Biblio

Considering the amount of time we spend on the internet, web pages have evolved over a period of time with rapid progression and momentum. With such advancement, we find ourselves fronting a few hostile ideologies, breaching the security levels of webpages as such. The most hazardous of them all is XSS, known as Cross-Site Scripting, is one of the attacks which frequently occur in website-based applications. Cross-Site Scripting (XSS) attacks happen when malicious data enters a web application through an untrusted source. The spam attacks happen in the form of Wall posts, News feed, Message spam and mostly when a user is open to download content of webpages. This paper investigates the use of machine learning to build classifiers to allow the detection of XSS. Establishing our approach, we target the detection modus operandi of XSS attack via two features: URLs and JavaScript. To predict the level of XSS threat, we will be using four machine learning algorithms (SVM, KNN, Random forest and Logistic Regression). Proposing these classified algorithms, webpages will be branded as malicious or benign. After assessing and calculating the dataset features, we concluded that the Random Forest Classifier performed most accurately with the lowest False Positive Rate of 0.34. This precision will ensure a method much efficient to evaluate threatening XSS for the smooth functioning of the system.

This article discusses the problem of detecting cross-site scripting (XSS) using machine learning methods. XSS is an attack in which malicious code is embedded on a page to interact with an attacker’s web server. The XSS attack ranks third in the ranking of key web application risks according to Open Source Foundation for Application Security (OWASP). This attack has not been studied for a long time. It was considered harmless. However, this is fallacious: the page or HTTP Cookie may contain very vulnerable data, such as payment document numbers or the administrator session token. Machine learning is a tool that can be used to detect XSS attacks. This article describes an experiment. As a result the model for detecting XSS attacks was created. Following machine learning algorithms are considered: the support vector method, the decision tree, the Naive Bayes classifier, and Logistic Regression. The accuracy of the presented methods is made a comparison.

Cryptojacking is currently being exploited by cyber-criminals. This form of malware runs in the computers of victims without their consent. It often infects browsers and does CPU-intensive computations to mine cryptocurrencies on behalf of the cyber-criminal, which takes the profits without paying for the resources consumed. Such attacks degrade computer performance and potentially reduce the hardware lifetime. We introduce a new cryptojacking detection mechanism based on monitoring the CPU usage of the visited web pages. This may look like an unreliable way to detect mining malware since many web sites are heavy computationally and that malware often throttles CPU usage. However, by combining a set of CPU monitoring features and using machine learning, we manage to obtain metrics like precision and recall close to 1.

Cryptocurrencies are the digital currencies designed to replace the regular cash money while taking place in our daily lives especially for the last couple of years. Mining cryptocurrencies are one of the popular ways to have them and make a profit due to unstable values in the market. This attracts attackers to utilize malware on internet users' computer resources, also known as cryptojacking, to mine cryptocurrencies. Cryptojacking started to be a major issue in the internet world. In this case, we developed MiNo, a web browser add-on application to detect these malicious mining activities running without the user's permission or knowledge. This add-on provides security and efficiency for the computer resources of the internet users. MiNo designed and developed with double-layer protection which makes it ahead of its competitors in the market.

Cryptojacking, the appropriation of users' computational resources without their knowledge or consent to obtain cryp-tocurrencies, is a widespread attack, relatively easy to implement and hard to detect. Either browser-based or binary, cryptojacking lacks robust and reliable detection solutions. This paper presents a hybrid approach to detect cryptojacking where no previous knowledge about the attacks or training data is needed. Our Cryp-tojacking Intrusion Detection Approach, Cryingjackpot, extracts and combines flow and performance counter-based features, aggregating hosts with similar behavior by using unsupervised machine learning algorithms. We evaluate Cryingjackpot experimentally with both an artificial and a hybrid dataset, achieving F1-scores up to 97%.

With rising value and popularity of cryptocurrencies, they inevitably attract cybercriminals seeking illicit profits within blockchain ecosystem. Two of the most popular methods are ransomware and cryptojacking. Ransomware, being the first and more obvious threat has been extensively studied in the past. Unlike that, scientists have often neglected cryptojacking, because it’s less obvious and less harmful than ransomware. In this paper, we’d like to propose enhanced detection program to combat cryptojacking, additionally briefly touching history of cryptojacking, also known as malicious mining and reviewing most notable previous attempts to detect and combat cryptojacking. The review would include out previous work on malicious mining detection and our current detection program is based on its previous iteration, which mostly used CPU usage heuristics to detect cryptojacking. However, we will include additional metrics for malicious mining detection, such as network usage and calls to cryptographic libraries, which result in a 93% detection rate against the selected number of cryptojacking samples, compared to 81% rate achieved in previous work. Finally, we’ll discuss generalization of proposed detection technique to include GPU cryptojackers.

Malware refers to any software accessing or being installed in a system without the authorisation of administrators. Various malware has been widely used for cyber-criminals to accomplish their evil intentions and goals. To combat the increasing amount and reduce the threat of malicious programs, a novel deep learning framework, which uses NLP techniques for reference, combines CNN and LSTM neurones to capture the locally spatial correlations and learn from sequential longterm dependency is proposed. Hence, high-level abstractions and representations are automatically extracted for the malware classification task. The classification accuracy improves from 0.81 (best one by Random Forest) to approximately 1.0.

Internet is the most widely used technology in the current era of information technology and it is embedded in daily life activities. Due to its extensive use in everyday life, it has many applications such as social media (Face book, WhatsApp, messenger etc.,) and other online applications such as online businesses, e-counseling, advertisement on websites, e-banking, e-hunting websites, e-doctor appointment and e-doctor opinion. The above mentioned applications of internet technology makes things very easy and accessible for human being in limited time, however, this technology is vulnerable to various security threats. A vital and severe threat associated with this technology or a particular application is “Phishing attack” which is used by attacker to usurp the network security. Phishing attacks includes fake E-mails, fake websites, fake applications which are used to steal their credentials or usurp their security. In this paper, a detailed overview of various phishing attacks, specifically their background knowledge, and solutions proposed in literature to address these issues using various techniques such as anti-phishing, honey pots and firewalls etc. Moreover, installation of intrusion detection systems (IDS) and intrusion detection and prevention system (IPS) in the networks to allow the authentic traffic in an operational network. In this work, we have conducted end use awareness campaign to educate and train the employs in order to minimize the occurrence probability of these attacks. The result analysis observed for this survey was quite excellent by means of its effectiveness to address the aforementioned issues.

Cloud computing provides so many groundbreaking advantages over native computing servers like to improve capacity and decrease costs, but meanwhile, it carries many security issues also. In this paper, we find the feasible security attacks made about cloud computing, including Wrapping, Browser Malware-Injection and Flooding attacks, and also problems caused by accountability checking. We have also analyzed the honey pot attack and its procedural intrusion way into the system. This paper on overall deals with the most common security breaches in cloud computing and finally honey pot, in particular, to analyze its intrusion way. Our major scope is to do overall security, analyze in the cloud and then to take up with a particular attack to deal with granular level. Honey pot is the one such attack that is taken into account and its intrusion policies are analyzed. The specific honey pot algorithm is in the queue as the extension of this project in the future.

Enterprises round the globe have been searching for a way to securely empower AndroidTM devices for work but have spurned away from the Android platform due to ongoing fragmentation and security concerns. Discrepant vulnerabilities have been reported in Android smartphones since Android Lollipop release. Smartphones can be easily hacked by installing a malicious application, visiting an infectious browser, receiving a crafted MMS, interplaying with plug-ins, certificate forging, checksum collisions, inter-process communication (IPC) abuse and much more. To highlight this issue a manual analysis of Android vulnerabilities is performed, by using data available in National Vulnerability Database NVD and Android Vulnerability website. This paper includes the vulnerabilities that risked the dual persona support in Android 5 and above, till Dec 2017. In our security threat analysis, we have identified a comprehensive list of Android vulnerabilities, vulnerable Android versions, manufacturers, and information regarding complete and partial patches released. So far, there is no published research work that systematically presents all the vulnerabilities and vulnerability assessment for dual persona feature of Android's smartphone. The data provided in this paper will open ways to future research and present a better Android security model for dual persona.

Recognizing the need for proactive analysis of cyber adversary behavior, this paper presents a new event-driven simulation model and implementation to reveal the efforts needed by attackers who have various entry points into a network. Unlike previous models which focus on the impact of attackers' actions on the defender's infrastructure, this work focuses on the attackers' strategies and actions. By operating on a request-response session level, our model provides an abstraction of how the network infrastructure reacts to access credentials the adversary might have obtained through a variety of strategies. We present the current capabilities of the simulator by showing three variants of Bronze Butler APT on a network with different user access levels.

Gartner, a large research and advisory company, anticipates that by 2024 80% of security operation centers (SOCs) will use machine learning (ML) based solutions to enhance their operations.11https://www.ciodive.com/news/how-data-science-tools-can-lighten-the-load-for-cybersecurity-teams/572209/ In light of such widespread adoption, it is vital for the research community to identify and address usability concerns. This work presents the results of the first in situ usability assessment of ML-based tools. With the support of the US Navy, we leveraged the national cyber range-a large, air-gapped cyber testbed equipped with state-of-the-art network and user emulation capabilities-to study six US Naval SOC analysts' usage of two tools. Our analysis identified several serious usability issues, including multiple violations of established usability heuristics for user interface design. We also discovered that analysts lacked a clear mental model of how these tools generate scores, resulting in mistrust \$a\$ and/or misuse of the tools themselves. Surprisingly, we found no correlation between analysts' level of education or years of experience and their performance with either tool, suggesting that other factors such as prior background knowledge or personality play a significant role in ML-based tool usage. Our findings demonstrate that ML-based security tool vendors must put a renewed focus on working with analysts, both experienced and inexperienced, to ensure that their systems are usable and useful in real-world security operations settings.

In the past air-gapped systems that are isolated from networks have been considered to be very secure. Yet there have been reports of such systems being breached. These breaches have shown to use unconventional means for communication also known as covert channels such as Acoustic, Electromagnetic, Magnetic, Electric, Optical, and Thermal to transfer data. In this paper, a review of various attack methods that can compromise an air-gapped system is presented along with a summary of how efficient and dangerous a particular method could be. The capabilities of each covert channel are listed to better understand the threat it poses and also some countermeasures to safeguard against such attack methods are mentioned. These attack methods have already been proven to work and awareness of such covert channels for data exfiltration is crucial in various industries.

Air-gapped networks are isolated from the Internet, since they store and process sensitive information. It has been shown that attackers can exfiltrate data from air-gapped networks by sending acoustic signals generated by computer speakers, however this type of covert channel relies on the existence of loudspeakers in the air-gapped environment. In this paper, we present CD-LEAK - a novel acoustic covert channel that works in constrained environments where loudspeakers are not available to the attacker. Malware installed on a compromised computer can maliciously generate acoustic signals via the optical CD/DVD drives. Binary information can then be modulated over the acoustic signals and be picked up by a nearby Internet connected receiver (e.g., a workstation, hidden microphone, smartphone, laptop, etc.). We examine CD/DVD drives and discuss their acoustical characteristics. We also present signal generation and detection, and data modulation and demodulation algorithms. Based on our proposed method, we developed a transmitter and receiver for PCs and smartphones, and provide the design and implementation details. We examine the channel and evaluate it on various optical drives. We also provide a set of countermeasures against this threat - which has been overlooked.

The Precision Time Protocol is essential for many time-sensitive and time-aware applications. However, it was never designed for security, and despite various approaches to harden this protocol against manipulation, it is still prone to cyber-attacks. Here Advanced Persistent Threats (APT) are of particular concern, as they may stealthily and over extended periods of time manipulate computer clocks that rely on the accurate functioning of this protocol. Simulating such attacks is difficult, as it requires firmware manipulation of network and PTP infrastructure components. Therefore, this paper proposes and demonstrates a programmable Man-in-the-Middle (pMitM) and a programmable injector (pInj) device that allow the implementation of a variety of attacks, enabling security researchers to quantify the impact of APTs on time synchronisation.

Autonomy in cybersystems depends on their ability to be self-aware by understanding the intent of services and applications that are running on those systems. In case of mission-critical cybersystems that are deployed in dynamic and unpredictable environments, the newly integrated unknown applications or services can either be benign and essential for the mission or they can be cyberattacks. In some cases, these cyberattacks are evasive Advanced Persistent Threats (APTs) where the attackers remain undetected for reconnaissance in order to ascertain system features for an attack e.g. Trojan Laziok. In other cases, the attackers can use the system only for computing e.g. cryptomining malware. APTs such as cryptomining malware neither disrupt normal system functionalities nor trigger any warning signs because they simply perform bitwise and cryptographic operations as any other benign compression or encoding application. Thus, it is difficult for defense mechanisms such as antivirus applications to detect these attacks. In this paper, we propose an Operating Context profiling system based on deep neural networks-Long Short-Term Memory (LSTM) networks-using Windows Performance Counters data for detecting these evasive cryptomining applications. In addition, we propose Deep Cryptomining Profiler (DeCrypto Pro), a detection system with a novel model selection framework containing a utility function that can select a classification model for behavior profiling from both the light-weight machine learning models (Random Forest and k-Nearest Neighbors) and a deep learning model (LSTM), depending on available computing resources. Given data from performance counters, we show that individual models perform with high accuracy and can be trained with limited training data. We also show that the DeCrypto Profiler framework reduces the use of computational resources and accurately detects cryptomining applications by selecting an appropriate model, given the constraints such as data sample size and system configuration.

YARA rules utilises string or pattern matching to perform malware analysis and is one of the most effective methods in use today. However, its effectiveness is dependent on the quality and quantity of YARA rules employed in the analysis. This can be managed through the rule optimisation process, although, this may not necessarily guarantee effective utilisation of YARA rules and its generated findings during its execution phase, as the main focus of YARA rules is in determining whether to trigger a rule or not, for a suspect sample after examining its rule condition. YARA rule conditions are Boolean expressions, mostly focused on the binary outcome of the malware analysis, which may limit the optimised use of YARA rules and its findings despite generating significant information during the execution phase. Therefore, this paper proposes embedding fuzzy rules with YARA rules to optimise its performance during the execution phase. Fuzzy rules can manage imprecise and incomplete data and encompass a broad range of conditions, which may not be possible in Boolean logic. This embedding may be more advantageous when the YARA rules become more complex, resulting in multiple complex conditions, which may not be processed efficiently utilising Boolean expressions alone, thus compromising effective decision-making. This proposed embedded approach is applied on a collected malware corpus and is tested against the standard and enhanced YARA rules to demonstrate its success.

During the Cold War era, countries with asymmetrical relationships often demonstrated how lower-tier nation states required the alliance and support from top-tier nation states. This statement no longer stands true as country such as North Korea has exploited global financial institutions through various malware such as WANNACRY V0, V1, V2, evtsys.exe, and BRAMBUL WORM. Top tier nation states such as the U.S. are unable to use diplomatic clout or to retaliate against the deferrer. Our study examined the affidavit filed against the North Korean hacker, Park Jin Hyok, which was provided by the FBI. Our paper focuses on the operations and campaigns that were carried out by the Lazarus Group by focusing on the key factors of the infrastructure and artifacts. Due to the nature of the cyber deterrence, deterrence in the cyber realm is far complex than the nuclear deterrence. We focused on the Sony Picture Entertainment’s incident for our study. In this study, we discuss how cyber deterrence can be employed when different nation states share an asymmetrical relationship. Furthermore, we focus on contestability and attribution that is a key factor that makes cyber deterrence difficult.

The Automation industries that uses Supervisory Control and Data Acquisition (SCADA) systems are highly vulnerable for Network threats. Systems that are air-gapped and isolated from the internet are highly affected due to insider attacks like Spoofing, DOS and Malware threats that affects confidentiality, integrity and availability of Operational Technology (OT) system elements and degrade its performance even though security measures are taken. In this paper, a behavior-based intrusion prevention system (IPS) is designed for OT networks. The proposed system is implemented on SCADA test bed with two systems replicates automation scenarios in industry. This paper describes 4 main classes of cyber-attacks with their subclasses against SCADA systems and methodology with design of components of IPS system, database creation, Baselines and deployment of system in environment. IPS system identifies not only IT protocols but also Industry Control System (ICS) protocols Modbus and DNP3 with their inside communication fields using deep packet inspection (DPI). The analytical results show 99.89% accuracy on binary classification and 97.95% accuracy on multiclass classification of different attack vectors performed on network with low false positive rate. These results are also validated by actual deployment of IPS in SCADA systems with the prevention of DOS attack.

The purpose of this work is to analyze the security model of a robotized system, to analyze the approaches to assessing the security of this system, and to develop our own framework. The solution to this problem involves the use of developed frameworks. The analysis will be conducted on a robotic system of robots. The prefix structures assume that the robotic system is divided into levels, and after that it is necessary to directly protect each level. Each level has its own characteristics and drawbacks that must be considered when developing a security system for a robotic system.

Malicious domain names are consistently changing. It is challenging to keep blacklists of malicious domain names up-to-date because of the time lag between its creation and detection. Even if a website is clean itself, it does not necessarily mean that it won't be used as a pivot point to redirect users to malicious destinations. To address this issue, this paper demonstrates how to use linkage analysis and open-source threat intelligence to visualize the relationship of malicious domain names whilst verifying their categories, i.e., drive-by download, unwanted software etc. Featured by a graph-based model that could present the inter-connectivity of malicious domain names in a dynamic fashion, the proposed approach proved to be helpful for revealing the group patterns of different kinds of malicious domain names. When applied to analyze a blacklisted set of URLs in a real enterprise network, it showed better effectiveness than traditional methods and yielded a clearer view of the common patterns in the data.

We classify .NET files as either benign or malicious by examining directed graphs derived from the set of functions comprising the given file. Each graph is viewed probabilistically as a Markov chain where each node represents a code block of the corresponding function, and by computing the PageRank vector (Perron vector with transport), a probability measure can be defined over the nodes of the given graph. Each graph is vectorized by computing Lebesgue antiderivatives of hand-engineered functions defined on the vertex set of the given graph against the PageRank measure. Files are subsequently vectorized by aggregating the set of vectors corresponding to the set of graphs resulting from decompiling the given file. The result is a fast, intuitive, and easy-to-compute glass-box vectorization scheme, which can be leveraged for training a standalone classifier or to augment an existing feature space. We refer to this vectorization technique as PageRank Measure Integration Vectorization (PMIV). We demonstrate the efficacy of PMIV by training a vanilla random forest on 2.5 million samples of decompiled. NET, evenly split between benign and malicious, from our in-house corpus and compare this model to a baseline model which leverages a text-only feature space. The median time needed for decompilation and scoring was 24ms. 11Code available at https://github.com/gtownrocks/grafuple.

The limited information on the cyberattacks available in the unclassified regime, hardens standardizing the analysis. We address the problem of modeling and analyzing cyberattacks using a multimodal graph approach. We formulate the stages, actors, and outcomes of cyberattacks as a multimodal graph. Multimodal graph nodes include cyberattack victims, adversaries, autonomous systems, and the observed cyber events. In multimodal graphs, single-modality graphs are interconnected according to their interaction. We apply community and centrality analysis on the graph to obtain in-depth insights into the attack. In community analysis, we cluster those nodes that exhibit “strong” inter-modal ties. We further use centrality to rank the nodes according to their importance. Classifying nodes according to centrality provides the progression of the attack from the attacker to the targeted nodes. We apply our methods to two popular case studies, namely GhostNet and Putter Panda and demonstrate a clear distinction in the attack stages.

In cloud computing environments with many virtual machines, containers, and other systems, an epidemic of malware can be crippling and highly threatening to business processes. In this vision paper, we introduce a hierarchical approach to performing malware detection and analysis using several recent advances in machine learning on graphs, hypergraphs, and natural language. We analyze individual systems and their logs, inspecting and understanding their behavior with attentional sequence models. Given a feature representation of each system's logs using this procedure, we construct an attributed network of the cloud with systems and other components as vertices and propose an analysis of malware with inductive graph and hypergraph learning models. With this foundation, we consider the multicloud case, in which multiple clouds with differing privacy requirements cooperate against the spread of malware, proposing the use of federated learning to perform inference and training while preserving privacy. Finally, we discuss several open problems that remain in defending cloud computing environments against malware related to designing robust ecosystems, identifying cloud-specific optimization problems for response strategy, action spaces for malware containment and eradication, and developing priors and transfer learning tasks for machine learning models in this area.

The rapid growth of Android malware apps poses a great security threat to users thus it is very important and urgent to detect Android malware effectively. What's more, the increasing unknown malware and evasion technique also call for novel detection method. In this paper, we focus on API feature and develop a novel method to detect Android malware. First, we propose a novel selection method for API feature related with the malware class. However, such API also has a legitimate use in benign app thus causing FP problem (misclassify benign as malware). Second, we further explore structure relationships between these APIs and map to a matrix interpreted as the hand-refined API-based feature graph. Third, a CNN-based classifier is developed for the API-based feature graph classification. Evaluations of a real-world dataset containing 3,697 malware apps and 3,312 benign apps demonstrate that selected API feature is effective for Android malware classification, just top 20 APIs can achieve high F1 of 94.3% under Random Forest classifier. When the available API features are few, classification performance including FPR indicator can achieve effective improvement effectively by complementing our further work.