Biblio

The drawbacks of traditional methods of cybernetic vulnerability detection relate to the required time to identify new threats, to register them in the Common Vulnerabilities and Exposures (CVE) records, and to score them with the Common Vulnerabilities Scoring System (CVSS). These problems can be mitigated by early vulnerability detection systems relying on social media and open-source data. This paper presents a model that aims to identify emerging cybernetic vulnerabilities in cybersecurity news articles, as part of a system for automatic detection of early cybernetic threats using Open Source Intelligence (OSINT). Three machine learning models were trained on a novel dataset of 1000 labeled news articles to create a strong baseline for classifying cybersecurity articles as relevant (i.e., introducing new security threats), or irrelevant: Support Vector Machines, a Multinomial Naïve Bayes classifier, and a finetuned BERT model. The BERT model obtained the best performance with a mean accuracy of 88.45% on the test dataset. Our experiments support the conclusion that Natural Language Processing (NLP) models are an appropriate choice for early vulnerability detection systems in order to extract relevant information from cybersecurity news articles.

Smart grid achieves reliable, efficient and flexible grid data processing by integrating traditional power grid with information and communication technology. The control center can evaluate the supply and demand of the power grid through aggregated data of users, and then dynamically adjust the power supply, price of the power, etc. However, since the grid data collected from users may disclose the user's electricity using habits and daily activities, the privacy concern has become a critical issue. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring the trusted third party. In this paper, we propose a privacy-preserving smart grid data aggregation scheme satisfying local differential privacy (LDP) based on randomized response. Our scheme can achieve efficient and practical estimation of the statistics of power supply and demand while preserving any individual participant's privacy. The performance analysis shows that our scheme is efficient in terms of computation and communication overhead.

Discovering the potential vulnerabilities in software plays a crucial role in ensuring the security of computer system. This paper proposes a method that can assist security auditors with the analysis of source code. When security auditors identify new vulnerabilities, our method can be adopted to make a list of recommendations that may have the same vulnerabilities for the security auditors. Our method relies on graph representation to automatically extract the mode of PDG(program dependence graph, a structure composed of control dependence and data dependence). Besides, it can be applied to the vulnerability extrapolation scenario, thus reducing the amount of audit code. We worked on an open-source vulnerability test set called Juliet. According to the evaluation results, the clustering effect produced is satisfactory, so that the feature vectors extracted by the Graph2Vec model are applied to labeling and supervised learning indicators are adopted to assess the model for its ability to extract features. On a total of 12,000 small data sets, the training score of the model can reach up to 99.2%, and the test score can reach a maximum of 85.2%. Finally, the recommendation effect of our work is verified as satisfactory.

The application of network technology and high-tech equipment in power systems has increased the degree of grid intelligence, and malicious attacks on smart grids have also increased year by year. The wrong data injection attack launched by the attacker will destroy the integrity of the data by changing the data of the sensor and controller, which will lead to the wrong decision of the control system and even paralyze the power transmission network. This paper uses the measured values of smart grid sensors as samples, analyzes the attack vectors maliciously injected by attackers and the statistical characteristics of system data, and proposes a false data injection attack detection strategy. It is considered that the measured values of sensors have spatial distribution characteristics, the Gaussian mixture model of grid node feature vectors is obtained by training sample values, the test measurement values are input into the Gaussian mixture model, and the knowledge of clustering is used to detect whether the power grid is malicious data attacks. The power supplies of IEEE-18 and IEEE-30 simulation systems was tested, and the influence of the system statistical measurement characteristics on the detection accuracy was analyzed. The results show that the proposed strategy has better detection performance than the support vector machine method.

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Banking, defence applications and cryptosystems often demand security features, including cryptography, tamper resistance, stealth, and etc., by means of hardware approaches and/or software approaches to prevent data leakages. The hardware physical attacks or commonly known as side channel attacks have been employed to extract the secret keys of the encrypted algorithms implemented in hardware devices by analyzing their physical parameters such as power dissipation, electromagnetic interference and timing information. Altered functions or unauthorized modules may be added to the circuit design during the shipping and manufacturing process, bringing in security threats to the deployed systems. In this presentation, we will discuss hardware assurance from both device level and circuit level, and present how machine learning techniques can be utilized. At the device level, we will first provide an overview of the different cryptography algorithms and present the side channel attacks, particularly the powerful Correlation Power Analysis (CPA) and Correlation Electromagnetic Analysis (CEMA) with a leakage model that can be used to reveal the secret keys of the cryptosystems. We will then discuss several countermeasure techniques and present how highly secured microchips can be designed based on these techniques. At the circuit level, we will provide an overview of manufactured IC circuit analysis through invasive IC delayering and imaging. We then present several machine learning techniques that can be efficiently applied to the retrieval of circuit contact points and connections for further netlist/functional analysis.

Most organizations use web applications for sharing resources and communication via the internet and information security is one of the biggest concerns in most organizations. Web applications are becoming vulnerable to threats and malicious attacks every day, which lead to violation of confidentiality, integrity, and availability of information assets.We have proposed and implemented a new automated tool for the identification and mitigation of Cross-Site Request Forgery (CSRF) vulnerability. A secret token pattern based has been used in the automated tool, which applies effective security mechanism on PHP based web applications, without damaging the content and its functionalities, where the authenticated users can perform web activities securely.

In this paper, we introduce a method for visualizing and classifying malware binaries. A malware binary consists of a series of data points of compiled machine codes that represent programming components. The occurrence and recurrence behavior of these components is determined by the common tasks malware samples in a particular family carry out. Thus, we view a malware binary as a series of emissions generated by an underlying stochastic process and use recurrence plots to transform malware binaries into two-dimensional texture images. We observe that recurrence plot-based malware images have significant visual similarities within the same family and are different from samples in other families. We apply deep CNN classifiers to classify malware samples. The proposed approach does not require creating malware signature or manual feature engineering. Our preliminary experimental results show that the proposed malware representation leads to a higher and more stable accuracy in comparison to directly transforming malware binaries to gray-scale images.

Carefully constructed cyber-attacks directly influence the data integrity and the operational functionality of the smart energy grid. In this paper, we have explored the data integrity attack behaviour in a wide-area sensor-enabled IIoT-SCADA system. We have demonstrated that an intelligent cyber-attacker can inject false information through the sensor devices that may remain stealthy in the traditional detection module and corrupt estimated system states at the utility control centres. Next, to protect the operation, we defined a set of critical measurements that need to be protected for the resilient operation of the grid. Finally, we placed the measurement units using an optimal allocation strategy by ensuring that a limited number of nodes are protected against the attack while the system observability is satisfied. Under such scenarios, a wide range of experiments has been conducted to evaluate the performance considering IEEE 14-bus, 24 bus-reliability test system, 85-bus, 141-bus and 145-bus test systems. Results show that by ensuring the protection of around 25% of the total nodes, the IIoT-SCADA enabled energy grid can be protected against injection attacks while observability of the network is well-maintained.

The virtualization technology in cloud environment brings some data and privacy security issues to users. Aiming at the problems of virtual machines singleness, homogeneity and static state in cloud environment, a negative feedback dynamic scheduling algorithm is proposed. This algorithm is based on mimic defense and creates multiple virtual machines to complete user request services together through negative feedback control mechanism which can achieve real-time monitor of the running state of virtual machines. When virtual machines state is found to be inconsistent, this algorithm will dynamically change its execution environment, resulting in the attacker's information collection and vulnerability exploitation process being disrupting. Experiments show that the algorithm can better solve security threats caused by the singleness, homogeneity and static state of virtual machines in the cloud, and improve security and reliability of cloud users.

mHealth apps have a vital role in facilitation of human health management. Users have to enter sensitive health related information in these apps to fully utilize their functionality. Unauthorized sharing of sensitive health information is undesirable by the users. mHealth apps also collect data other than that required for their functionality like surfing behavior of a user or hardware details of devices used. mHealth software and their developers also share such data with third parties for reasons other than medical support provision to the user, like advertisements of medicine and health insurance plans. Existence of a comprehensive and easy to understand data privacy policy, on user data acquisition, sharing and management is a salient requirement of modern user privacy protection demands. Readability is one parameter by which ease of understanding of privacy policy is determined. In this research, privacy policies of 27 free Android, medical apps are analyzed. Apps having user rating of 4.0 and downloads of 1 Million or more are included in data set of this research.RGL, Flesch-Kincaid Reading Grade Level, SMOG, Gunning Fox, Word Count, and Flesch Reading Ease of privacy policies are calculated. Average Reading Grade Level of privacy policies is 8.5. It is slightly greater than average adult RGL in the US. Free mHealth apps have a large number of users in other, less educated parts of the World. Privacy policies with an average RGL of 8.5 may be difficult to comprehend in less educated populations.

At present, when the device management application programs the devices (such as mobile terminals, Internet of things terminals and devices, etc.), buffer overflow will inevitably occur due to the defects of filter input condition setting, variable type conversion error, logical judgment error, pointer reference error and so on. For this kind of software and its running environment, it is difficult to reduce the false positive rate and false negative rate with traditional static detection method for buffer overflow vulnerability, while the coverage rate of dynamic detection method is still insufficient and it is difficult to achieve full automation. In view of this, this paper proposes an automatic dynamic detection method based on boundary testing, which has complete test data set and full coverage of defects. With this method, the input test points of the software system under test are automatically traversed, and each input test point is analyzed automatically to generate complete test data; driven by the above complete test data, the software under test runs automatically, in which the embedded dynamic detection code automatically judges the conditions of overflow occurrence, and returns the overflow information including the location of the error code before the overflow really occurs. Because the overflow can be located accurately without real overflow occurrence, this method can ensure the normal detection of the next input test point, thus ensuring the continuity of the whole automatic detection process and the full coverage of buffer overflow detection. The test results show that all the indexes meet the requirements of the method and design.

Vehicular Social Networks (VSNs) are expected to become a reality soon, where commuters having common interests in the virtual community of vehicles, drivers, passengers can share information, both about road conditions and their surroundings. This will improve transportation efficiency and public safety. However, social networking exposes vehicles to different kinds of cyber-attacks. This concern can be addressed through an efficient and secure key management framework. This study presents a Secure and Transparent Public-key Management (ST-PKMS) based on blockchain and notary system, but it addresses security and privacy challenges specific to VSNs. ST-PKMS significantly enhances the efficiency and trustworthiness of mutual authentication. In ST-PKMS, each vehicle has multiple short-lived anonymous public-keys, which are recorded on the blockchain platform. However, public-keys get activated only when a notary system notarizes it, and clients accept only notarized public-keys during mutual authentication. Compromised vehicles can be effectively removed from the VSNs by blocking notarization of their public-keys; thus, the need to distribute Certificate Revocation List (CRL) is eliminated in the proposed scheme. ST-PKMS ensures transparency, security, privacy, and availability, even in the face of an active adversary. The simulation and evaluation results show that the ST-PKMS meets real-time performance requirements, and it is cost-effective in terms of scalability, delay, and communication overhead.

A large amount of malware families use the domain generation algorithms (DGA) to randomly generate a large amount of domain names. It is a good way to bypass conventional blacklists of domain names, because we cannot predict which of the randomly generated domain names are selected for command and control (C&C) communications. An effective approach for detecting known DGA families is to investigate the malware with reverse engineering to find the adopted generation algorithms. As reverse engineering cannot handle the variants of DGA families, some researches leverage supervised learning to find new variants. However, the explainability of supervised learning is low and cannot find previously unseen DGA families. In this paper, we propose a graph-based semi-supervised learning scheme to track the evolution of known DGA families and find previously unseen DGA families. With a domain relation graph, we can clearly figure out how new variants relate to known DGA domain names, which induces better explainability. We deployed the proposed scheme on real network scenarios and show that the proposed scheme can not only comprehensively and precisely find known DGA families, but also can find new DGA families which have not seen before.

Machine learning (ML) models are now a key component for many applications. However, machine learning based systems (MLBSs), those systems that incorporate them, have proven vulnerable to various new attacks as a result. Currently, there exists no systematic process for eliciting security requirements for MLBSs that incorporates the identification of adversarial machine learning (AML) threats with those of a traditional non-MLBS. In this research study, we explore the applicability of traditional threat modeling and existing attack libraries in addressing MLBS security in the requirements phase. Using an example MLBS, we examined the applicability of 1) DFD and STRIDE in enumerating AML threats; 2) Microsoft SDL AI/ML Bug Bar in ranking the impact of the identified threats; and 3) the Microsoft AML attack library in eliciting threat mitigations to MLBSs. Such a method has the potential to assist team members, even with only domain specific knowledge, to collaboratively mitigate MLBS threats.

Over recent years, we have seen a significant rise in popularity of autonomous vehicle. Several researches have shown the severity of security threats that autonomous vehicles face -for example, Miller and Valasek (2015) were able to remotely take complete control over a 2014 Jeep Cherokee in a so called "Jeephack" [1]. This paper analyses the threats that the Electrical and Electronic (E/E) architecture of an autonomous vehicle has to face and rank those threats by severity. To achieve this, the Microsoft's STRIDE threat analysis technique was applied and 13 threats were identified. These are sorted by their Common Vulnerability Scoring System (CVSS) scores. Potential mitigation methods are then suggested for the five topmost severe threats.

In smart cities, data privacy and security issues of Electronic Health Record(EHR) are grabbing importance day by day as cyber attackers have identified the weaknesses of EHR platforms. Besides, health insurance companies interacting with the EHRs play a vital role in covering the whole or a part of the financial risks of a patient. Insurance companies have specific policies for which patients have to pay them. Sometimes the insurance policies can be altered by fraudulent entities. Another problem that patients face in smart cities is when they interact with a health organization, insurance company, or others, they have to prove their identity to each of the organizations/companies separately. Health organizations or insurance companies have to ensure they know with whom they are interacting. To build a platform where a patient's personal information and insurance policy are handled securely, we introduce an application of blockchain to solve the above-mentioned issues. In this paper, we present a solution for the healthcare system that will provide patient privacy and transparency towards the insurance policies incorporating blockchain. Privacy of the patient information will be provided using cryptographic tools.

Information security provides a set of mechanisms to be implemented in the organisation to protect the disclosure of data to the unauthorised person. Access control is the primary security component that allows the user to authorise the consumption of resources and data based on the predefined permissions. However, the access rules are static in nature, which does not adapt to the dynamic environment includes but not limited to healthcare, cloud computing, IoT, National Security and Intelligence Arena and multi-centric system. There is a need for an additional countermeasure in access decision that can adapt to those working conditions to assess the threats and to ensure privacy and security are maintained. Risk analysis is an act of measuring the threats to the system through various means such as, analysing the user behaviour, evaluating the user trust, and security policies. It is a modular component that can be integrated into the existing access control to predict the risk. This study presents the different techniques and approaches applied for risk analysis in access control. Based on the insights gained, this paper formulates the taxonomy of risk analysis and properties that will allow researchers to focus on areas that need to be improved and new features that could be beneficial to stakeholders.

Anomaly detection is one of the research hotspots in Bitcoin transaction data analysis. In view of the existing research that only considers the transaction as an isolated node when extracting features, but has not yet used the network structure to dig deep into the node information, a bitcoin abnormal transaction detection method that combines the node’s own features and the neighborhood features is proposed. Based on the formation mechanism of the interactive relationship in the transaction network, first of all, according to a certain path selection probability, the features of the neighbohood nodes are extracted by way of random walk, and then the node’s own features and the neighboring features are fused to use the network structure to mine potential node information. Finally, an unsupervised detection algorithm is used to rank the transaction points on the constructed feature set to find abnormal transactions. Experimental results show that, compared with the existing feature extraction methods, feature fusion improves the ability to detect abnormal transactions.

Using the physical characteristics of the encryption device, an attacker can more easily obtain the key, which is called side-channel attack. Common side-channel attacks, such as simple power analysis (SPA) and differential power analysis (DPA), mainly focus on the statistical analysis of the data involved in the encryption algorithm, while there are relatively few studies on the Hamming weight of the addresses. Therefore, a new method of address-based Hamming weight analysis, address collision attack, is proposed in this research. The collision attack method (CA) and support vector machines algorithm (SVM) are used for analysis, meanwhile, the scalar multiplication implemented by protected address-bit DPA (ADPA) can be attack on the ChipWhisperer-Pro CW1200.

The following topics are dealt with: authorisation; data privacy; mobile computing; security of data; cryptography; Internet of Things; message authentication; invasive software; Android (operating system); vectors.

Due to the rapid growth of using Internet of Things (IoT) devices in the daily life, the need to achieve an acceptable level of security and privacy according to the real security risks for these devices is rising. Security risks may include privacy threats like gaining sensitive information from a device, and authentication problems from counterfeit or cloned devices. It becomes more challenging to add strong security features to extremely constrained devices compared to battery operated devices that have more computational and storage capabilities. We propose a novel application specific instruction-set architecture that allows flexibility on many design levels and achieves the required security level for the Electronic Product Code (EPC) passive Radio Frequency Identification (RFID) tag device. Our solution moves a major design effort from hardware to software, which largely reduces the final unit cost. The proposed architecture can be implemented with 4,662 gate equivalent units (GEs) for 65 nm CMOS technology excluding the memory and the cryptographic units. The synthesis results fulfill the requirements of extremely constrained devices and allow the inclusion of cryptographic units into the datapath of the proposed application-specific instruction set processor (ASIP).

Data privacy has been an important area of research in recent years. Dataset often consists of sensitive data fields, exposure of which may jeopardize interests of individuals associated with the data. In order to resolve this issue, privacy techniques can be used to hinder the identification of a person through anonymization of the sensitive data in the dataset to protect sensitive information, while the anonymized dataset can be used by the third parties for analysis purposes without obstruction. In this research, we investigated a privacy technique, k-anonymity for different values of on different number columns of the dataset. Next, the information loss due to k-anonymity is computed. The anonymized files go through the classification process by some machine-learning algorithms i.e., Naive Bayes, J48 and neural network in order to check a balance between data anonymity and data utility. Based on the classification accuracy, the optimal values of and are obtained, and thus, the optimal and can be used for k-anonymity algorithm to anonymize optimal number of columns of the dataset.

Internet of Medical Things (IoMT) is the connection between medical devices and information systems to share, collect, process, store, and integrate patient and health data using network technologies. X-Rays, MR, MRI, and CT scans are the most frequently used patient medical image data. These images usually include patient information in one of the corners of the image. In this research work, to protect patient information, a new robust and secure watermarking algorithm developed for a selected region of interest (ROI) of medical images. First ROI selected from the medical image, then selected part divided equal blocks and applied Discrete Cosine Transformation (DCT) algorithm to embed a watermark into the selected coefficients. Several geometric and removal attacks are applied to the watermarked multimedia element such as lossy image compression, the addition of Gaussian noise, denoising, filtering, median filtering, sharpening, contrast enhancement, JPEG compression, and rotation. Experimental results show very promising results in PSNR and similarity ratio (SR) values after blocked DCT (B-DCT) based embedding algorithm against the Discrete Wavelet Transformation (DWT), Least Significant Bits (LSB) and DCT algorithms.

Security has become the vital component of today's technology. People wish to safeguard their valuable items in bank lockers. With growing technology most of the banks have replaced the manual lockers by digital lockers. Even though there are numerous biometric approaches, these are not robust. In this work we propose a new approach for personal biometric identification based on features extracted from ECG.

An air-gapped computer is physically isolated from unsecured networks to guarantee effective protection against data exfiltration. Due to air gaps, unauthorized data transfer seems impossible over legitimate communication channels, but in reality many so-called physical covert channels can be constructed to allow data exfiltration across the air gaps. Most of such covert channels are very slow and often require certain strict conditions to work (e.g., no physical obstacles between the sender and the receiver). In this paper, we introduce a new physical covert channel named BitJabber that is extremely fast and strong enough to even penetrate concrete walls. We show that this covert channel can be easily created by an unprivileged sender running on a victim’s computer. Specifically, the sender constructs the channel by using only memory accesses to modulate the electromagnetic (EM) signals generated by the DRAM clock. While possessing a very high bandwidth (up to 300,000 bps), this new covert channel is also very reliable (less than 1% error rate). More importantly, this covert channel can enable data exfiltration from an air-gapped computer enclosed in a room with thick concrete walls up to 15 cm.