Biblio

Air-gapped computers are hermetically isolated from the Internet to eliminate any means of information leakage. In this paper we present HOTSPOT - a new type of airgap crossing technique. Signals can be sent secretly from air-gapped computers to nearby smartphones and then on to the Internet - in the form of thermal pings. The thermal signals are generated by the CPUs and GPUs and intercepted by a nearby smartphone. We examine this covert channel and discuss other work in the field of air-gap covert communication channels. We present technical background and describe thermal sensing in modern smartphones. We implement a transmitter on the computer side and a receiver Android App on the smartphone side, and discuss the implementation details. We evaluate the covert channel and tested it in a typical work place. Our results show that it possible to send covert signals from air-gapped PCs to the attacker on the Internet through the thermal pings. We also propose countermeasures for this type of covert channel which has thus far been overlooked.

With the ever-increasing number of smart home devices, the issues related to these environments are also growing. With an ever-growing attack surface, there is no standard way to protect homes and their inhabitants from new threats. The inhabitants are rarely aware of the increased security threats that they are exposed to and how to manage them. To tackle this problem, we propose a solution based on segmented architectures similar to the ones used in industrial systems. In this approach, the smart home is segmented into various levels, which can broadly be categorised into an inner level and external level. The external level is protected by a firewall that checks the communication from/to the Internet to/from the external devices. The internal level is protected by an additional firewall that filters the information and the communications between the external and the internal devices. This segmentation guarantees a trusted environment among the entities of the internal network. In this paper, we propose an adaptive trust model that checks the behaviour of the entities and in case the entities violate trust rules they can be put in quarantine or banned from the network.

In this paper, we design NETHCF, a line-rate in-network system for filtering spoofed traffic. NETHCF leverages the opportunity provided by programmable switches to design a novel defense against spoofed IP traffic, and it is highly efficient and adaptive. One key challenge stems from the restrictions of the computational model and memory resources of programmable switches. We address this by decomposing the HCF system into two complementary components-one component for the data plane and another for the control plane. We also aggregate the IP-to-Hop-Count (IP2HC) mapping table for efficient memory usage, and design adaptive mechanisms to handle end-to-end routing changes, IP popularity changes, and network activity dynamics. We have built a prototype on a hardware Tofino switch, and our evaluation demonstrates that NETHCF can achieve line-rate and adaptive traffic filtering with low overheads.

IoT is evolving as a combination of interconnected devices over a particular network. In the proposed paper, we discuss about the security of IoT system in the wireless devices. IoT security is the platform in which the connected devices over the network are safeguarded over internet of things framework. Wireless devices play an eminent role in this kind of networks since most of the time they are connected to the internet. Accompanied by major users cannot ensure their end to end security in the IoT environment. However, connecting these devices over the internet via using IoT increases the chance of being prone to the serious issues that may affect the system and its data if they are not protected efficiently. In the proposed paper, the security of IoT in wireless devices will be enhanced by using ECC. Since the issues related to security are becoming common these days, an attempt has been made in this proposed paper to enhance the security of IoT networks by using ECC for wireless devices.

The characteristics of decentralization, tamper-resistance and transaction anonymity of blockchain can resolve effectively the problems in traditional intellectual property such as the difficulty of electronic obtaining for evidence, the high cost and low compensation when safeguarding the copyrights. Blockchain records the information through encryption algorithm, removes the third party, and stores the information in all nodes to prevent the information from being tampered with, so as to realize the protection of intellectual property. Starting from the bottom layer of blockchain, this paper expounds in detail the characteristics and the technical framework of blockchain. At the same time, according to the existing problems in transaction throughput, time delay and resource consumption of blockchain system, optimization mechanisms such as cross-chain and proof of stake are analyzed. Finally, combined with the characteristics of blockchain technology and existing application framework, this paper summarizes the existing problems in the industry and forecasts the development trend of intellectual property based on blockchain technology.

Increased use of secure chat and voice/ video apps has transformed the social life. While the benefits and facilitations are seemingly limitless, so are the asscoiacted vulnerabilities and threats. Besides ensuring confidentiality requirements for common users, known facts of non-readable contents over the network make these apps more attractive for criminals. Though access to contents of cryptograhically secure sessions is not possible, network forensics of secure apps can provide interesting information which can be of great help during criminal invetigations. In this paper, we presented a novel framework of profiling the secure chat and voice/ video calling apps which can be employed to extract hidden patterns about the app, information of involved parties, activities of chatting, voice/ video calls, status indications and notifications while having no information of communication protocol of the app and its security architecture. Signatures of any secure app can be developed though our framework and can become base of a large scale solution. Our methodology is considered very important for different cases of criminal investigations and bussiness intelligence solutions for service provider networks. Our results are applicable to any mobile platform of iOS, android and windows.

Software Engineering is a field of new challenges every day. With every passing day, new technologies emerge. There was an era of web Applications, but the time has changed and most of the web Applications are available as Mobile Applications as well. The Mobile Applications are either android based or iOS based. To deliver error free, secure and reliable Application, it is necessary to test the Applications properly. Software testing is a phase of software development life cycle, where we test an Application in all aspects. Nowadays different type of tools are available for testing an Application automatically but still we have too many challenges for applying test cases on a given Application. In this paper the authors will discuss the challenges of automation of test cases for a Mobile based payment Application.

This paper presents a detailed analysis of some relevant security features of iOS and Android -the two most popular operating systems for mobile devices- from the perspective of user privacy. In particular, permissions that can be modified at run time on these platforms are analyzed. Additionally, a framework is introduced for permission analysis, a hybrid mobile application that can run on both iOS and Android. The framework, which can be extended, places special emphasis on the relationship between the user's privacy and the permission system.

In this contemporary world, working on internet is a crucial task owing to the security threats in the network like intrusions, injections etc. To recognize and reduce these system attacks, analysts and academicians have introduced Intrusion Detection Systems (IDSs) with the various standards and applications. There are different types of Intrusion Detection Systems (IDS) arise to solve the attacks in various environments. Though IDS is more powerful, it produces the results on the abnormal behaviours said to be attacks with false positive and false negative rates which leads to inaccurate detection rate. The other problem is that, there are more number of attacks arising simultaneously with different behaviour being detected by the IDS with high false positive rates which spoils the strength and lifetime of the system, system's efficiency and fault tolerance. Complex Event Processing (CEP) plays a vital role in handling the alerts as events in real time environment which mainly helps to recognize and reduce the redundant alerts.CEP identifies and analyses relationships between events in real time, allowing the system to proactively take efficient actions to respond to specific alerts.In this study, the tendency of Complex Event Processing (CEP) over Intrusion Detection System (IDS) which offers effective handling of the alerts received from IDS in real time and the promotion of the better detection of the attacks are discussed. The merits and challenges of CEP over IDS described in this paper helps to understand and educate the IDS systems to focus on how to tackle the dynamic attacks and its alerts in real time.

In the information era, the size of network traffic is complex because of massive Internet-based services and rapid amounts of data. The more network traffic has enhanced, the more cyberattacks have dramatically increased. Therefore, cybersecurity intrusion detection has been a challenge in the current research area in recent years. The Intrusion detection system requires high-level protection and detects modern and complex attacks with more accuracy. Nowadays, big data analytics is the main key to solve marketing, security and privacy in an extremely competitive financial market and government. If a huge amount of stream data flows within a short period time, it is difficult to analyze real-time decision making. Performance analysis is extremely important for administrators and developers to avoid bottlenecks. The paper aims to reduce time-consuming by using Apache Kafka and Spark Streaming. Experiments on the UNSWNB-15 dataset indicate that the integration of Apache Kafka and Spark Streaming can perform better in terms of processing time and fault-tolerance on the huge amount of data. According to the results, the fault tolerance can be provided by the multiple brokers of Kafka and parallel recovery of Spark Streaming. And then, the multiple partitions of Apache Kafka increase the processing time in the integration of Apache Kafka and Spark Streaming.

In internet based communication, various types of attacks have been evolved. Hence, attacker easily breaches the securities. Traditional intrusion detection techniques to observe these attacks have failed and thus hefty systems are required to remove these attacks before they expose entire network. With the ability of artificial intelligence systems to adapt high computational speed, boost fault tolerance, and error resilience against noisy information, a hybrid particle swarm optimization(PSO) fuzzy rule based inference engine has been designed in this paper. The fuzzy logic based on degree of truth while the PSO algorithm based on population stochastic technique helps in learning from the scenario, thus their combination will increase the toughness of intrusion detection system. The proposed network intrusion detection system will be able to classify normal as well as anomalism behaviour in the network. DARPA-KDD99 dataset examined on this system to address the behaviour of each connection on network and compared with existing system. This approach improves the result on the basis of precision, recall and F1-score.

Media streaming has largely dominated the Internet traffic and the trend will keep increasing in the next years. To efficiently distribute the media content, Information-Centric Networking (ICN) has attracted many researchers. Since end users usually obtain content from indeterminate caches in ICN, the publisher cannot reinforce data security and access control depending on the caches. Hence, the ability of self-contained protection is important for the cached contents. Attribute-based encryption (ABE) is considered the preferred solution to achieve this goal. However, the existing ABE schemes usually have problems regarding efficiency. The exponentiation in key generation and pairing operation in decryption respectively increases linearly with the number of attributes involved, which make it costly. In this paper, we propose an efficient key-policy ABE with fast key generation and decryption (FKP-ABE). In the key generation, we get rid of exponentiation and only require multiplications/divisions for each attribute in the access policy. And in the decryption, we reduce the pairing operations to a constant number, no matter how many attributes are used. The efficiency analysis indicates that our scheme has better performance than the existing KP-ABE schemes. Finally, we present an implementation framework that incorporates the proposed FKP-ABE with the ICN architecture.

Conversational agents assist traditional teaching-learning instruments in proposing new designs for knowledge creation and learning analysis, across organizational environments. Means of building common educative background in both industry and academic fields become of interest for ensuring educational effectiveness and consistency. Such a context requires transferable practices and becomes the basis for the Agile adoption into Higher Education, at both curriculum and operational levels. The current work proposes a model for delivering Agile Scrum training through an assistive web-based conversational service, where analytics are collected to provide an overview on learners' knowledge path. Besides its specific applicability into Software Engineering (SE) industry, the model is to assist the academic SE curriculum. A user-acceptance test has been carried out among 200 undergraduate students and patterns of interaction have been depicted for 2 conversational strategies.

Cyber-Physical Systems (CPS) are playing important roles in the critical infrastructure now. A prominent family of CPSs are networked control systems in which the control and feedback signals are carried over computer networks like the Internet. Communication over insecure networks make system vulnerable to cyber attacks. In this article, we design an intrusion detection and compensation framework based on system/plant identification to fight covert attacks. We collect error statistics of the output estimation during the learning phase of system operation and after that, monitor the system behavior to see if it significantly deviates from the expected outputs. A compensating controller is further designed to intervene and replace the classic controller once the attack is detected. The proposed model is tested on a DC motor as the plant and is put against a deception signal amplification attack over the forward link. Simulation results show that the detection algorithm well detects the intrusion and the compensator is also successful in alleviating the attack effects.

Development of an attack-resilient smart grid depends heavily on the availability of a representative environment, such as a Cyber Physical Security (CPS) testbed, to accelerate the transition of state-of-the-art research work to industry deployment by experimental testing and validation. There is an ongoing initiative to develop an interconnected federated testbed to build advanced computing systems and integrated data sharing networks. In this paper, we present a distributed simulation for power system using federated testbed in the context of Wide Area Monitoring System (WAMS) cyber-physical security. In particular, we have applied the transmission line modeling (TLM) technique to split a first order two-bus system into two subsystems: source and load subsystems, which are running in geographically dispersed simulators, while exchanging system variables over the internet. We have leveraged the resources available at Iowa State University's Power Cyber Laboratory (ISU PCL) and the US Army Research Laboratory (US ARL) to perform the distributed simulation, emulate substation and control center networks, and further implement a data integrity attack and physical disturbances targeting WAMS application. Our experimental results reveal the computed wide-area network latency; and model validation errors. Further, we also discuss the high-level conceptual architecture, inspired by NASPInet, necessary for developing the CPS testbed federation.

Identity management is an essential cornerstone of securing online services. Service provisioning relies on correct and valid attributes of a digital identity. Therefore, the identity provider is a trusted third party with a specific trust requirement towards a verified attribute supply. This trust demand implies a significant dependency on users and service providers. We propose a novel attribute aggregation method to reduce the reliance on one identity provider. Trust in an attribute is modelled as a combined assurance of several identity providers based on probability distributions. We formally describe the proposed aggregation model. The resulting trust model is implemented in a gateway that is used for authentication with self-sovereign identity solutions. Thereby, we devise a service provider specific web of trust that constitutes an intermediate approach bridging a global hierarchical model and a locally decentralized peer to peer scheme.

With more than a trillion web pages, there is a plethora of content available for consumption. Search Engine queries invariably lead to overwhelming information, parts of it relevant and some others irrelevant. Often the information provided can be conflicting, ambiguous, and inconsistent contributing to the loss of credibility of the content. In the past, researchers have proposed approaches for credibility assessment and enumerated factors influencing the credibility of web pages. In this work, we detailed a WEBCred framework for automated genre-aware credibility assessment of web pages. We developed a tool based on the proposed framework to extract web page features instances and identify genre a web page belongs to while assessing it's Genre Credibility Score ( GCS). We validated our approach on `Information Security' dataset of 8,550 URLs with 171 features across 7 genres. The supervised learning algorithm, Gradient Boosted Decision Tree classified genres with 88.75% testing accuracy over 10 fold cross-validation, an improvement over the current benchmark. We also examined our approach on `Health' domain web pages and had comparable results. The calculated GCS correlated 69% with crowdsourced Web Of Trust ( WOT) score and 13% with algorithm based Alexa ranking across 5 Information security groups. This variance in correlation states that our GCS approach aligns with human way ( WOT) as compared to algorithmic way (Alexa) of web assessment in both the experiments.

Over the past few years, we have been witnessing the rise of misinformation on the Internet. People fall victims of fake news continuously, and contribute to their propagation knowingly or inadvertently. Many recent efforts seek to reduce the damage caused by fake news by identifying them automatically with artificial intelligence techniques, using signals from domain flag-lists, online social networks, etc. In this work, we present Check-It, a system that combines a variety of signals into a pipeline for fake news identification. Check-It is developed as a web browser plugin with the objective of efficient and timely fake news detection, while respecting user privacy. In this paper, we present the design, implementation and performance evaluation of Check-It. Experimental results show that it outperforms state-of-the-art methods on commonly-used datasets.

In the last few years, cryptocurrency mining has become more and more important on the Internet activity and nowadays is even having a noticeable impact on the global economy. This has motivated the emergence of a new malicious activity called cryptojacking, which consists of compromising other machines connected to the Internet and leverage their resources to mine cryptocurrencies. In this context, it is of particular interest for network administrators to detect possible cryptocurrency miners using network resources without permission. Currently, it is possible to detect them using IP address lists from known mining pools, processing information from DNS traffic, or directly performing Deep Packet Inspection (DPI) over all the traffic. However, all these methods are still ineffective to detect miners using unknown mining servers or result too expensive to be deployed in real-world networks with large traffic volume. In this paper, we present a machine learning-based method able to detect cryptocurrency miners using NetFlow/IPFIX network measurements. Our method does not require to inspect the packets' payload; as a result, it achieves cost-efficient miner detection with similar accuracy than DPI-based techniques.

Cryptojacking is the permissionless use of a target device to covertly mine cryptocurrencies. With cryptojacking attackers use malicious JavaScript codes to force web browsers into solving proof-of-work puzzles, thus making money by exploiting resources of the website visitors. To understand and counter such attacks, we systematically analyze the static, dynamic, and economic aspects of in-browser cryptojacking. For static analysis, we perform content-, currency-, and code-based categorization of cryptojacking samples to 1) measure their distribution across websites, 2) highlight their platform affinities, and 3) study their code complexities. We apply unsupervised learning to distinguish cryptojacking scripts from benign and other malicious JavaScript samples with 96.4% accuracy. For dynamic analysis, we analyze the effect of cryptojacking on critical system resources, such as CPU and battery usage. Additionally, we perform web browser fingerprinting to analyze the information exchange between the victim node and the dropzone cryptojacking server. We also build an analytical model to empirically evaluate the feasibility of cryptojacking as an alternative to online advertisement. Our results show a large negative profit and loss gap, indicating that the model is economically impractical. Finally, by leveraging insights from our analyses, we build countermeasures for in-browser cryptojacking that improve upon the existing remedies.

An emerging Internet business is residential proxy (RESIP) as a service, in which a provider utilizes the hosts within residential networks (in contrast to those running in a datacenter) to relay their customers' traffic, in an attempt to avoid server- side blocking and detection. With the prominent roles the services could play in the underground business world, little has been done to understand whether they are indeed involved in Cybercrimes and how they operate, due to the challenges in identifying their RESIPs, not to mention any in-depth analysis on them. In this paper, we report the first study on RESIPs, which sheds light on the behaviors and the ecosystem of these elusive gray services. Our research employed an infiltration framework, including our clients for RESIP services and the servers they visited, to detect 6 million RESIP IPs across 230+ countries and 52K+ ISPs. The observed addresses were analyzed and the hosts behind them were further fingerprinted using a new profiling system. Our effort led to several surprising findings about the RESIP services unknown before. Surprisingly, despite the providers' claim that the proxy hosts are willingly joined, many proxies run on likely compromised hosts including IoT devices. Through cross-matching the hosts we discovered and labeled PUP (potentially unwanted programs) logs provided by a leading IT company, we uncovered various illicit operations RESIP hosts performed, including illegal promotion, Fast fluxing, phishing, malware hosting, and others. We also reverse engi- neered RESIP services' internal infrastructures, uncovered their potential rebranding and reselling behaviors. Our research takes the first step toward understanding this new Internet service, contributing to the effective control of their security risks.

This paper presents the results of a qualitative study on discussions about two major law enforcement interventions against Dark Net Market (DNM) users extracted from relevant Reddit forums. We assess the impact of Operation Hyperion and Operation Bayonet (combined with the closure of the site Hansa) by analyzing posts and comments made by users of two Reddit forums created for the discussion of Dark Net Markets. The operations are compared in terms of the size of the discussions, the consequences recorded, and the opinions shared by forum users. We find that Operation Bayonet generated a higher number of discussions on Reddit, and from the qualitative analysis of such discussions it appears that this operation also had a greater impact on the DNM ecosystem.

A multitude of leaked data can be purchased through the Dark Web nowadays. Recent reports highlight that the largest footprints of leaked data, which range from employee passwords to intellectual property, are linked to governmental institutions. According to OWL Cybersecurity, the US Navy is most affected. Thinking of leaked data like personal files, this can have a severe impact. For example, it can be the cornerstone for the start of sophisticated social engineering attacks, for getting credentials for illegal system access or installing malicious code in the target network. If personally identifiable information or sensitive data, access plans, strategies or intellectual property are traded on the Dark Web, this could pose a threat to the armed forces. The actual impact, role, and dimension of information treated in the Dark Web are rarely analysed. Is the available data authentic and useful? Can it endanger the capabilities of armed forces? These questions are even more challenging, as several well-known cases of deanonymization have been published over recent years, raising the question whether somebody really would use the Dark Web to sell highly sensitive information. In contrast, fake offers from scammers can be found regularly, only set up to cheat possible buyers. A victim of illegal offers on the Dark Web will typically not go to the police. The paper analyses the technical base of the Dark Web and examines possibilities of deanonymization. After an analysis of Dark Web marketplaces and the articles traded there, a discussion of the potential risks to military operations will be used to identify recommendations on how to minimize the risk. The analysis concludes that surveillance of the Dark Web is necessary to increase the chance of identifying sensitive information early; but actually the `open' internet, the surface web and the Deep Web, poses the more important risk factor, as it is - in practice - more difficult to surveil than the Dark Web, and only a small share of breached information is traded on the latter.

With the rapid development of the Internet, the dark network has also been widely used in the Internet [1]. Due to the anonymity of the dark network, many illegal elements have committed illegal crimes on the dark. It is difficult for law enforcement officials to track the identity of these cyber criminals using traditional network survey techniques based on IP addresses [2]. The threat information is mainly from the dark web forum and the dark web market. In this paper, we introduce the current mainstream dark network communication system TOR and develop a visual dark web forum post association analysis system to graphically display the relationship between various forum messages and posters, and help law enforcement officers to explore deep levels. Clues to analyze crimes in the dark network.

Cybercrimes and cyber criminals widely use dark web and illegal functionalities of the dark web towards the world crisis. More than half of the criminal activities and the terror activities conducted through the dark web such as, cryptocurrency, selling human organs, red rooms, child pornography, arm deals, drug deals, hire assassins and hackers, hacking software and malware programs, etc. The law enforcement agencies such as FBI, NSA, Interpol, Mossad, FSB etc, are always conducting surveillance programs through the dark web to trace down the mass criminals and terrorists while stopping the crimes and the terror activities. This paper is about the dark web marketing and surveillance programs. In the deep end research will discuss the dark web access with securely and how the law enforcement agencies exponentially tracking down the users with terror behaviours and activities. Moreover, the paper discusses dark web sites which users can grab the dark web jihadist services and anonymous markets including safety precautions.