aggressively prunes the program slice extracted from the app's network-related code by evaluating each variable's impact on possible network invariants, and removing those unlikely to contribute through assigning them concrete values. In this way, Tiger avoids exploring a large number of program paths unrelated to the app's identifiable traffic, thereby reducing the cost of the code analysis by more than one order of magnitude, in comparison with the conventional slicing and execution approach. Our experiments show that Tiger is capable of recovering an app's full network activities within 18 seconds, achieving over 98% coverage of its identifiable packets and 0.742% false detection rate on app identification. Further running the technique on over 200,000 real-world Android apps (including 78.23% potentially harmful apps) leads to the discovery of surprising new types of traffic invariants, including fake device information, hardcoded time values, session IDs and credentials, as well as complicated trigger conditions for an app's network activities, such as human involvement, Intent trigger and server-side instructions. Our findings demonstrate that many network activities cannot easily be invoked through automatic UI exploration and code-analysis based approaches present a promising alternative.

Although anti-virus software has significantly evolved over the last decade, classic signature matching based on byte patterns is still a prevalent concept for identifying security threats. Anti-virus signatures are a simple and fast detection mechanism that can complement more sophisticated analysis strategies. However, if signatures are not designed with care, they can turn from a defensive mechanism into an instrument of attack. In this paper, we present a novel method for automatically deriving signatures from anti-virus software and discuss how the extracted signatures can be used to attack sensible data with the aid of the virus scanner itself. To this end, we study the practicability of our approach using four commercial products and exemplary demonstrate anti-virus assisted attacks in three different scenarios.

In cyberspace, unknown zero-day attacks can bring safety hazards. Traditional defense methods based on signatures are ineffective. Based on the Cyberspace Mimic Defense (CMD) architecture, the paper proposes a framework to detect the attacks and respond to them. Inputs are assigned to all online redundant heterogeneous functionally equivalent modules. Their independent outputs are compared and the outputs in the majority will be the final response. The abnormal outputs can be detected and so can the attack. The damaged executive modules with abnormal outputs will be replaced with new ones from the diverse executive module pool. By analyzing the abnormal outputs, the correspondence between inputs and abnormal outputs can be built and inputs leading to recurrent abnormal outputs will be written into the zero-day attack related database and their reuses cannot work any longer, as the suspicious malicious inputs can be detected and processed. Further responses include IP blacklisting and patching, etc. The framework also uses honeypot like executive module to confuse the attacker. The proposed method can prevent the recurrent attack based on the same exploit.