Biblio

Conventional Security Systems are improving with the advancement of Internet of Things (IoT) based technology. For better security, in addition to the currently available technology, surveillance systems are used. In this research, a Smart Surveillance System with machine-learning capabilities is designed to detect security breaches and it will resolve safety concerns. Machine learning algorithms are implemented to detect intruders as well as suspicious activities. Enery efficiency is the major concern for constant monitoring systems. As a result, the designed system focuses on power consumption by calibrating the system so that it can work on bare minimum power and additionally provides the required output. Fire sensor has also been integrated to detect fire for safety purposes. By adding upon the security infrastructure, next-generation smart surveillance systems can be created for a safe future. The developed system contains the necessary tools to recognize intruders by face recognition. Also using the ambient sensors (PIR sensor, fire detecting sensor), a secure environment is provided during working and non-working hours. The system shows high accuracy in human & flame detection. A more reliable security system can be created with the further development of this research.

With the explosion of Industry 4.0, industrial facilities and critical infrastructures are transforming into “smart” systems that dynamically adapt to external events. The result is an ecosystem of heterogeneous physical and cyber components, such as programmable logic controllers, which are more and more exposed to cyber-physical attacks, i.e., security breaches in cyberspace that adversely affect the physical processes at the core of industrial control systems. We apply runtime enforcement techniques, based on an ad-hoc sub-class of Ligatti et al.'s edit automata, to enforce specification compliance in networks of potentially compromised controllers, formalised in Hennessy and Regan's Timed Process Language. We define a synthesis algorithm that, given an alphabet P of observable actions and an enforceable regular expression e capturing a timed property for controllers, returns a monitor that enforces the property e during the execution of any (potentially corrupted) controller with alphabet P and complying with the property e. Our monitors correct and suppress incorrect actions coming from corrupted controllers and emit actions in full autonomy when the controller under scrutiny is not able to do so in a correct manner. Besides classical properties, such as transparency and soundness, the proposed enforcement ensures non-obvious properties, such as polynomial complexity of the synthesis, deadlock- and diverge-freedom of monitored controllers, together with scalability when dealing with networks of controllers.

Industrial control system (ICS) denotes a system consisting of actuators, control stations, and network that manages processes and functions in an industrial setting. The ICS community faces two major problems to keep pace with the broader trends of Industry 4.0: (1) a data rich, information poor (DRIP) syndrome, and (2) risk of financial and safety harms due to security breaches. In this paper, we propose a private cloud in the loop ICS architecture for real-time analytics that can bridge the gap between low data utilization and security hardening.

In recent years, almost all the real-world operations are transferred to cyber world and these market computers connect with each other via Internet. As a result of this, there is an increasing number of security breaches of the networks, whose admins cannot protect their networks from the all types of attacks. Although most of these attacks can be prevented with the use of firewalls, encryption mechanisms, access controls and some password protections mechanisms; due to the emergence of new type of attacks, a dynamic intrusion detection mechanism is always needed in the information security market. To enable the dynamicity of the Intrusion Detection System (IDS), it should be updated by using a modern learning mechanism. Neural Network approach is one of the mostly preferred algorithms for training the system. However, with the increasing power of parallel computing and use of big data for training, as a new concept, deep learning has been used in many of the modern real-world problems. Therefore, in this paper, we have proposed an IDS system which uses GPU powered Deep Learning Algorithms. The experimental results are collected on mostly preferred dataset KDD99 and it showed that use of GPU speed up training time up to 6.48 times depending on the number of the hidden layers and nodes in them. Additionally, we compare the different optimizers to enlighten the researcher to select the best one for their ongoing or future research.

Network security is an important issue in today's world with existence of network systems that communicate data and information about all aspects of our life, work and business. Network security is an important issue with connected networks and data communication between organisations of that specialized in different areas. Network security engineers spend a considerable amount of time to investigate network for security breaches and to enhance the security of their networks and data communications on their networks. They use Attack Graphs (AGs) which are graphical representation of networks to assist them in analysing large networks. With increase size of networks and their complexity, the use of attack graphs alone does not provide the necessary risk analysis and assessment facilities. There is a need for automated intelligent systems such as multiagent systems to assist in analysing, assessing and testing networks. Network systems changes with the increase in the size of organisation and connectivity of network of organisations based on the business needs or organisational or governmental rules and regulations. In this paper a multi-agent system is developed assist in analysing interconnected network to identify security risks. The multi-agent system is capable of security network analysis to identify paths using an attack graph of the network under consideration to protect network systems, as the networks grow and change, against possible attacks. The multiagent system uses a model developed by Mohammadian [3] for converting AGs to Fuzzy Cognitive Maps (FCMs) to identify attack paths from attack graphs and perform security risk analysis. In this paper a novel decision-making approach using FCMs is employed.

As more devices become connected to the internet and new technologies emerge to connect them, security must keep up to protect data during transmission and at rest. Several instances of security breaches have forced many companies to investigate the effectiveness of their security measures. In this paper, we discuss different methodologies for protecting data as it relates to wireless sensor networks (WSNs). Data collected from these sensors range in type from location data of an individual to surveillance for military applications. We propose a solution that protects the location of the base station and the nodes while transmitting data.

Multi-tenant cloud networks have various security and monitoring service functions (SFs) that constitute a service function chain (SFC) between two endpoints. SF rule ordering overlaps and policy conflicts can cause increased latency, service disruption and security breaches in cloud networks. Software Defined Network (SDN) based Network Function Virtualization (NFV) has emerged as a solution that allows dynamic SFC composition and traffic steering in a cloud network. We propose an SDN enabled Universal Policy Checking (SUPC) framework, to provide 1) Flow Composition and Ordering by translating various SF rules into the OpenFlow format. This ensures elimination of redundant rules and policy compliance in SFC. 2) Flow conflict analysis to identify conflicts in header space and actions between various SF rules. Our results show a significant reduction in SF rules on composition. Additionally, our conflict checking mechanism was able to identify several rule conflicts that pose security, efficiency, and service availability issues in the cloud network.

The majority of security breaches in cloud infrastructure in recent years are caused by human errors and misconfigured resources. Novel security models are imperative to overcome these issues. Such models must be customer-centric, continuous, not focused on traditional security paradigms like intrusion detection and adopt proactive techniques. Thus, this paper proposes CloudStrike, a cloud security system that implements the principles of Chaos Engineering to enable the aforementioned properties. Chaos Engineering is an emerging discipline employed to prevent non-security failures in cloud infrastructure via Fault Injection Testing techniques. CloudStrike employs similar techniques with a focus on injecting failures that impact security i.e. integrity, confidentiality and availability. Essentially, CloudStrike leverages the relationship between dependability and security models. Preliminary experiments provide insightful and prospective results.

The usage of small drones/UAVs has significantly increased recently. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. The similarity in the appearance of small drone and birds in complex background makes it challenging to detect drones in surveillance videos. This paper addresses the challenge of detecting small drones in surveillance videos using popular and advanced deep learning-based object detection methods. Different CNN-based architectures such as ResNet-101 and Inception with Faster-RCNN, as well as Single Shot Detector (SSD) model was used for experiments. Due to sparse data available for experiments, pre-trained models were used while training the CNNs using transfer learning. Best results were obtained from experiments using Faster-RCNN with the base architecture of ResNet-101. Experimental analysis on different CNN architectures is presented in the paper, along with the visual analysis of the test dataset.

The increase of the digitalization taking place in various industrial domains is leading developers towards the design and implementation of more and more complex networked control systems (NCS) supported by Wireless Sensor Networks (WSN). This naturally raises new challenges for the current WSN technology, namely in what concerns improved guarantees of technical aspects such as real-time communications together with safe and secure transmissions. Notably, in what concerns security aspects, several cryptographic protocols have been proposed. Since the design of these protocols is usually error-prone, security breaches can still be exposed and MALICIOUSly exploited unless they are rigorously analyzed and verified. In this paper we formally verify, using ProVerif, three cryptographic protocols used in WSN, regarding the security properties of secrecy and authenticity. The security analysis performed in this paper is more robust than the ones performed in related work. Our contributions involve analyzing protocols that were modeled considering an unbounded number of participants and actions, and also the use of a hierarchical system to classify the authenticity results. Our verification shows that the three analyzed protocols guarantee secrecy, but can only provide authenticity in specific scenarios.

Computer security has gained more and more attention in a public over the last years, since computer systems are suffering from significant and increasing security threats that cause security breaches by exploiting software vulnerabilities. The most efficient way to ensure the system security is to patch the vulnerable system before a malicious attack occurs. Besides the commonly-used push-type patch management, the pull-type patch management is also adopted. The main issues in the pull-type patch management are two-fold; when to check the vulnerability information and when to apply a patch? This paper considers the security patch management for a virtual machine (VM) based intrusion tolerant system (ITS), where the system undergoes the patch management with a periodic vulnerability checking strategy, and evaluates the system security from the availability aspect. A composite stochastic reward net (SRN) model is applied to capture the attack behavior of adversary and the defense behaviors of system. Two availability measures; interval availability and point-wise availability are formulated to quantify the system security via phase expansion. The proposed approach and metrics not only enable us to quantitatively assess the system security, but also provide insights on the patch management. In numerical experiments, we evaluate effects of the intrusion rate and the number of vulnerability checking on the system security.

Github Gist is a service provided by Github which is used by developers to share code snippets. While sharing, developers may inadvertently introduce security smells in code snippets as well, such as hard-coded passwords. Security smells are recurrent coding patterns that are indicative of security weaknesses, which could potentially lead to security breaches. The goal of this paper is to help software practitioners avoid insecure coding practices through an empirical study of security smells in publicly-available GitHub Gists. Through static analysis, we found 13 types of security smells with 4,403 occurrences in 5,822 publicly-available Python Gists. 1,817 of those Gists, which is around 31%, have at least one security smell including 689 instances of hard-coded secrets. We also found no significance relation between the presence of these security smells and the reputation of the Gist author. Based on our findings, we advocate for increased awareness and rigorous code review efforts related to software security for Github Gists so that propagation of insecure coding practices are mitigated.

Practitioners use infrastructure as code (IaC) scripts to provision servers and development environments. While developing IaC scripts, practitioners may inadvertently introduce security smells. Security smells are recurring coding patterns that are indicative of security weakness and can potentially lead to security breaches. The goal of this paper is to help practitioners avoid insecure coding practices while developing infrastructure as code (IaC) scripts through an empirical study of security smells in IaC scripts. We apply qualitative analysis on 1,726 IaC scripts to identify seven security smells. Next, we implement and validate a static analysis tool called Security Linter for Infrastructure as Code scripts (SLIC) to identify the occurrence of each smell in 15,232 IaC scripts collected from 293 open source repositories. We identify 21,201 occurrences of security smells that include 1,326 occurrences of hard-coded passwords. We submitted bug reports for 1,000 randomly-selected security smell occurrences. We obtain 212 responses to these bug reports, of which 148 occurrences were accepted by the development teams to be fixed. We observe security smells can have a long lifetime, e.g., a hard-coded secret can persist for as long as 98 months, with a median lifetime of 20 months.

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

The pervasive use of databases for the storage of critical and sensitive information in many organizations has led to an increase in the rate at which databases are exploited in computer crimes. While there are several techniques and tools available for database forensic analysis, such tools usually assume an apriori database preparation, such as relying on tamper-detection software to already be in place and the use of detailed logging. Further, such tools are built-in and thus can be compromised or corrupted along with the database itself. In practice, investigators need forensic and security audit tools that work on poorlyconfigured systems and make no assumptions about the extent of damage or malicious hacking in a database.In this paper, we present our database forensics methods, which are capable of examining database content from a storage (disk or RAM) image without using any log or file system metadata. We describe how these methods can be used to detect security breaches in an untrusted environment where the security threat arose from a privileged user (or someone who has obtained such privileges). Finally, we argue that a comprehensive and independent audit framework is necessary in order to detect and counteract threats in an environment where the security breach originates from an administrator (either at database or operating system level).

Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is an important human-machine distinction technology for website to prevent the automatic malicious program attack. CAPTCHA recognition studies can find security breaches in CAPTCHA, improve CAPTCHA technology, it can also promote the technologies of license plate recognition and handwriting recognition. This paper proposed a method based on Convolutional Neural Network (CNN) model to identify CAPTCHA and avoid the traditional image processing technology such as location and segmentation. The adaptive learning rate is introduced to accelerate the convergence rate of the model, and the problem of over-fitting and local optimal solution has been solved. The multi task joint training model is used to improve the accuracy and generalization ability of model recognition. The experimental results show that the model has a good recognition effect on CAPTCHA with background noise and character adhesion distortion.

A significant segment of the Internet of Things (IoT) is the resource constrained Low Power and Lossy Networks (LLNs). The communication protocol used in LLNs is 6LOWPAN (IPv6 over Low-power Wireless Personal Area Network) which makes use of RPL (IPv6 Routing Protocol over Low power and Lossy network) as its routing protocol. In recent times, several security breaches in IoT networks occurred by targeting routers to instigate various DDoS (Distributed Denial of Service) attacks. Hence, routing security has become an important problem in securing the IoT environment. Though RPL meets all the routing requirements of LLNs, it is important to perform a holistic security assessment of RPL as it is susceptible to many security attacks. An important attribute of RPL is its rank property. The rank property defines the placement of sensor nodes in the RPL DODAG (Destination Oriented Directed Acyclic Graphs) based on an Objective Function. Examples of Objective Functions include Expected Transmission Count, Packet Delivery Rate etc. Rank property assists in routing path optimization, reducing control overhead and maintaining a loop free topology through rank based data path validation. In this paper, we investigate the vulnerabilities of the rank property of RPL by constructing an Attack Graph. For the construction of the Attack Graph we analyzed all the possible threats associated with rank property. Through our investigation we found that violation of protocols related to rank property results in several RPL attacks causing topological sub-optimization, topological isolation, resource consumption and traffic disruption. Routing security essentially comprises mechanisms to ensure correct implementation of the routing protocol. In this paper, we also present some observations which can be used to devise mechanisms to prevent the exploitation of the vulnerabilities of the rank property.

Recent years have witnessed the trend of increasingly relying on distributed infrastructures. This increased the number of reported incidents of security breaches compromising users' privacy, where third parties massively collect, process and manage users' personal data. Towards these security and privacy challenges, we combine hierarchical identity based cryptographic mechanisms with emerging blockchain infrastructures and propose a blockchain-based data usage auditing architecture ensuring availability and accountability in a privacy-preserving fashion. Our approach relies on the use of auditable contracts deployed in blockchain infrastructures. Thus, it offers transparent and controlled data access, sharing and processing, so that unauthorized users or untrusted servers cannot process data without client's authorization. Moreover, based on cryptographic mechanisms, our solution preserves privacy of data owners and ensures secrecy for shared data with multiple service providers. It also provides auditing authorities with tamper-proof evidences for data usage compliance.

Policy design is an important part of software development. As security breaches increase in variety, designing a security policy that addresses all potential breaches becomes a nontrivial task. A complete security policy would specify rules to prevent breaches. Systematically determining which, if any, policy clause has been violated by a reported breach is a means for identifying gaps in a policy. Our research goal is to help analysts measure the gaps between security policies and reported breaches by developing a systematic process based on semantic reasoning. We propose SEMAVER, a framework for determining coverage of breaches by policies via comparison of individual policy clauses and breach descriptions. We represent a security policy as a set of norms. Norms (commitments, authorizations, and prohibitions) describe expected behaviors of users, and formalize who is accountable to whom and for what. A breach corresponds to a norm violation. We develop a semantic similarity metric for pairwise comparison between the norm that represents a policy clause and the norm that has been violated by a reported breach. We use the US Health Insurance Portability and Accountability Act (HIPAA) as a case study. Our investigation of a subset of the breaches reported by the US Department of Health and Human Services (HHS) reveals the gaps between HIPAA and reported breaches, leading to a coverage of 65%. Additionally, our classification of the 1,577 HHS breaches shows that 44% of the breaches are accidental misuses and 56% are malicious misuses. We find that HIPAA's gaps regarding accidental misuses are significantly larger than its gaps regarding malicious misuses.

The rapid digitalisation of the hospitality industry over recent years has brought forth many new points of attack for consideration. The hasty implementation of these systems has created a reality in which businesses are using the technical solutions, but employees have very little awareness when it comes to the threats and implications that they might present. This gap in awareness is further compounded by the existence of preestablished, often rigid, cultures that drive how hospitality businesses operate. Potential attackers are recognising this and the last two years have seen a huge increase in cyber-attacks within the sector.Attempts at addressing the increasing threats have taken the form of technical solutions such as encryption, access control, CCTV, etc. However, a high majority of security breaches can be directly attributed to human error. It is therefore necessary that measures for addressing the rising trend of cyber-attacks go beyond just providing technical solutions and make provision for educating employees about how to address the human elements of security. Inculcating security awareness amongst hospitality employees will provide a foundation upon which a culture of security can be created to promote the seamless and secured interaction of hotel users and technology.One way that the hospitality industry has tried to solve the awareness issue is through their current paper-based training. This is unengaging, expensive and presents limited ways to deploy, monitor and evaluate the impact and effectiveness of the content. This leads to cycles of constant training, making it very hard to initiate awareness, particularly within those on minimum waged, short-term job roles.This paper presents a structured approach for eliciting industry requirement for developing and implementing an immersive Cyber Security Awareness learning platform. It used a series of over 40 interviews and threat analysis of the hospitality industry to identify the requirements fo- designing and implementing cyber security program which encourage engagement through a cycle of reward and recognition. In particular, the need for the use of gamification elements to provide an engaging but gentle way of educating those with little or no desire to learn was identified and implemented. Also presented is a method for guiding and monitoring the impact of their employee's progress through the learning management system whilst monitoring the levels of engagement and positive impact the training is having on the business.

Given the growing sophistication of cyber attacks, designing a perfectly secure system is not generally possible. Quantitative security metrics are thus needed to measure and compare the relative security of proposed security designs and policies. Since the investigation of security breaches has shown a strong impact of human errors, ignoring the human user in computing these metrics can lead to misleading results. Despite this, and although security researchers have long observed the impact of human behavior on system security, few improvements have been made in designing systems that are resilient to the uncertainties in how humans interact with a cyber system. In this work, we develop an approach for including models of user behavior, emanating from the fields of social sciences and psychology, in the modeling of systems intended to be secure. We then illustrate how one of these models, namely general deterrence theory, can be used to study the effectiveness of the password security requirements policy and the frequency of security audits in a typical organization. Finally, we discuss the many challenges that arise when adopting such a modeling approach, and then present our recommendations for future work.

This article is about study of honeypots. In this work, we use some honeypot sensors deployment and analysis to identify, currently, what are the main attacks and security breaches explored by attackers to compromise systems. For example, a common server or service exposed to the Internet can receive a million of hits per day, but sometimes would not be easy to identify the difference between legitimate access and an attacker trying to scan, and then, interrupt the service. Finally, the objective of this research is to investigate the efficiency of the honeypots sensors to identify possible safety gaps and new ways of attacks. This research aims to propose some guidelines to avoid or minimize the damage caused by these attacks in real systems.

This research paper identifies security issues; especially energy based security attacks and enhances security of the system. It is very essential to consider Security of the system to be developed in the initial Phases of the software Cycle of Software Development (SDLC) as many billions of bucks are drained owing to security flaws in software caused due to improper or no security process. Security breaches that occur on software system are in umpteen numbers. Scientific Literature propose many solutions to overcome security issues, all security mechanisms are reactive in nature. In this paper new security solution is proposed that is proactive in nature especially for energy based denial of service attacks which is frequent in the recent past. Proposed solution is based on energy consumption by system known as energy points.

Security breaches and attacks are becoming a more critical and, simultaneously, a challenging problems for many firms in networked supply chains. A game theory-based model is developed to investigate how interdependent feature of information security risk influence the optimal strategy of firms to invest in information security. The equilibrium levels of information security investment under non-cooperative game condition are compared with socially optimal solutions. The results show that the infectious risks often induce firms to invest inefficiently whereas trust risks lead to overinvest in information security. We also find that firm's investment may not necessarily monotonous changes with infectious risks and trust risks in a centralized case. Furthermore, relative to the socially efficient level, firms facing infectious risks may invest excessively depending on whether trust risks is large enough.

Most network traffic analysis applications are designed to discover malicious activity by only relying on high-level flow-based message properties. However, to detect security breaches that are specifically designed to target one network (e.g., Advanced Persistent Threats), deep packet inspection and anomaly detection are indispensible. In this paper, we focus on how we can support experts in discovering whether anomalies at message level imply a security risk at network level. In SNAPS (Semantic Network traffic Analysis through Projection and Selection), we provide a bottom-up pixel-oriented approach for network traffic analysis where the expert starts with low-level anomalies and iteratively gains insight in higher level events through the creation of multiple selections of interest in parallel. The tight integration between visualization and machine learning enables the expert to iteratively refine anomaly scores, making the approach suitable for both post-traffic analysis and online monitoring tasks. To illustrate the effectiveness of this approach, we present example explorations on two real-world data sets for the detection and understanding of potential Advanced Persistent Threats in progress.