Biblio

In the information era, knowledge is becoming increasingly significant for all industries, especially criminal investigation that deeply relies on intelligence and strategies. Therefore, there is an urgent need for effective management and utilization of criminal investigation knowledge. As an important branch of knowledge engineering, the expert system can simulate the thinking pattern of an expert, proposing strategies and solutions based on the knowledge stored in the knowledge base. A crucial step in building the expert system is to construct the knowledge base, which determines the function and capability of the expert system. This paper establishes a practical knowledge base for criminal investigation, combining the technologies of cloud computing with traditional method of manual entry to acquire and process knowledge. The knowledge base covers data information and expert knowledge with detailed classification of rules and cases, providing answers through comparison and reasoning. The knowledge becomes more accurate and reliable after repeated inspection and verification by human experts.

In recent years, new types of cyber attacks called targeted attacks have been observed. It targets specific organizations or individuals, while usual large-scale attacks do not focus on specific targets. Organizations have published many Word or PDF files on their websites. These files may provide the starting point for targeted attacks if they include hidden data unintentionally generated in the authoring process. Adhatarao and Lauradoux analyzed hidden data found in the PDF files published by security agencies in many countries and showed that many PDF files potentially leak information like author names, details on the information system and computer architecture. In this study, we analyze hidden data of PDF files published on the website of police agencies in Japan and compare the results with Adhatarao and Lauradoux's. We gathered 110989 PDF files. 56% of gathered PDF files contain personal names, organization names, usernames, or numbers that seem to be IDs within the organizations. 96% of PDF files contain software names.

Cyber threat intelligence (CTI) is vital for enabling effective cybersecurity decisions by providing timely, relevant, and actionable information about emerging threats. Monitoring the dark web to generate CTI is one of the upcoming trends in cybersecurity. As a result, developing CTI capabilities with the dark web investigation is a significant focus for cybersecurity companies like Deepwatch, DarkOwl, SixGill, ThreatConnect, CyLance, ZeroFox, and many others. In addition, the dark web marketplace (DWM) monitoring tools are of much interest to law enforcement agencies (LEAs). The fact that darknet market participants operate anonymously and online transactions are pseudo-anonymous makes it challenging to identify and investigate them. Therefore, keeping up with the DWMs poses significant challenges for LEAs today. Nevertheless, the offerings on the DWM give insights into the dark web economy to LEAs. The present work is one such attempt to describe and analyze dark web market data collected for CTI using a dark web crawler. After processing and labeling, authors have 53 DWMs with their product listings and pricing.

Early detection of conflict potentials around the community is vital for the Central Java Regional Police Department, especially in the Analyst section of the Directorate of Security Intelligence. Performance in carrying out early detection will affect the peace and security of the community. The performance of potential conflict detection activities can be improved using an integrated early detection information system by shortening the time after observation, report preparation, information processing, and analysis. Developed using Unified Process as a software life cycle, the obtained result shows the time-based performance variables of the officers are significantly improved, including observation time, report production, data finding, and document formatting.

Ransomware groups represent a significant cyber threat to Western states. Most high-end ransomware actors reside in territorial safe-haven jurisdictions and prove to be resistant to traditional law enforcement activities. This has prompted public sector and cybersecurity industry leaders to perceive ransomware as a national security threat requiring a whole-of-government approach, including cyber operations. In this paper, we investigate whether cyber operations or the threat of cyber operations influence the ransomware ecosystem. Subsequently, we assess the vectors of influence and characteristics of past operations that have disrupted the ecosystem. We describe the specifics of the ransomware-as-a-service system and provide three case studies (DarkSide/BlackMatter, REvil, Conti) highly representative of the current ecosystem and the effect cyber operations have on it. Additionally, we present initial observations about the influence of cyber operations on the system, including best practices from cyber operations against non-state groups. We conclude that even professional, highly skilled, and top-performing ransomware groups can be disrupted through cyber operations. In fact, cyber operations can even bypass some limits imposed on law enforcement operations. Even when ransomware groups rebrand or resurface after a hiatus, we suggest their infrastructure (both technical, human, and reputational) will still suffer mid-to long-term disruption. Although cyber operations are unlikely to be a silver bullet, they are an essential tool in the whole-of-government and multinational efforts and may even grow in importance in the next several years.1‘Releasing the hounds’ is a term for offensive cyber operations aimed at disrupting global ransomware gangs, especially those conducted by militaries or intelligence agencies. First use is found in Patrick Gray and Adam Boileau, ‘Feature Podcast: Releasing the Hounds with Bobby Chesney’, Risky Business, 28 May 2020, https://risky.biz/HF6/.

Even as Internet of Things (IoT) network security grows, concerns about the security of IoT devices have arisen. Although a few companies produce IP-connected gadgets for such ranging from small office, their security policies and implementations are often weak. They also require firmware updates or revisions to boost security and reduce vulnerabilities in equipment. A brownfield advance is necessary to verify systems where these helpless devices are present: putting in place basic security mechanisms within the system to render the system powerless possibly. Gadgets should cohabit without threatening their security in the same device. IoT network security has evolved into a platform that can segregate a large number of IoT devices, allowing law enforcement to compel the communication of defenseless devices in order to reduce the damage done by its unlawful transaction. IoT network security appears to be doable in well-known gadget types and can be deployed with minimum transparency.

Crime is a social problem that after the confinement of COVID-19 has increased significantly worldwide, which is why it is important to know what technological tools can be used to prevent criminal acts. In the present work, a systemic analysis was carried out to determine the importance of how to prevent crime using new information technologies. Fifty research articles were selected between 2015 and 2021. The information was obtained from different databases such as IEEE Xplore, Redalyc, Scopus, SciELO and Medline. Keywords were used to delimit the search and be more precise in our inquiry on the web. The results obtained show specific information on how to prevent crime using new information technologies. We conclude that new information technologies help to prevent crime since several developed countries have implemented their security system effectively, while underdeveloped countries do not have adequate technologies to prevent crime.

Among the operational shortfalls that hinder law enforcement from achieving greater success in preventing terrorist attacks is the difficulty in dynamically assessing individualized violent extremism risk at scale given the enormous amount of primarily text-based records in disparate databases. In this work, we undertake the critical task of employing natural language processing (NLP) techniques and supervised machine learning models to classify textual data in analyst and investigator notes and reports for radicalization behavioral indicators. This effort to generate structured knowledge will build towards an operational capability to assist analysts in rapidly mining law enforcement and intelligence databases for cues and risk indicators. In the near-term, this effort also enables more rapid coding of biographical radicalization profiles to augment a research database of violent extremists and their exhibited behavioral indicators.

With the rapid development of the wireless network and smart mobile equipment, many lawbreakers employ mobile devices to destroy and steal important information and property from other persons. In order to fighting the criminal act efficiently, the public security organ need to collect the evidences from the crime tools and submit to the court. In the meantime, with development of internal storage technology, the law enforcement officials collect lots of information from the smart mobile equipment, for the sake of handling the huge amounts of data, we propose a framework that combine distributed clustering methods to analyze data sets, this model will split massive data into smaller pieces and use clustering method to analyze each smaller one on disparate machines to solve the problem of large amount of data, thus forensics investigation work will be more effectively.

In the computer era, various digital devices are used along with networking technology for data communication in secured manner. But sometimes these systems are misused by the attackers. Information security with the high efficiency devices, tools are utilized for protecting the communication media and valuable data. In case of any unwanted incidents and security breaches, digital forensics methods and measures are well utilized for detecting the type of attacks, sources of attacks, their purposes. By utilizing information related to security measures, digital forensics evidences with suitable methodologies, digital forensics investigators detect the cyber-crimes. It is also necessary to prove the cyber-crimes before the law enforcement department. During this process investigators type to collect different types of information from the digital devices concerned to the cyber-attack. One of the major tasks of the digital investigator is collecting and managing the seizure records from the crime-scene. The present paper discusses the seizure record framework for digital forensics investigations.

The rapid rise of the Dark Web and supportive technologies has served as the backbone facilitating online illegal activity worldwide. These illegal activities supported by anonymisation technologies such as Tor has made it increasingly elusive to law enforcement agencies. Despite several successful law enforcement operations, illegal activity on the Dark Web is still growing. There are approaches to monitor, mine, and research the Dark Web, all with varying degrees of success. Given the complexity and dynamics of the services offered, we recognize the need for in depth analysis of the Dark Web with regard to its infrastructures, actors, types of abuse and their relationships. This involves the challenging task of information extraction from the very heterogeneous collection of web pages that make up the Dark Web. Most providers develop their services on top of standard frameworks such as WordPress, Simple Machine Forum, phpBB and several other frameworks to deploy their services. As a result, these service providers publish significant number of pages based on similar structural and stylistic templates. We propose an efficient, scalable, repeatable and accurate approach to cluster Dark Web pages based on those structural and stylistic features. Extracting relevant information from those clusters should make it feasible to conduct in depth Dark Web analysis. This paper presents our clustering algorithm to accelerate information extraction, and as a result improve attribution of digital traces to infrastructures or individuals in the fight against cyber crime.

In today's digital era, data is most important in every phase of work. The storage and processing on data with security is the need of each and every application field. Data need to be tamper resistant due to possibility of alteration. Data can be represented and stored in heterogeneous format. There are chances of attack on information which is vital for particular organization. With rapid increase in cyber crime, attackers behave maliciously to alter those data. But it is having great impact on forensic evidences which is required for provenance. Therefore, it is required to maintain the reliability and provenance of digital evidences as it travels through various stages during forensic investigation. In this approach, there is a forensic chain in which generated report passes through various levels or intermediaries such as pathology laboratory, doctor, police department etc. To build the transparent system with immutability of forensic evidences, blockchain technology is more suitable. Blockchain technology provides the transfer of assets or evidence reports in transparent environment without central authority. In this paper blockchain based secure system for forensic evidences is proposed. The proposed system is implemented on Ethereum platform. The tampering of forensic evidence can be easily traced at any stage by anyone in the forensic chain. The security enhancement of forensic evidences is achieved through implementation on Ethereum platform with high integrity, traceability and immutability.

We perform the first post EU General Data Protection Regulation (GDPR) usability study of privacy policies for mobile apps. For our analysis, we collect a dataset of historical (prior to GDPR implementation in May 2018) and contemporary privacy policies in different categories. In contrast to the common belief, that after the GDPR most of the privacy policies are easier to understand, our analysis shows that this is not so.

MANET stands for Mobile ad-hoc network, which is also known as a wireless network. It provides a routable networking environment which does not have a centralized infrastructure. MANET is used in many important sectors like economic sector (corporate field), security sector (military field), education sector (video conferences and lectures), law sector (law enforcement) and many more. Even though it plays a vital role in different sectors and improves its economic growth, security is a major concern in MANET. Due to lack of inbuilt security, several attacks like data traffic attack, control traffic attack. The wormhole is a kind of control traffic attack which forms wormhole link between nodes. In this paper, we have proposed an approach to detect and get rid of the wormhole attack. The proposed approach is based on trust values, which will decide whether nodes are affected by using parameters like receiving time and data rate. On evaluation, we have concluded that the wormhole attack decreases the network's performance while using trusted approach its value increases. Means PDR and throughput return best results for the affected network while in case of end to end delay it returns similar results as of unaffected network.

Proxy web caching is usually employed to maximize the efficiency and utilization of the network and the origin servers while reducing the request latency. However, and due to the limited cache size, some replacement policy has to be enforced in order to decide on the object(s) to be evicted from the cache once it is full. This paper introduces the use of the K-mean clustering to categorize the objects in the cache into groups of different priorities. This categorization is then used for replacement purposes such that the object(s) of lowest priority are chosen for eviction. The proposed improved the hit rate and the byte hit rate of the cache when compared to conventional and intelligent web proxy caching algorithms.

This paper analyzes the necessity of artificial intelligence and big data in the security application of regulatory places. The author studies the specific application of artificial intelligence and big data in ideological dynamics management, access control system, video surveillance system, emergency alarm system, perimeter control system, police inspection system, daily behavior management, and system implementation management. The author puts forward how to do technical integration, improve information sharing, strengthen the construction of talents, and increase management fund expenditure. The purpose of this paper is to enhance the security management level of regulatory places and optimize the management environment of regulatory places.

The widespread use of powerful mobile devices has deeply affected the mix of traffic traversing both the Internet and enterprise networks (with bring-your-own-device policies). Traffic encryption has become extremely common, and the quick proliferation of mobile apps and their simple distribution and update have created a specifically challenging scenario for traffic classification and its uses, especially network-security related ones. The recent rise of Deep Learning (DL) has responded to this challenge, by providing a solution to the time-consuming and human-limited handcrafted feature design, and better clas-sification performance. The counterpart of the advantages is the lack of interpretability of these black-box approaches, limiting or preventing their adoption in contexts where the reliability of results, or interpretability of polices is necessary. To cope with these limitations, eXplainable Artificial Intelligence (XAI) techniques have seen recent intensive research. Along these lines, our work applies XAI-based techniques (namely, Deep SHAP) to interpret the behavior of a state-of-the-art multimodal DL traffic classifier. As opposed to common results seen in XAI, we aim at a global interpretation, rather than sample-based ones. The results quantify the importance of each modality (payload- or header-based), and of specific subsets of inputs (e.g., TLS SNI and TCP Window Size) in determining the classification outcome, down to per-class (viz. application) level. The analysis is based on a publicly-released recent dataset focused on mobile app traffic.

Ciphertext-policy attribute-based encryption (CP-ABE) is applied to many data service platforms to provides secure and fine-grained access control. In this paper, a new CP-ABE system based on the algebraic decision diagram (ADD) is presented. The new system makes full use of both the powerful description ability and the high calculating efficiency of ADD to improves the performance and efficiency of algorithms contained in CP-ABE. First, the new system supports both positive and negative attributes in the description of access polices. Second, the size of the secret key is constant and is not affected by the number of attributes. Third, time complexity of the key generation and decryption algorithms are O(1). Finally, this scheme allows visitors to have different access permissions to access shared data or file. At the same time, PV operation is introduced into CP-ABE framework for the first time to prevent resource conflicts caused by read and write operations on shared files. Compared with other schemes, the new scheme proposed in this paper performs better in function and efficiency.

When disasters occur, they not only affect the human life. Therefore, communication in disaster management is very important. During the disaster recovery phase, the network infrastructure may be partially fragmented and mobile rescue operations may involve many teams with different roles which can dynamically change. Therefore, disaster management services require high flexibility both in terms of network infrastructure management and rescue group communication. Existing studies have shown that IP-based or traditional telephony solutions are not well-suited to deal with such flexible group communication and network management due to their connection-oriented communication, no built-in support for mobile devices, and no mechanism for network fragmentation. Recent studies show that information-centric networking offers scalable and flexible communication based on its name-based interest-oriented communication approach. However, considering the difficulty of deploying a new service on the existing network, the programmability and virtualization of the network are required. This paper presents our implementation of an information-centric disaster management system based on network function virtualization (vICSNF). We show a proof-of-concept system with a case study for Seoul disaster management services. The system achieves flexibility both in terms of network infrastructure management and rescue group communication. Obtained testbed results show that vICSNF achieves a low communication overhead compared to the IP-based approach and the auto-configuration of vICSNFs enables the quick deployment for disaster management services in disaster scenarios.

Unmanned Aerial Vehicles (UAVs) also known as drones are being used in many applications where they can record or stream videos. One interesting application is the Intelligent Transportation Systems (ITS) and public safety applications where drones record videos and send them to a control center for further analysis. These videos are shared by various clients such as law enforcement or emergency personnel. In such cases, the recording might include faces of civilians or other sensitive information that might pose privacy concerns. While the video can be encrypted and stored in the cloud that way, it can still be accessed once the keys are exposed to third parties which is completely insecure. To prevent such insecurity, in this paper, we propose proxy re-encryption based sharing scheme to enable third parties to access only limited videos without having the original encryption key. The costly pairing operations in proxy re-encryption are not used to allow rapid access and delivery of the surveillance videos to third parties. The key management is handled by a trusted control center, which acts as the proxy to re-encrypt the data. We implemented and tested the approach in a realistic simulation environment using different resolutions under ns-3. The implementation results and comparisons indicate that there is an acceptable overhead while it can still preserve the privacy of drivers and passengers.

Video streams acquired from thermal cameras are proven to be beneficial in diverse number of fields including military, healthcare, law enforcement, and security. Despite the hype, thermal imaging is increasingly affected by poor resolution, where it has expensive optical sensors and inability to attain optical precision. In recent years, deep learning based super-resolution algorithms are developed to enhance the video frame resolution at high accuracy. This paper presents a comparative analysis of super resolution (SR) techniques based on deep neural networks (DNN) that are applied on thermal video dataset. SRCNN, EDSR, Auto-encoder, and SRGAN are also discussed and investigated. Further the results on benchmark thermal datasets including FLIR, OSU thermal pedestrian database and OSU color thermal database are evaluated and analyzed. Based on the experimental results, it is concluded that, SRGAN has delivered a superior performance on thermal frames when compared to other techniques and improvements, which has the ability to provide state-of-the art performance in real time operations.

Bitcoin is a decentralized digital currency whose transactions are recorded in a common ledger, so called blockchain. Due to the anonymity and lack of law enforcement, Bitcoin has been misused in darknet markets which deal with illegal products, such as drugs and weapons. Therefore from the security forensics aspect, it is demanded to establish an approach to identify newly emerged darknet markets' transactions and addresses. In this paper, we thoroughly analyze Bitcoin transactions and addresses related to darknet markets and propose a novel identification method of darknet markets' addresses. To improve the identification performance, we propose a voting based method which decides the labels of multiple addresses controlled by the same user based on the number of the majority label. Through the computer simulation with more than 200K Bitcoin addresses, it was shown that our voting based method outperforms the nonvoting based one in terms of precision, recal, and F1 score. We also found that DNM's addresses pay higher fees than others, which significantly improves the classification.

iOS is one of the most secure operating systems based on policies created and enforced by Apple. Though not impervious or free from vulnerabilities, iOS has remained resilient to many attacks partially based on lower market share of devices, but primarily because of tight controls placed on iOS development and application deployment. Locked iOS devices pose a specific hard problem for both law enforcement and corporate IT dealing with malicious insiders or intrusion scenarios. The need to recover forensic data from locked iOS devices has been of public interest for some time. In this paper, we describe a case study analysis of the iPhone 5c model and our attempts to use electromagnetic (EM) fault-injection as a side channel means to unlock the device. Based on our study, we report on our unsuccessful attempts in unlocking a locked iPhone 5c using this side channel-based approach. As a contribution, we provide initial analysis of the iPhone 5c processor's spectral mapping under different states, a brief survey of published techniques related to iPhone unlock scenarios, and a set of lessons learned and recommended best practices for other researchers who are interested in future EM-based iOS studies.

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.