Biblio

Anomaly-based Detection Systems (ADSs) attempt to learn the features of behaviors and events of a system and/or users over a period to build a profile of normal behaviors. There has been a growing interest in ADSs and typically conceived as more powerful systems One of the important factors for ADSs is an ability to distinguish between normal and abnormal behaviors in a given period. However, it is getting complicated due to the dynamic network environment that changes every minute. It is dangerous to distinguish between normal and abnormal behaviors with a fixed threshold in a dynamic environment because it cannot guarantee the threshold is always an indication of normal behaviors. In this paper, we propose an adaptive threshold for a dynamic environment with a trust management scheme for efficiently managing the profiles of normal and abnormal behaviors. Based on the assumption of the statistical analysis-based ADS that normal data instances occur in high probability regions while malicious data instances occur in low probability regions of a stochastic model, we set two adaptive thresholds for normal and abnormal behaviors. The behaviors between the two thresholds are classified as suspicious behaviors, and they are efficiently evaluated with a trust management scheme.

This paper analyzes security problems of modern computer systems caused by vulnerabilities in their operating systems (OSs). Our scrutiny of widely used enterprise OSs focuses on their vulnerabilities by examining the statistical data available on how vulnerabilities in these systems are disclosed and eliminated, and by assessing their criticality. This is done by using statistics from both the National Vulnerabilities Database and the Common Vulnerabilities and Exposures System. The specific technical areas the paper covers are the quantitative assessment of forever-day vulnerabilities, estimation of days-of-grey-risk, the analysis of the vulnerabilities severity and their distributions by attack vector and impact on security properties. In addition, the study aims to explore those vulnerabilities that have been found across a diverse range of OSs. This leads us to analyzing how different intrusion-tolerant architectures deploying the OS diversity impact availability, integrity, and confidentiality.

Bitcoin is popular not only with consumers, but also with cybercriminals (e.g., in ransomware and online extortion, and commercial online child exploitation). Given the potential of Bitcoin to be involved in a criminal investigation, the need to have an up-to-date and in-depth understanding on the forensic acquisition and analysis of Bitcoins is crucial. However, there has been limited forensic research of Bitcoin in the literature. The general focus of existing research is on postmortem analysis of specific locations (e.g. wallets on mobile devices), rather than a forensic approach that combines live data forensics and postmortem analysis to facilitate the identification, acquisition, and analysis of forensic traces relating to the use of Bitcoins on a system. Hence, the latter is the focus of this paper where we present an open source tool for live forensic and postmortem analysing automatically. Using this open source tool, we describe a list of target artifacts that can be obtained from a forensic investigation of popular Bitcoin clients and Web Wallets on different web browsers installed on Windows 7 and Windows 10 platforms.

We consider the possibility of detecting malicious behaviors of the advanced persistent threat (APT) at endpoints during incident response or forensics investigations. Specifically, we study the case where third-party sensors are not available; our observables are obtained solely from inherent digital artifacts of Windows operating systems. What is of particular interest is an artifact called the Application Compatibility Cache (Shimcache). As it is not apparent from the Shimcache when a file has been executed, we propose an algorithm of estimating the time of file execution up to an interval. We also show guarantees of the proposed algorithm's performance and various possible extensions that can improve the estimation. Finally, combining this approach with methods of machine learning, as well as information from other digital artifacts, we design a prototype system called XTEC and demonstrate that it can help hunt for the APT in a real-world case study.

Malware threat classification involves understanding the behavior of the malicious software and how it affects a victim host system. Classifying threats allows for measured response appropriate to the risk involved. Malware incident response depends on many automated tools for the classification of threat to help identify the appropriate reaction to a threat alert. Cuckoo Sandbox is one such tool which can be used for automated analysis of malware and one method of threat classification provided is a threat score. A security analyst might submit a suspicious file to Cuckoo for analysis to determine whether or not the file contains malware or performs potentially malicious behavior on a system. Cuckoo is capable of producing a report of this behavior and ranks the severity of the observed actions as a score from one to ten, with ten being the most severe. As such, a malware sample classified as an 8 would likely take priority over a sample classified as a 3. Unfortunately, this scoring classification can be misleading due to the underlying methodology of severity classification. In this paper we demonstrate why the current methodology of threat scoring is flawed and therefore we believe it can be improved with greater emphasis on analyzing the behavior of the malware. This allows for a threat classification rating which scales with the risk involved in the malware behavior.

To protect sensitive information of an organization, we need to have proper access controls since several data breach incidents were happened because of broken access controls. Normally, the IT auditing process would be used to identify security weaknesses and should be able to detect any potential access control violations in advance. However, most auditing processes are done manually and not performed consistently since lots of resources are required; thus, the auditing is performed for quality assurance purposes only. This paper proposes an automated process to audit the access controls on the Windows server operating system. We define the audit checklist and use the controls defined in ISO/IEC 27002:2013 as a guideline for identifying audit objectives. In addition, an automated audit tool is developed for checking security controls against defined security policies. The results of auditing are the list of automatically generated passed and failed policies. If the auditing is done consistently and automatically, the intrusion incidents could be detected earlier and essential damages could be prevented. Eventually, it would help increase the reliability of the system.

We have analyzed the hardware full-disk encryption of several solid state drives (SSDs) by reverse engineering their firmware. These drives were produced by three manufacturers between 2014 and 2018, and are both internal models using the SATA and NVMe interfaces (in a M.2 or 2.5" traditional form factor) and external models using the USB interface. In theory, the security guarantees offered by hardware encryption are similar to or better than software implementations. In reality, we found that many models using hardware encryption have critical security weaknesses due to specification, design, and implementation issues. For many models, these security weaknesses allow for complete recovery of the data without knowledge of any secret (such as the password). BitLocker, the encryption software built into Microsoft Windows will rely exclusively on hardware full-disk encryption if the SSD advertises support for it. Thus, for these drives, data protected by BitLocker is also compromised. We conclude that, given the state of affairs affecting roughly 60% of the market, currently one should not rely solely on hardware encryption offered by SSDs and users should take additional measures to protect their data.

The paper describes the process of coding information in the heterogenic multi-core system on chip for virtual-based security designed For image processing, signal processing and neural networks emulation. The coding of information carried out in assembly language according to the GOST. This is an implementation of the GOST - a standard symmetric key block cipher has a 64-bit block size and 256-bit key size.

Profiled DPA is a new method combined with machine learning method in side channel attack which is put forward by Whitnall in CHES 2015.[1]The most important part lies in effectiveness of extracting information. This paper introduces a new rule Explained Local Variance (ELV) to extract information in profiled stage for profiled DPA. It attracts information effectively and shields noise to get better accuracy than the original rule. The ELV enables an attacker to use less power traces to get the same result as before. It also leads to 94.6% space reduction and 29.2% time reduction for calculation. For security circuit implementation, a high order masking scheme in modelsim is implemented. A new exchange network is put forward. 96.9% hardware resource is saved due to the usage of this network.

In this paper, a general content adaptive image steganography detector in the spatial domain is proposed. We assemble conventional Haar and LBP features to construct local co-occurrence features, then the boosted classifiers are used to assemble the features as well as the final detector, and each weak classifier of the boosted classifiers corresponds to the co-occurrence feature of a local image region. Moreover, the classification ability and the generalization power of the candidate features are both evaluated for decision in the feature selection procedure of boosting training, which makes the final detector more accuracy. The experimental results on standard dataset show that the proposed framework can detect two primary content adaptive stego algorithms in the spatial domain with higher accuracy than the state-of-the-art steganalysis method.

This paper shows that stochastic heuristic approach for implicitly solving addition chain problem (ACP) in public-key cryptosystem (PKC) enhances the efficiency of the PKC and improves the security by blinding the multiplications/squaring operations involved against side-channel attack (SCA). We show that while the current practical heuristic approaches being deterministic expose the fixed pattern of the operations, using stochastic method blinds the pattern by being unpredictable and generating diffident pattern of operation for the same exponent at a different time. Thus, if the addition chain (AC) is generated implicitly every time the exponentiation operation is being made, needless for such approaches as padding by insertion of dummy operations and the operation is still totally secured against the SCA. Furthermore, we also show that the stochastic approaches, when carefully designed, further reduces the length of the operation than state-of-the-art practical methods for improving the efficiency. We demonstrated our investigation by implementing RSA cryptosystem using the stochastic approach and the results benchmarked with the existing current methods.

People use their personal computers, laptops, tablets and smart phones to digitally sign documents in company's websites and other online electronic applications, and one of the main cybersecurity challenges in this process is trusted digital signature. While the majority of systems use password-based authentication to secure electronic signature, some more critical systems use USB token and smart card to prevent identity theft and implement the trusted digital signing process. Even though smart card provides stronger security, any weakness in the terminal itself can compromise the security of smart card. In this paper, we investigate current smart card digital signature, and illustrate well-known basic vulnerabilities of smart card terminal with the real implementation of two possible attacks including PIN sniffing and message alteration just before signing. As we focus on second attack in this paper, we propose a novel mechanism using time-based digital signing by smart card to defend against message alteration attack. Our prototype implementation and performance analysis illustrate that our proposed mechanism is feasible and provides stronger security. Our method uses popular timestamping protocol packets and does not require any new key distribution and certificate issuance.

Cloud computing is an assured progression inside the future of facts generation. It's far a sub-domain of network security. These days, many huge or small organizations are switching to cloud which will shop and arrange their facts. As a result, protection of cloud networks is the want of the hour. DDoS is a killer software for cloud computing environments on net today. It is a distributed denial of carrier. we will beat the ddos attacks if we have the enough assets. ddos attacks can be countered by means of dynamic allocation of the assets. In this paper the attack is detected as early as possible and prevention methods is done and also mitigation method is also implemented thus attack can be avoided before it may occur.

This paper presents a novel technique to quantify the operational resilience for power electronic-based components affected by High-Impact Low-Frequency (HILF) weather-related events such as high speed winds. In this study, the resilience quantification is utilized to investigate how prompt the system goes back to the pre-disturbance or another stable operational state. A complexity quantification metric is used to assess the system resilience. The test system is a Solid-State Transformer (SST) representing a complex, nonlinear interconnected system. Results show the effectiveness of the proposed technique for quantifying the operational resilience in systems affected by weather-related disturbances.

For modern Automatic Test Equipment (ATE) one of the most daunting tasks is now Information Assurance (IA). What was once at most a secondary item consisting mainly of installing an Anti-Virus suite is now becoming one of the most important aspects of ATE. Given the current climate of IA it has become important to ensure ATE is kept safe from any breaches of security or loss of information. Even though most ATE are not on the Internet (or even on a network for many) they are still vulnerable to some of the same attack vectors plaguing common computers and other electronic devices. This paper will discuss some of the processes and procedures which must be used to ensure that modern ATE can continue to be used to test and detect faults in the systems they are designed to test. The common items that must be considered for ATE are as follows: The ATE system must have some form of Anti-Virus (as should all computers). The ATE system should have a minimum software footprint only providing the software needed to perform the task. The ATE system should be verified to have all the Operating System (OS) settings configured pursuant to the task it is intended to perform. The ATE OS settings should include password and password expiration settings to prevent access by anyone not expected to be on the system. The ATE system software should be written and constructed such that it in itself is not readily open to attack. The ATE system should be designed in a manner such that none of the instruments in the system can easily be attacked. The ATE system should insure any paths to the outside world (such as Ethernet or USB devices) are limited to only those required to perform the task it was designed for. These and many other common configuration concerns will be discussed in the paper.

Malware authors attempt to obfuscate and hide their code in its static and dynamic states. This paper provides a novel approach to aid analysis by intercepting and capturing malware artifacts and providing dynamic control of process flow. Capturing malware artifacts allows an analyst to more quickly and comprehensively understand malware behavior and obfuscation techniques and doing so interactively allows multiple code paths to be explored. The faster that malware can be analyzed the quicker the systems and data compromised by it can be determined and its infection stopped. This research proposes an instantiation of an interactive malware analysis and artifact capture tool.

Recently a huge trend on the internet of things (IoT) and an exponential increase in automated tools are helping malware producers to target IoT devices. The traditional security solutions against malware are infeasible due to low computing power for large-scale data in IoT environment. The number of malware and their variants are increasing due to continuous malware attacks. Consequently, the performance improvement in malware analysis is critical requirement to stop rapid expansion of malicious attacks in IoT environment. To solve this problem, the paper proposed a novel framework for classifying malware in IoT environment. To achieve flne-grained malware classification in suggested framework, the malware image classification system (MICS) is designed for representing malware image globally and locally. MICS first converts the suspicious program into the gray-scale image and then captures hybrid local and global malware features to perform malware family classification. Preliminary experimental outcomes of MICS are quite promising with 97.4% classification accuracy on 9342 windows suspicious programs of 25 families. The experimental results indicate that proposed framework is quite capable to process large-scale IoT malware.

Behavioral malware detection aims to improve on the performance of static signature-based techniques used by anti-virus systems, which are less effective against modern polymorphic and metamorphic malware. Behavioral malware classification aims to go beyond the detection of malware by also identifying a malware's family according to a naming scheme such as the ones used by anti-virus vendors. Behavioral malware classification techniques use run-time features, such as file system or network activities, to capture the behavioral characteristic of running processes. The increasing volume of malware samples, diversity of malware families, and the variety of naming schemes given to malware samples by anti-virus vendors present challenges to behavioral malware classifiers. We describe a behavioral classifier that uses a Convolutional Recurrent Neural Network and data from Microsoft Windows Prefetch files. We demonstrate the model's improvement on the state-of-the-art using a large dataset of malware families and four major anti-virus vendor naming schemes. The model is effective in classifying malware samples that belong to common and rare malware families and can incrementally accommodate the introduction of new malware samples and families.

In this paper we present a new approach, named DLGraph, for malware detection using deep learning and graph embedding. DLGraph employs two stacked denoising autoencoders (SDAs) for representation learning, taking into consideration computer programs' function-call graphs and Windows application programming interface (API) calls. Given a program, we first use a graph embedding technique that maps the program's function-call graph to a vector in a low-dimensional feature space. One SDA in our deep learning model is used to learn a latent representation of the embedded vector of the function-call graph. The other SDA in our model is used to learn a latent representation of the given program's Windows API calls. The two learned latent representations are then merged to form a combined feature vector. Finally, we use softmax regression to classify the combined feature vector for predicting whether the given program is malware or not. Experimental results based on different datasets demonstrate the effectiveness of the proposed approach and its superiority over a related method.

Mobile ad hoc networks (MANETs) are a set of mobile wireless nodes that can communicate without the need for an infrastructure. Features of MANETs have made them vulnerable to many security attacks including wormhole attack. In the past few years, different methods have been introduced for detecting, mitigating, and preventing wormhole attacks in MANETs. In this paper, we introduce a new decentralized scheme based on statistical metrics for detecting wormholes that employs “number of new neighbors” along with “number of neighbors” for each node as its parameters. The proposed scheme has considerably low detection delay and does not create any traffic overhead for routing protocols which include neighbor discovery mechanism. Also, it possesses reasonable processing power and memory usage. Our simulation results using NS3 simulator show that the proposed scheme performs well in terms of detection accuracy, false positive rate and mean detection delay.

Browser fingerprinting has emerged as a technique to track users without their consent. Unlike cookies, fingerprinting is a stateless technique that does not store any information on devices, but instead exploits unique combinations of attributes handed over freely by browsers. The uniqueness of fingerprints allows them to be used for identification. However, browser fingerprints change over time and the effectiveness of tracking users over longer durations has not been properly addressed. In this paper, we show that browser fingerprints tend to change frequently-from every few hours to days-due to, for example, software updates or configuration changes. Yet, despite these frequent changes, we show that browser fingerprints can still be linked, thus enabling long-term tracking. FP-STALKER is an approach to link browser fingerprint evolutions. It compares fingerprints to determine if they originate from the same browser. We created two variants of FP-STALKER, a rule-based variant that is faster, and a hybrid variant that exploits machine learning to boost accuracy. To evaluate FP-STALKER, we conduct an empirical study using 98,598 fingerprints we collected from 1, 905 distinct browser instances. We compare our algorithm with the state of the art and show that, on average, we can track browsers for 54.48 days, and 26 % of browsers can be tracked for more than 100 days.

Text-based CAPTCHAs are still commonly used to attempt to prevent automated access to web services. By displaying an image of distorted text, they attempt to create a challenge image that OCR software can not interpret correctly, but a human user can easily determine the correct response to. This work focuses on a CAPTCHA used by a popular Chinese language question-and-answer website and how resilient it is to modern machine learning methods. While the majority of text-based CAPTCHAs focus on transcription tasks, the CAPTCHA solved in this work is based on localization of inverted symbols in a distorted image. A convolutional neural network (CNN) was created to evaluate the likelihood of a region in the image belonging to an inverted character. It is used with a feature map and clustering to identify potential locations of inverted characters. Training of the CNN was performed using curriculum learning and compared to other potential training methods. The proposed method was able to determine the correct response in 95.2% of cases of a simulated CAPTCHA and 67.6% on a set of real CAPTCHAs. Potential methods to increase difficulty of the CAPTCHA and the success rate of the automated solver are considered.

To ensure reliable and predictable service in the electrical grid it is important to gauge the level of trust present within critical components and substations. Although trust throughout a smart grid is temporal and dynamically varies according to measured states, it is possible to accurately formulate communications and service level strategies based on such trust measurements. Utilizing an effective set of machine learning and statistical methods, it is shown that establishment of trust levels between substations using behavioral pattern analysis is possible. It is also shown that the establishment of such trust can facilitate simple secure communications routing between substations.

Advanced persistent threat (APT) has been considered globally as a serious social problem since the 2010s. Adversaries of this threat, at first, try to penetrate into targeting organizations by using a backdoor which is opened with drive-by-download attacks, malicious e-mail attachments, etc. After adversaries' intruding, they usually execute benign applications (e.g, OS built-in commands, management tools published by OS vendors, etc.) for investigating networks of targeting organizations. Therefore, if they penetrate into networks once, it is difficult to rapidly detect these malicious activities only by using anti-virus software or network-based intrusion systems. Meanwhile, enterprise networks are managed well in general. That means network administrators have a good grasp of installed applications and routinely used applications for employees' daily works. Thereby, in order to find anomaly behaviors on well-managed networks, it is effective to observe changes executing their applications. In this paper, we propose a lightweight host-based intrusion detection system by using process generation patterns. Our system periodically collects lists of active processes from each host, then the system constructs process trees from the lists. In addition, the system detects anomaly processes from the process trees considering parent-child relationships, execution sequences and lifetime of processes. Moreover, we evaluated the system in our organization. The system collected 2, 403, 230 process paths in total from 498 hosts for two months, then the system could extract 38 anomaly processes. Among them, one PowerShell process was also detected by using an anti-virus software running on our organization. Furthermore, our system could filter out the other 18 PowerShell processes, which were used for maintenance of our network.

To be applicable to high user request workloads, web caching strategies benefit from low implementation and update effort. In this regard, the Least Recently Used (LRU) replacement principle is a simple and widely-used method. Despite its popularity, LRU has deficits in the achieved hit rate performance and cannot consider transport and network optimization criteria for selecting content to be cached. As a result, many alternatives have been proposed in the literature, which improve the cache performance at the cost of higher complexity. In this work, we evaluate the implementation complexity and runtime performance of LRU, Least Frequently Used (LFU), and score based strategies in the class of fast O(1) updates with constant effort per request. We implement Window LFU (W-LFU) within this class and show that O(1) update effort can be achieved. We further compare fast update schemes of Score Gated LRU and new Score Gated Polling (SGP). SGP is simpler than LRU and provides full flexibility for arbitrary score assessment per data object as information basis for performance optimization regarding network cost and quality measures.